├── .gitignore ├── LICENSE ├── MANIFEST.in ├── README.rst ├── doc ├── .gitignore ├── examples │ ├── quickstart.hires.png │ ├── quickstart.pdf │ ├── quickstart.png │ └── quickstart.py └── source │ ├── conf.py │ ├── control.rst │ ├── disturbances.rst │ ├── emulator.rst │ ├── index.rst │ ├── installation.rst │ ├── mpc.rst │ ├── prediction.rst │ ├── quickstart.rst │ ├── reference.rst │ └── stateestimation.rst ├── examples ├── .gitignore ├── __init__.py ├── quickstart.py └── simple_space_heating_mpc.py ├── mpcpy ├── __init__.py ├── __version__.py ├── control.py ├── disturbances.py ├── emulator.py ├── mpc.py ├── prediction.py └── stateestimation.py ├── setup.py └── tests ├── .gitignore ├── __init__.py ├── all.py ├── boundaryconditions.py ├── data └── example.mo ├── emulator.py ├── examples.py ├── prediction.py └── stateestimation.py /.gitignore: -------------------------------------------------------------------------------- 1 | *.pyc 2 | mpcpy.egg-info 3 | dist 4 | build 5 | env 6 | .idea 7 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | include README.rst 2 | include LICENSE 3 | include examples/simple_space_heating_mpc.ipynb 4 | include examples/cplex_api.ipynb -------------------------------------------------------------------------------- /README.rst: -------------------------------------------------------------------------------- 1 | mpcpy 2 | ----- 3 | 4 | A group of classes to run Model Predictive Control (MPC) simulations using python. 5 | 6 | Installation 7 | ============ 8 | 9 | Prequisites 10 | ^^^^^^^^^^^ 11 | 12 | * `numpy` 13 | 14 | 15 | Setup 16 | ^^^^^ 17 | 18 | Install via pip:: 19 | 20 | pip install mpcpy 21 | 22 | Or: 23 | 24 | * download a `release `_ 25 | * unzip and cd to the folder 26 | * run ``python setup.py install`` 27 | 28 | 29 | Examples 30 | ======== 31 | 32 | In the examples folder some documented examples of how to work with mpcpy are available as IPython Notebooks. 33 | 34 | This example should get you started with mpcpy 35 | 36 | * `Simple space heating mpc `_ 37 | -------------------------------------------------------------------------------- /doc/.gitignore: -------------------------------------------------------------------------------- 1 | build -------------------------------------------------------------------------------- /doc/examples/quickstart.hires.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BrechtBa/mpcpy/108b7a19bc07f17b1fae30ee8ecfdd1e732c1c47/doc/examples/quickstart.hires.png -------------------------------------------------------------------------------- /doc/examples/quickstart.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BrechtBa/mpcpy/108b7a19bc07f17b1fae30ee8ecfdd1e732c1c47/doc/examples/quickstart.pdf -------------------------------------------------------------------------------- /doc/examples/quickstart.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BrechtBa/mpcpy/108b7a19bc07f17b1fae30ee8ecfdd1e732c1c47/doc/examples/quickstart.png -------------------------------------------------------------------------------- /doc/examples/quickstart.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | ################################################################################ 3 | # Copyright 2015 Brecht Baeten 4 | # This file is part of mpcpy. 5 | # 6 | # mpcpy is free software: you can redistribute it and/or modify 7 | # it under the terms of the GNU General Public License as published by 8 | # the Free Software Foundation, either version 3 of the License, or 9 | # (at your option) any later version. 10 | # 11 | # mpcpy is distributed in the hope that it will be useful, 12 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 13 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14 | # GNU General Public License for more details. 15 | # 16 | # You should have received a copy of the GNU General Public License 17 | # along with mpcpy. If not, see . 18 | ################################################################################ 19 | 20 | import numpy as np 21 | import matplotlib.pyplot as plt 22 | 23 | import mpcpy 24 | import pyomo.environ as pyomo 25 | 26 | 27 | # Define an emulator class 28 | class Emulator(mpcpy.Emulator): 29 | """ 30 | A custom system emulator 31 | """ 32 | def simulate(self, starttime, stoptime, input): 33 | dt = 1 34 | time = np.arange(starttime, stoptime+dt, dt, dtype=np.float) 35 | 36 | # initialize 37 | x = np.ones_like(time)*self.res['x'][-1] 38 | 39 | # interpolate inputs 40 | u = np.interp(time, input['time'], input['u']) 41 | d = np.interp(time, input['time'], input['d']) 42 | 43 | # perform simulation 44 | for i, t in enumerate(time[:-1]): 45 | # dx/dt = A*x + d + u 46 | x[i+1] = x[i] + (self.parameters['A']*x[i] + d[i] + u[i])*dt 47 | 48 | # create and return a results dict 49 | res = { 50 | 'time': time, 51 | 'x': x, 52 | 'd': d, 53 | 'u': u, 54 | } 55 | 56 | return res 57 | 58 | 59 | # Define a control class 60 | class SetpointControl(mpcpy.Control): 61 | """ 62 | A control to keep the state as close to a set point as possible 63 | """ 64 | def formulation(self): 65 | # create a pyomo model 66 | model = pyomo.AbstractModel() 67 | 68 | model.i = pyomo.Set() 69 | model.ip = pyomo.Set() 70 | 71 | model.time = pyomo.Param(model.ip) 72 | model.d = pyomo.Param(model.ip, initialize=0.) 73 | model.x = pyomo.Var(model.ip, domain=pyomo.Reals, initialize=0.) 74 | model.u = pyomo.Var(model.ip, domain=pyomo.NonNegativeReals, bounds=(0., 1.), initialize=0.) 75 | 76 | model.x0 = pyomo.Param(initialize=0.) 77 | 78 | model.initialcondition = pyomo.Constraint( 79 | rule=lambda model: model.x[0] == model.x0 80 | ) 81 | 82 | model.constraint = pyomo.Constraint( 83 | model.i, 84 | rule=lambda model, i: (model.x[i+1]-model.x[i])/(model.time[i+1]-model.time[i]) == 85 | self.parameters['A']*model.x[i] + model.d[i] + model.u[i] 86 | ) 87 | 88 | model.objective = pyomo.Objective( 89 | rule=lambda model: sum((model.x[i]-self.parameters['set'])**2 for i in model.i) 90 | ) 91 | 92 | # store the model inside the object 93 | self.model = model 94 | 95 | def solution(self, sta, pre): 96 | # create data and instantiate the pyomo model 97 | ip = np.arange(len(pre['time'])) 98 | data = { 99 | None: { 100 | 'i': {None: ip[:-1]}, 101 | 'ip': {None: ip}, 102 | 'time': {(i,): v for i, v in enumerate(pre['time'])}, 103 | 'x0': {None: sta['x']}, 104 | 'd': {(i,): pre['d'][i] for i in ip}, 105 | } 106 | } 107 | 108 | instance = self.model.create_instance(data) 109 | 110 | # solve and return the control inputs 111 | optimizer = pyomo.SolverFactory('ipopt') 112 | results = optimizer.solve(instance) 113 | 114 | sol = { 115 | 'time': np.array([pyomo.value(instance.time[i]) for i in instance.ip]), 116 | 'x': np.array([pyomo.value(instance.x[i]) for i in instance.ip]), 117 | 'u': np.array([pyomo.value(instance.u[i]) for i in instance.ip]), 118 | 'd': np.array([pyomo.value(instance.d[i]) for i in instance.ip]), 119 | } 120 | 121 | return sol 122 | 123 | 124 | # Define a state estimation class 125 | class StateestimationPerfect(mpcpy.Stateestimation): 126 | """ 127 | Perfect state estimation 128 | """ 129 | def stateestimation(self, time): 130 | return {'x': np.interp(time, self.emulator.res['time'], self.emulator.res['x'])} 131 | 132 | 133 | # instantiate the emulator 134 | emulator = Emulator(['u', 'd'], parameters={'A': -0.2}, initial_conditions={'x': 0}) 135 | 136 | # test the emulator with some random data 137 | time = np.arange(0., 1001., 10.) 138 | np.random.seed(0) 139 | d = np.random.random(len(time)) - 0.5 140 | u = 1.0*np.ones(len(time)) 141 | 142 | emulator.initialize() 143 | res = emulator(time, {'time': time, 'd': d, 'u': u}) 144 | print(res) 145 | 146 | 147 | # create a disturbances object 148 | time = np.arange(0., 1001., 10.) 149 | d = 0.5*np.sin(2*np.pi*time/1000) 150 | disturbances = mpcpy.Disturbances({'time': time, 'd': d}) 151 | 152 | bcs = disturbances(np.array([0, 20, 40, 60, 100])) 153 | print(bcs) 154 | 155 | 156 | # create a stateestimation object 157 | stateestimation = StateestimationPerfect(emulator) 158 | sta = stateestimation(0) 159 | print(sta) 160 | 161 | 162 | # create a prediction object 163 | prediction = mpcpy.Prediction(disturbances) 164 | pre = prediction(np.array([0, 20, 40, 60, 100])) 165 | print(pre) 166 | 167 | 168 | # create a control object and mpc object 169 | control = SetpointControl(stateestimation, prediction, parameters={'A': -0.2, 'set': 3.0}, 170 | horizon=100., timestep=10., receding=10.) 171 | mpc = mpcpy.MPC(emulator, control, disturbances, emulationtime=1000, resulttimestep=10) 172 | 173 | # run the mpc 174 | res = mpc(verbose=1) 175 | print(res) 176 | 177 | 178 | # plot results 179 | fig, ax = plt.subplots(2, 1) 180 | ax[0].plot(res['time'], res['u']) 181 | ax[0].set_ylabel('u') 182 | 183 | ax[1].plot(res['time'], res['x']) 184 | ax[1].set_xlabel('time') 185 | ax[1].set_ylabel('x') 186 | 187 | 188 | if __name__ == '__main__': 189 | plt.show() -------------------------------------------------------------------------------- /doc/source/conf.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # 3 | # Someproject documentation build configuration file, created by 4 | # sphinx-quickstart on Mon Sep 05 08:56:23 2016. 5 | # 6 | # This file is execfile()d with the current directory set to its 7 | # containing dir. 8 | # 9 | # Note that not all possible configuration values are present in this 10 | # autogenerated file. 11 | # 12 | # All configuration values have a default; values that are commented out 13 | # serve to show the default. 14 | 15 | # If extensions (or modules to document with autodoc) are in another directory, 16 | # add these directories to sys.path here. If the directory is relative to the 17 | # documentation root, use os.path.abspath to make it absolute, like shown here. 18 | # 19 | # import os 20 | # import sys 21 | # sys.path.insert(0, os.path.abspath('.')) 22 | 23 | # -- General configuration ------------------------------------------------ 24 | 25 | # If your documentation needs a minimal Sphinx version, state it here. 26 | # 27 | # needs_sphinx = '1.0' 28 | 29 | # Add any Sphinx extension module names here, as strings. They can be 30 | # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom 31 | # ones. 32 | 33 | import sys 34 | import os 35 | import sphinx_rtd_theme 36 | 37 | sys.path.insert(0, os.path.abspath('../..')) 38 | 39 | # import the version string 40 | from mpcpy.__version__ import version as __version__ 41 | 42 | extensions = [ 43 | 'sphinx.ext.autodoc', 44 | 'sphinx.ext.autosummary', 45 | 'sphinx.ext.mathjax', 46 | 'sphinx.ext.viewcode', 47 | 'numpydoc', 48 | 'matplotlib.sphinxext.plot_directive', 49 | ] 50 | 51 | # Add any paths that contain templates here, relative to this directory. 52 | templates_path = ['_templates'] 53 | 54 | # The suffix(es) of source filenames. 55 | # You can specify multiple suffix as a list of string: 56 | # 57 | # source_suffix = ['.rst', '.md'] 58 | source_suffix = '.rst' 59 | 60 | # The encoding of source files. 61 | # 62 | # source_encoding = 'utf-8-sig' 63 | 64 | # The master toctree document. 65 | master_doc = 'index' 66 | 67 | # General information about the project. 68 | project = u'mpcpy' 69 | copyright = u'2017, Brecht Baeten' 70 | author = u'Brecht Baeten' 71 | 72 | # The version info for the project you're documenting, acts as replacement for 73 | # |version| and |release|, also used in various other places throughout the 74 | # built documents. 75 | # 76 | # The short X.Y version. 77 | version = str(__version__) 78 | # The full version, including alpha/beta/rc tags. 79 | release = str(__version__) 80 | 81 | # The language for content autogenerated by Sphinx. Refer to documentation 82 | # for a list of supported languages. 83 | # 84 | # This is also used if you do content translation via gettext catalogs. 85 | # Usually you set "language" from the command line for these cases. 86 | language = None 87 | 88 | # There are two options for replacing |today|: either, you set today to some 89 | # non-false value, then it is used: 90 | # 91 | # today = '' 92 | # 93 | # Else, today_fmt is used as the format for a strftime call. 94 | # 95 | # today_fmt = '%B %d, %Y' 96 | 97 | # List of patterns, relative to source directory, that match files and 98 | # directories to ignore when looking for source files. 99 | # This patterns also effect to html_static_path and html_extra_path 100 | exclude_patterns = [] 101 | 102 | # The reST default role (used for this markup: `text`) to use for all 103 | # documents. 104 | # 105 | # default_role = None 106 | 107 | # If true, '()' will be appended to :func: etc. cross-reference text. 108 | # 109 | # add_function_parentheses = True 110 | 111 | # If true, the current module name will be prepended to all description 112 | # unit titles (such as .. function::). 113 | # 114 | # add_module_names = True 115 | 116 | # If true, sectionauthor and moduleauthor directives will be shown in the 117 | # output. They are ignored by default. 118 | # 119 | # show_authors = False 120 | 121 | # The name of the Pygments (syntax highlighting) style to use. 122 | pygments_style = 'sphinx' 123 | 124 | # A list of ignored prefixes for module index sorting. 125 | # modindex_common_prefix = [] 126 | 127 | # If true, keep warnings as "system message" paragraphs in the built documents. 128 | # keep_warnings = False 129 | 130 | # If true, `todo` and `todoList` produce output, else they produce nothing. 131 | todo_include_todos = False 132 | 133 | 134 | # -- Options for HTML output ---------------------------------------------- 135 | 136 | # The theme to use for HTML and HTML Help pages. See the documentation for 137 | # a list of builtin themes. 138 | # 139 | html_theme = 'sphinx_rtd_theme' 140 | 141 | # Theme options are theme-specific and customize the look and feel of a theme 142 | # further. For a list of options available for each theme, see the 143 | # documentation. 144 | # 145 | # html_theme_options = {} 146 | 147 | # Add any paths that contain custom themes here, relative to this directory. 148 | html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] 149 | 150 | # The name for this set of Sphinx documents. 151 | # " v documentation" by default. 152 | # 153 | # html_title = u'Someproject v3.1.2' 154 | 155 | # A shorter title for the navigation bar. Default is the same as html_title. 156 | # 157 | # html_short_title = None 158 | 159 | # The name of an image file (relative to this directory) to place at the top 160 | # of the sidebar. 161 | # 162 | # html_logo = None 163 | 164 | # The name of an image file (relative to this directory) to use as a favicon of 165 | # the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 166 | # pixels large. 167 | # 168 | # html_favicon = None 169 | 170 | # Add any paths that contain custom static files (such as style sheets) here, 171 | # relative to this directory. They are copied after the builtin static files, 172 | # so a file named "default.css" will overwrite the builtin "default.css". 173 | html_static_path = ['_static'] 174 | 175 | # Add any extra paths that contain custom files (such as robots.txt or 176 | # .htaccess) here, relative to this directory. These files are copied 177 | # directly to the root of the documentation. 178 | # 179 | # html_extra_path = [] 180 | 181 | # If not None, a 'Last updated on:' timestamp is inserted at every page 182 | # bottom, using the given strftime format. 183 | # The empty string is equivalent to '%b %d, %Y'. 184 | # 185 | # html_last_updated_fmt = None 186 | 187 | # If true, SmartyPants will be used to convert quotes and dashes to 188 | # typographically correct entities. 189 | # 190 | # html_use_smartypants = True 191 | 192 | # Custom sidebar templates, maps document names to template names. 193 | # 194 | html_sidebars = { 195 | '**': [ 196 | 'about.html', 197 | 'navigation.html', 198 | 'relations.html', 199 | 'searchbox.html', 200 | 'donate.html' 201 | ] 202 | } 203 | 204 | # Additional templates that should be rendered to pages, maps page names to 205 | # template names. 206 | # 207 | # html_additional_pages = {} 208 | 209 | # If false, no module index is generated. 210 | # 211 | # html_domain_indices = True 212 | 213 | # If false, no index is generated. 214 | # 215 | # html_use_index = True 216 | 217 | # If true, the index is split into individual pages for each letter. 218 | # 219 | # html_split_index = False 220 | 221 | # If true, links to the reST sources are added to the pages. 222 | # 223 | # html_show_sourcelink = True 224 | 225 | # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. 226 | # 227 | # html_show_sphinx = True 228 | 229 | # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. 230 | # 231 | # html_show_copyright = True 232 | 233 | # If true, an OpenSearch description file will be output, and all pages will 234 | # contain a tag referring to it. The value of this option must be the 235 | # base URL from which the finished HTML is served. 236 | # 237 | # html_use_opensearch = '' 238 | 239 | # This is the file name suffix for HTML files (e.g. ".xhtml"). 240 | # html_file_suffix = None 241 | 242 | # Language to be used for generating the HTML full-text search index. 243 | # Sphinx supports the following languages: 244 | # 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' 245 | # 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr', 'zh' 246 | # 247 | # html_search_language = 'en' 248 | 249 | # A dictionary with options for the search language support, empty by default. 250 | # 'ja' uses this config value. 251 | # 'zh' user can custom change `jieba` dictionary path. 252 | # 253 | # html_search_options = {'type': 'default'} 254 | 255 | # The name of a javascript file (relative to the configuration directory) that 256 | # implements a search results scorer. If empty, the default will be used. 257 | # 258 | # html_search_scorer = 'scorer.js' 259 | 260 | # Output file base name for HTML help builder. 261 | htmlhelp_basename = 'Someprojectdoc' 262 | 263 | # -- Options for LaTeX output --------------------------------------------- 264 | 265 | latex_elements = { 266 | # The paper size ('letterpaper' or 'a4paper'). 267 | # 268 | # 'papersize': 'letterpaper', 269 | 270 | # The font size ('10pt', '11pt' or '12pt'). 271 | # 272 | # 'pointsize': '10pt', 273 | 274 | # Additional stuff for the LaTeX preamble. 275 | # 276 | # 'preamble': '', 277 | 278 | # Latex figure (float) alignment 279 | # 280 | # 'figure_align': 'htbp', 281 | } 282 | 283 | # Grouping the document tree into LaTeX files. List of tuples 284 | # (source start file, target name, title, 285 | # author, documentclass [howto, manual, or own class]). 286 | latex_documents = [ 287 | (master_doc, 'mpcpy.tex', u'mpcpy Documentation', 288 | u'me', 'manual'), 289 | ] 290 | 291 | # The name of an image file (relative to this directory) to place at the top of 292 | # the title page. 293 | # 294 | # latex_logo = None 295 | 296 | # For "manual" documents, if this is true, then toplevel headings are parts, 297 | # not chapters. 298 | # 299 | # latex_use_parts = False 300 | 301 | # If true, show page references after internal links. 302 | # 303 | # latex_show_pagerefs = False 304 | 305 | # If true, show URL addresses after external links. 306 | # 307 | # latex_show_urls = False 308 | 309 | # Documents to append as an appendix to all manuals. 310 | # 311 | # latex_appendices = [] 312 | 313 | # It false, will not define \strong, \code, itleref, \crossref ... but only 314 | # \sphinxstrong, ..., \sphinxtitleref, ... To help avoid clash with user added 315 | # packages. 316 | # 317 | # latex_keep_old_macro_names = True 318 | 319 | # If false, no module index is generated. 320 | # 321 | # latex_domain_indices = True 322 | 323 | 324 | # -- Options for manual page output --------------------------------------- 325 | 326 | # One entry per manual page. List of tuples 327 | # (source start file, name, description, authors, manual section). 328 | man_pages = [ 329 | (master_doc, 'mpcpy', u'mpcpy Documentation', 330 | [author], 1) 331 | ] 332 | 333 | # If true, show URL addresses after external links. 334 | # 335 | # man_show_urls = False 336 | 337 | 338 | # -- Options for Texinfo output ------------------------------------------- 339 | 340 | # Grouping the document tree into Texinfo files. List of tuples 341 | # (source start file, target name, title, author, 342 | # dir menu entry, description, category) 343 | texinfo_documents = [ 344 | (master_doc, 'mpcpy', u'mpcpy Documentation', 345 | author, 'mpcpy', '', 346 | 'Miscellaneous'), 347 | ] 348 | 349 | # Documents to append as an appendix to all manuals. 350 | # 351 | # texinfo_appendices = [] 352 | 353 | # If false, no module index is generated. 354 | # 355 | # texinfo_domain_indices = True 356 | 357 | # How to display URL addresses: 'footnote', 'no', or 'inline'. 358 | # 359 | # texinfo_show_urls = 'footnote' 360 | 361 | # If true, do not generate a @detailmenu in the "Top" node's menu. 362 | # 363 | # texinfo_no_detailmenu = False 364 | 365 | 366 | 367 | # -- Options for numpydoc ---------------------------------------------------- 368 | 369 | numpydoc_show_class_members = False 370 | numpydoc_show_inherited_class_members = False 371 | 372 | 373 | # -- Options for autodoc ---------------------------------------------------- 374 | 375 | autoclass_content = 'both' -------------------------------------------------------------------------------- /doc/source/control.rst: -------------------------------------------------------------------------------- 1 | Control 2 | ======= 3 | 4 | .. autoclass:: mpcpy.Control 5 | :members: 6 | :special-members: __call__ -------------------------------------------------------------------------------- /doc/source/disturbances.rst: -------------------------------------------------------------------------------- 1 | Disturbances 2 | ============ 3 | 4 | .. autoclass:: mpcpy.Disturbances 5 | :members: 6 | :special-members: __call__ -------------------------------------------------------------------------------- /doc/source/emulator.rst: -------------------------------------------------------------------------------- 1 | Emulator 2 | ======== 3 | 4 | .. autoclass:: mpcpy.Emulator 5 | :members: 6 | :special-members: __call__ 7 | -------------------------------------------------------------------------------- /doc/source/index.rst: -------------------------------------------------------------------------------- 1 | .. mpcpy documentation master file, created by python-git-package. 2 | You can adapt this file completely to your liking, but it should at least 3 | contain the root `toctree` directive. 4 | 5 | mpcpy 6 | ===== 7 | 8 | :Release: |version| 9 | :Date: |today| 10 | 11 | 12 | A group of classes to run Model Predictive Control (MPC) simulations using python. 13 | 14 | 15 | Contents: 16 | 17 | .. toctree:: 18 | :maxdepth: 2 19 | 20 | installation 21 | quickstart 22 | reference 23 | 24 | 25 | -------------------------------------------------------------------------------- /doc/source/installation.rst: -------------------------------------------------------------------------------- 1 | Installation 2 | ============ 3 | .. toctree:: 4 | :maxdepth: 3 5 | 6 | 7 | Prequisites 8 | ----------- 9 | 10 | * :code:`numpy` 11 | 12 | 13 | Setup 14 | ----- 15 | 16 | Install via pip: 17 | 18 | .. code-block:: bash 19 | 20 | pip install mpcpy 21 | 22 | Or: 23 | 24 | * download a `release `_ 25 | * unzip and cd to the folder 26 | * run :code:`python setup.py install` 27 | 28 | -------------------------------------------------------------------------------- /doc/source/mpc.rst: -------------------------------------------------------------------------------- 1 | MPC 2 | === 3 | 4 | .. autoclass:: mpcpy.MPC 5 | :members: 6 | :special-members: __call__ -------------------------------------------------------------------------------- /doc/source/prediction.rst: -------------------------------------------------------------------------------- 1 | Prediction 2 | ========== 3 | 4 | .. autoclass:: mpcpy.Prediction 5 | :members: 6 | :special-members: __call__ -------------------------------------------------------------------------------- /doc/source/quickstart.rst: -------------------------------------------------------------------------------- 1 | Quickstart 2 | ========== 3 | .. toctree:: 4 | :maxdepth: 3 5 | 6 | 7 | MPC programming structure 8 | ------------------------- 9 | 10 | In Model Predictive Control (MPC) a dynamic system is controlled with some 11 | control inputs. These inputs are computed with the aid of a model of the dynamic 12 | system which predicts the system behavior over a certain time horizon. 13 | 14 | An MPC simulation is here defined as a dynamic simulation of such a system. In 15 | such a simulation the actual system is replaced by a system emulator, a dynamic 16 | simulation which should model the system behavior a close as possible. 17 | 18 | The control algorithm contains a more simplified model of the dynamic system and 19 | often uses an optimization technique to compute the control signals which 20 | minimize some objective function over a time horizon. This is referred to as an 21 | Optimal Control Problem (OCP). 22 | 23 | The system under consideration is most likely subjected to some uncontrolled 24 | disturbances. The OCP thus requires a prediction of these disturbances over the 25 | prediction horizon to be able to compute the system behavior. Furthermore, an 26 | estimation of the current system state is needed, probably requiring some 27 | (virtual) measurements. 28 | 29 | An MPC simulation can thus be broken down into the following steps: 30 | 31 | * Estimate the state of the system. 32 | * Generate a prediction for the disturbances over the control horizon. 33 | * Compute control signals by solving an OCP. 34 | * Supply the control signals to a system emulator and simulate the system behavior. 35 | * Repeat. 36 | 37 | 38 | In mpcpy the :code:`mpcpy.mcp` object contains the main functions for 39 | orchestrating the MPC. It depends on an :code:`mpcpy.emulator` object, an 40 | :code:`mpcpy.disturbances` object and an :code:`mpcpy.control` object. 41 | 42 | The emulation time is split up in parts defined by the control receding time. 43 | For each part, the above steps are executed and the emulator simulates the 44 | system over the receding time. 45 | 46 | The :code:`mpcpy.control` object handles the generation of control signals. 47 | Therfore it requires an :code:`mpcpy.stateestimation` object and an 48 | :code:`mpcpy.prediction` object. 49 | 50 | Example 51 | ^^^^^^^ 52 | 53 | When the :code:`mpcpy.emulator`, :code:`mpcpy.control` and 54 | :code:`mpcpy.disturbances` objects are defined, an :code:`mpcpy.mpc` object can 55 | be instantiated: 56 | 57 | .. literalinclude:: ../../examples/quickstart.py 58 | :lines: 171 59 | 60 | 61 | 62 | Emulator 63 | -------- 64 | 65 | The :code:`mpcpy.emulator` class serves as a base class for defining emulator 66 | objects. By default, the emulator object returns the control solution as its 67 | result. So when no detailed emulator model is available, the 68 | :code:`mpcpy.emulator` class can be used to split an OCP in different parts and 69 | solve it sequentially. Care must be taken that the OCPs overlap sufficiently, so 70 | the receding time must be significantly shorter than the horizon. 71 | 72 | The :code:`mpcpy.emulator` class requires a list of keys as input arguments, 73 | this list specifies which disturbances and control signals should be 74 | passed to the simulation, all control signals are passed to the simulation. 75 | 76 | When subclassing the :code:`mpcpy.emulator` class the main method to be 77 | redefined is the :code:`simulate` method. This method receives a starttime, 78 | stoptime and input dictionary as arguments and should return a results 79 | dictionary with a time array and values for the results between the start- and 80 | stoptime. 81 | 82 | Internally a :code:`res` dictionary holds the results of the simulation up to 83 | the current point in time. This :code:`res` dictionary can also be used to 84 | determine the state required for the OCP. 85 | 86 | The system can then be simulated by calling an :code:`mpcpy.emulator` object 87 | with an array of input times (the time steps at which results are required) and 88 | a dictionary of inputs. During an MPC simulation this is handled by the 89 | :code:`mpcpy.mpc` object. 90 | 91 | Example 92 | ^^^^^^^ 93 | 94 | .. literalinclude:: ../../examples/quickstart.py 95 | :lines: 27-56,134-144 96 | 97 | 98 | Disturbances 99 | ------------ 100 | 101 | The :code:`mpcpy.disturbances` class is a helper class for manging the 102 | disturbances. 103 | Signals are defined based on a dictionary which must include a :code:`time` key 104 | and specifies the values of all signals at these time steps. 105 | An :code:`mpcpy.disturbances` object can then interpolate the values to 106 | the required time points in the :code:`mpcpy.emulator` and 107 | :code:`mpcpy.prediction` objects. 108 | 109 | Example 110 | ^^^^^^^ 111 | 112 | .. literalinclude:: ../../examples/quickstart.py 113 | :lines: 148-153 114 | 115 | 116 | Control 117 | ------- 118 | The emulator requires control inputs which are generated by an object derived 119 | from the :code:`mpcpy.Control` base class. 120 | The methods that should be redefined in a child class are the 121 | :code:`formulation` and :code:`solution` methods. 122 | 123 | The :code:`formulation` method is called once by the :code:`mpcpy.mpc` object 124 | before the start of the simulation. This method can thus be used to formulate an 125 | abstract optimal control problem and assign values that will not change through 126 | the course of the simulation. 127 | 128 | The :code`solution` method should generate the control signals to be used by the 129 | emulator. When the control signals should be generated, it is called with two 130 | parameters representing the current state of the system and the predictions of 131 | the disturbances over the control horizon. These are generated by the 132 | :code:`mpcpy.stateestimation` and :code:`mpcpy.prediction` objects supplied to 133 | the control object during initialization respectively. 134 | The :code`solution` method must return a dictionary with a time vector and the 135 | control signal values over the entire control horizon. 136 | 137 | Example 138 | ^^^^^^^ 139 | 140 | .. literalinclude:: ../../examples/quickstart.py 141 | :lines: 60-121 142 | 143 | When the stateestimation and prediction objects are defined, a control object 144 | can be instantiated: 145 | 146 | .. literalinclude:: ../../examples/quickstart.py 147 | :lines: 169-170 148 | 149 | 150 | State estimation 151 | ---------------- 152 | 153 | The :code:`mpcpy.stateestimation` class is a base class for defining object that 154 | estimate the current state required for the control based on measurements done 155 | on the emulator. When the states required for the control problem are not 156 | directly measurable they are often computed with a Kalman filter. 157 | The main method to redefine in a child class is the :code`stateestimation` 158 | method. This method takes the time at which the states should be estimated as an 159 | input parameter and must return a dictionary of key-value pairs representing the 160 | state at that time. 161 | 162 | Example 163 | ^^^^^^^ 164 | 165 | .. literalinclude:: ../../examples/quickstart.py 166 | :lines: 125-130,157-159 167 | 168 | 169 | Prediction 170 | ---------- 171 | 172 | The :code:`mpcpy.prediction` class is a base class for returning prediction data 173 | required for the OCP. 174 | 175 | It requires a :code:`mpcpy.disturbances` object which represent the actual 176 | conditions. From these values predictions may be derived. 177 | 178 | Example 179 | ^^^^^^^ 180 | 181 | .. literalinclude:: ../../examples/quickstart.py 182 | :lines: 163-165 183 | 184 | 185 | MPC 186 | --- 187 | 188 | The entire MPC simulation can now be run from the above defined objects. Just 189 | call the :code:`mpcpy.MPC` object to start the simulation. Results will be 190 | returned in a dictionary. 191 | 192 | 193 | Example 194 | ^^^^^^^ 195 | 196 | .. literalinclude:: ../../examples/quickstart.py 197 | :lines: 169-175 198 | 199 | 200 | The optimal control inputs and resulting state of the quickstart example is presented below. 201 | 202 | .. plot:: ../../examples/quickstart.py 203 | -------------------------------------------------------------------------------- /doc/source/reference.rst: -------------------------------------------------------------------------------- 1 | Reference 2 | ========= 3 | .. toctree:: 4 | :maxdepth: 3 5 | 6 | disturbances 7 | emulator 8 | stateestimation 9 | prediction 10 | control 11 | mpc -------------------------------------------------------------------------------- /doc/source/stateestimation.rst: -------------------------------------------------------------------------------- 1 | Stateestimation 2 | =============== 3 | 4 | .. autoclass:: mpcpy.Stateestimation 5 | :members: 6 | :special-members: __call__ -------------------------------------------------------------------------------- /examples/.gitignore: -------------------------------------------------------------------------------- 1 | .ipynb_checkpoints 2 | dsin.txt -------------------------------------------------------------------------------- /examples/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BrechtBa/mpcpy/108b7a19bc07f17b1fae30ee8ecfdd1e732c1c47/examples/__init__.py -------------------------------------------------------------------------------- /examples/quickstart.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | ################################################################################ 3 | # Copyright 2015 Brecht Baeten 4 | # This file is part of mpcpy. 5 | # 6 | # mpcpy is free software: you can redistribute it and/or modify 7 | # it under the terms of the GNU General Public License as published by 8 | # the Free Software Foundation, either version 3 of the License, or 9 | # (at your option) any later version. 10 | # 11 | # mpcpy is distributed in the hope that it will be useful, 12 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 13 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14 | # GNU General Public License for more details. 15 | # 16 | # You should have received a copy of the GNU General Public License 17 | # along with mpcpy. If not, see . 18 | ################################################################################ 19 | 20 | import numpy as np 21 | import matplotlib.pyplot as plt 22 | 23 | import mpcpy 24 | import pyomo.environ as pyomo 25 | 26 | 27 | # Define an emulator class 28 | class Emulator(mpcpy.Emulator): 29 | """ 30 | A custom system emulator 31 | """ 32 | def simulate(self, starttime, stoptime, input): 33 | dt = 1 34 | time = np.arange(starttime, stoptime+dt, dt, dtype=np.float) 35 | 36 | # initialize 37 | x = np.ones_like(time)*self.res['x'][-1] 38 | 39 | # interpolate inputs 40 | u = np.interp(time, input['time'], input['u']) 41 | d = np.interp(time, input['time'], input['d']) 42 | 43 | # perform simulation 44 | for i, t in enumerate(time[:-1]): 45 | # dx/dt = A*x + d + u 46 | x[i+1] = x[i] + (self.parameters['A']*x[i] + d[i] + u[i])*dt 47 | 48 | # create and return a results dict 49 | res = { 50 | 'time': time, 51 | 'x': x, 52 | 'd': d, 53 | 'u': u, 54 | } 55 | 56 | return res 57 | 58 | 59 | # Define a control class 60 | class SetpointControl(mpcpy.Control): 61 | """ 62 | A control to keep the state as close to a set point as possible 63 | """ 64 | def formulation(self): 65 | # create a pyomo model 66 | model = pyomo.AbstractModel() 67 | 68 | model.i = pyomo.Set() 69 | model.ip = pyomo.Set() 70 | 71 | model.time = pyomo.Param(model.ip) 72 | model.d = pyomo.Param(model.ip, initialize=0.) 73 | model.x = pyomo.Var(model.ip, domain=pyomo.Reals, initialize=0.) 74 | model.u = pyomo.Var(model.ip, domain=pyomo.NonNegativeReals, bounds=(0., 1.), initialize=0.) 75 | 76 | model.x0 = pyomo.Param(initialize=0.) 77 | 78 | model.initialcondition = pyomo.Constraint( 79 | rule=lambda model: model.x[0] == model.x0 80 | ) 81 | 82 | model.constraint = pyomo.Constraint( 83 | model.i, 84 | rule=lambda model, i: (model.x[i+1]-model.x[i])/(model.time[i+1]-model.time[i]) == 85 | self.parameters['A']*model.x[i] + model.d[i] + model.u[i] 86 | ) 87 | 88 | model.objective = pyomo.Objective( 89 | rule=lambda model: sum((model.x[i]-self.parameters['set'])**2 for i in model.i) 90 | ) 91 | 92 | # store the model inside the object 93 | self.model = model 94 | 95 | def solution(self, sta, pre): 96 | # create data and instantiate the pyomo model 97 | ip = np.arange(len(pre['time'])) 98 | data = { 99 | None: { 100 | 'i': {None: ip[:-1]}, 101 | 'ip': {None: ip}, 102 | 'time': {(i,): v for i, v in enumerate(pre['time'])}, 103 | 'x0': {None: sta['x']}, 104 | 'd': {(i,): pre['d'][i] for i in ip}, 105 | } 106 | } 107 | 108 | instance = self.model.create_instance(data) 109 | 110 | # solve and return the control inputs 111 | optimizer = pyomo.SolverFactory('ipopt') 112 | results = optimizer.solve(instance) 113 | 114 | sol = { 115 | 'time': np.array([pyomo.value(instance.time[i]) for i in instance.ip]), 116 | 'x': np.array([pyomo.value(instance.x[i]) for i in instance.ip]), 117 | 'u': np.array([pyomo.value(instance.u[i]) for i in instance.ip]), 118 | 'd': np.array([pyomo.value(instance.d[i]) for i in instance.ip]), 119 | } 120 | 121 | return sol 122 | 123 | 124 | # Define a state estimation class 125 | class StateestimationPerfect(mpcpy.Stateestimation): 126 | """ 127 | Perfect state estimation 128 | """ 129 | def stateestimation(self, time): 130 | return {'x': np.interp(time, self.emulator.res['time'], self.emulator.res['x'])} 131 | 132 | 133 | # instantiate the emulator 134 | emulator = Emulator(['u', 'd'], parameters={'A': -0.2}, initial_conditions={'x': 0}) 135 | 136 | # test the emulator with some random data 137 | time = np.arange(0., 1001., 10.) 138 | np.random.seed(0) 139 | d = np.random.random(len(time)) - 0.5 140 | u = 1.0*np.ones(len(time)) 141 | 142 | emulator.initialize() 143 | res = emulator(time, {'time': time, 'd': d, 'u': u}) 144 | print(res) 145 | 146 | 147 | # create a disturbances object 148 | time = np.arange(0., 1001., 10.) 149 | d = 0.5*np.sin(2*np.pi*time/1000) 150 | disturbances = mpcpy.Disturbances({'time': time, 'd': d}) 151 | 152 | bcs = disturbances(np.array([0, 20, 40, 60, 100])) 153 | print(bcs) 154 | 155 | 156 | # create a stateestimation object 157 | stateestimation = StateestimationPerfect(emulator) 158 | sta = stateestimation(0) 159 | print(sta) 160 | 161 | 162 | # create a prediction object 163 | prediction = mpcpy.Prediction(disturbances) 164 | pre = prediction(np.array([0, 20, 40, 60, 100])) 165 | print(pre) 166 | 167 | 168 | # create a control object and mpc object 169 | control = SetpointControl(stateestimation, prediction, parameters={'A': -0.2, 'set': 3.0}, 170 | horizon=100., timestep=10., receding=10.) 171 | mpc = mpcpy.MPC(emulator, control, disturbances, emulationtime=1000, resulttimestep=10) 172 | 173 | # run the mpc 174 | res = mpc(verbose=1) 175 | print(res) 176 | 177 | 178 | # plot results 179 | fig, ax = plt.subplots(2, 1) 180 | ax[0].plot(res['time'], res['u']) 181 | ax[0].set_ylabel('u') 182 | 183 | ax[1].plot(res['time'], res['x']) 184 | ax[1].set_xlabel('time') 185 | ax[1].set_ylabel('x') 186 | 187 | 188 | if __name__ == '__main__': 189 | plt.show() -------------------------------------------------------------------------------- /examples/simple_space_heating_mpc.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | ################################################################################ 3 | # Copyright 2015 Brecht Baeten 4 | # This file is part of mpcpy. 5 | # 6 | # mpcpy is free software: you can redistribute it and/or modify 7 | # it under the terms of the GNU General Public License as published by 8 | # the Free Software Foundation, either version 3 of the License, or 9 | # (at your option) any later version. 10 | # 11 | # mpcpy is distributed in the hope that it will be useful, 12 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 13 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14 | # GNU General Public License for more details. 15 | # 16 | # You should have received a copy of the GNU General Public License 17 | # along with mpcpy. If not, see . 18 | ################################################################################ 19 | 20 | import numpy as np 21 | import matplotlib.pyplot as plt 22 | import pyomo.environ as pyomo 23 | 24 | import mpcpy 25 | 26 | # Disturbances 27 | time = np.arange(0.,24.01*3600.,3600.) 28 | dst = { 29 | 'time': time, 30 | 'T_am': 5 + 2*np.sin(2*np.pi*time/24./3600.)+273.15, 31 | 'Q_flow_so': 500 + 500*np.sin(2*np.pi*time/24./3600.), 32 | 'p_el': 0.2 + 0.05*np.sin(2*np.pi*time/24./3600.), 33 | 'Q_flow_hp_max': 5000*np.ones_like(time), 34 | 'T_in_min': 20*np.ones_like(time)+273.15, 35 | 'T_em_max': 30*np.ones_like(time)+273.15 36 | } 37 | 38 | disturbances = mpcpy.Disturbances(dst, periodic=False) 39 | # test 40 | print(disturbances(1800)) 41 | print(disturbances(24.5 * 3600)) # extrapolation 42 | 43 | 44 | # Emulator 45 | class Emulator(mpcpy.Emulator): 46 | """ 47 | A custom system emulator 48 | """ 49 | def simulate(self, starttime, stoptime, input): 50 | dt = 60 51 | time = np.arange(starttime, stoptime+dt, dt, dtype=np.float) 52 | 53 | # initialize 54 | T_em = np.ones_like(time)*self.res['T_em'][-1] 55 | T_in = np.ones_like(time)*self.res['T_in'][-1] 56 | 57 | # interpolate inputs 58 | Q_flow_hp = np.interp(time, input['time'], input['Q_flow_hp']) 59 | Q_flow_so = np.interp(time, input['time'], input['Q_flow_so']) 60 | T_am = np.interp(time, input['time'], input['T_am']) 61 | 62 | for i,t in enumerate(time[:-1]): 63 | # C_em dT_em/dt = Q_flow_hp - UA_em_in*(T_em-T_in) 64 | T_em[i+1] = T_em[i] + (Q_flow_hp[i] - self.parameters['UA_em_in']*(T_em[i]-T_in[i]))*dt/self.parameters['C_em'] 65 | 66 | # C_in dT_in/dt = Q_flow_so - UA_em_in*(T_in-T_em) - UA_in_am*(T_in-T_am) 67 | T_in[i+1] = T_in[i] + (Q_flow_so[i] - self.parameters['UA_em_in']*(T_in[i]-T_em[i]) - self.parameters['UA_in_am']*(T_in[i]-T_am[i]))*dt/self.parameters['C_em'] 68 | 69 | # create and return a results dict 70 | res = { 71 | 'time': time, 72 | 'Q_flow_hp':Q_flow_hp, 73 | 'Q_flow_so':Q_flow_so, 74 | 'T_em':T_em, 75 | 'T_in':T_in, 76 | 'T_am':T_am, 77 | } 78 | 79 | return res 80 | 81 | 82 | # Emulator parameters and initial conditions: 83 | emulator_parameters = { 84 | 'C_em': 10e6, 85 | 'C_in': 5e6, 86 | 'UA_in_am': 200, 87 | 'UA_em_in': 1600 88 | } 89 | emulator_initial_conditions = { 90 | 'T_em': 22+273.15, 91 | 'T_in': 21+273.15 92 | } 93 | 94 | emulator = Emulator(['T_am','Q_flow_so','Q_flow_hp'],parameters=emulator_parameters,initial_conditions=emulator_initial_conditions) 95 | emulator.initialize() 96 | # test 97 | inp = { 98 | 'time': [0., 3600., 7200.], 99 | 'T_am': [273.15, 274.15, 275.15], 100 | 'Q_flow_so': [500., 400., 300.], 101 | 'Q_flow_hp': [4000., 4000., 4000.] 102 | } 103 | emulator(np.arange(0., 7201., 1200.), inp) 104 | 105 | print(emulator.res['time']) 106 | print(emulator.res['T_em']) 107 | 108 | 109 | # State estimation 110 | class StateestimationPerfect(mpcpy.Stateestimation): 111 | """ 112 | Custom state estimation method 113 | """ 114 | def stateestimation(self, time): 115 | state = {} 116 | state['T_in'] = np.interp(time, self.emulator.res['time'], self.emulator.res['T_in']) 117 | state['T_em'] = np.interp(time, self.emulator.res['time'], self.emulator.res['T_em']) 118 | 119 | return state 120 | 121 | stateestimation = StateestimationPerfect(emulator) 122 | # test 123 | print(stateestimation(0)) 124 | 125 | 126 | # Prediction 127 | prediction = mpcpy.Prediction(disturbances) 128 | # test 129 | print(prediction([0., 1800., 3600.])) 130 | 131 | 132 | # Control 133 | class LinearProgram(mpcpy.Control): 134 | def formulation(self): 135 | """ 136 | formulates the abstract optimal control problem 137 | """ 138 | model = pyomo.AbstractModel() 139 | 140 | # sets 141 | model.i = pyomo.Set() # initialize=range(len(time)-1) 142 | model.ip = pyomo.Set() # initialize=range(len(time)) 143 | 144 | # parameters 145 | model.time = pyomo.Param(model.ip) 146 | 147 | model.UA_em_in = pyomo.Param(initialize=800.) 148 | model.UA_in_am = pyomo.Param(initialize=200.) 149 | 150 | model.C_in = pyomo.Param(initialize=5.0e6) 151 | model.C_em = pyomo.Param(initialize=20.0e6) 152 | 153 | model.T_in_ini = pyomo.Param(initialize=21.+273.15) 154 | model.T_em_ini = pyomo.Param(initialize=22.+273.15) 155 | 156 | model.T_in_min = pyomo.Param(initialize=20.+273.15) 157 | model.T_in_max = pyomo.Param(initialize=24.+273.15) 158 | 159 | model.T_am = pyomo.Param(model.i, initialize=0.+273.15) 160 | model.Q_flow_so = pyomo.Param(model.i, initialize=0.) 161 | 162 | # variables 163 | model.T_in = pyomo.Var(model.ip,domain=pyomo.Reals, initialize=20.+273.15) 164 | model.T_em = pyomo.Var(model.ip,domain=pyomo.Reals,initialize=20.+273.15) 165 | 166 | model.T_in_min_slack = pyomo.Var(model.ip,domain=pyomo.NonNegativeReals, initialize=0) 167 | model.T_in_max_slack = pyomo.Var(model.ip,domain=pyomo.NonNegativeReals, initialize=0) 168 | 169 | model.Q_flow_hp = pyomo.Var(model.i,domain=pyomo.NonNegativeReals,bounds=(0.,10000.),initialize=0.) 170 | 171 | # constraints 172 | model.state_T_em = pyomo.Constraint( 173 | model.i, 174 | rule=lambda model,i: model.C_em*(model.T_em[i+1]-model.T_em[i])/(model.time[i+1]-model.time[i]) == \ 175 | model.Q_flow_hp[i] \ 176 | - model.UA_em_in*(model.T_em[i]-model.T_in[i]) 177 | ) 178 | model.ini_T_em = pyomo.Constraint(rule=lambda model: model.T_em[0] == model.T_em_ini) 179 | 180 | model.state_T_in = pyomo.Constraint( 181 | model.i, 182 | rule=lambda model,i: model.C_in*(model.T_in[i+1]-model.T_in[i])/(model.time[i+1]-model.time[i]) == \ 183 | model.Q_flow_so[i] \ 184 | - model.UA_em_in*(model.T_in[i]-model.T_em[i]) \ 185 | - model.UA_in_am*(model.T_in[i]-model.T_am[i]) 186 | ) 187 | model.ini_T_in = pyomo.Constraint(rule=lambda model: model.T_in[0] == model.T_in_ini) 188 | 189 | # soft constraints 190 | model.constraint_T_in_min_slack = pyomo.Constraint( 191 | model.ip, 192 | rule=lambda model,i: model.T_in_min_slack[i] >= model.T_in_min-model.T_in[i] 193 | ) 194 | 195 | model.constraint_T_in_max_slack = pyomo.Constraint( 196 | model.ip, 197 | rule=lambda model,i: model.T_in_max_slack[i] >= model.T_in[i]-model.T_in_max 198 | ) 199 | 200 | # a large number 201 | L = 1e6 202 | 203 | # objective 204 | model.objective = pyomo.Objective( 205 | rule=lambda model: sum(model.Q_flow_hp[i]*(model.time[i+1]-model.time[i])/3600/1000 for i in model.i) \ 206 | +sum(model.T_in_min_slack[i]*(model.time[i+1]-model.time[i])/3600 for i in model.i)*L\ 207 | +sum(model.T_in_max_slack[i]*(model.time[i+1]-model.time[i])/3600 for i in model.i)*L\ 208 | ) 209 | 210 | self.model = model 211 | 212 | def solution(self, sta, pre): 213 | """ 214 | instanciate the optimal control problem, solve it and return a solution dictionary 215 | """ 216 | 217 | ip = np.arange(len(pre['time'])) 218 | 219 | data = { 220 | None: { 221 | 'i': {None: ip[:-1]}, 222 | 'ip': {None: ip}, 223 | 'time': {(i,): v for i, v in enumerate(pre['time'])}, 224 | 'T_am': {(i,): pre['T_am'][i] for i in ip[:-1]}, 225 | 'Q_flow_so': {(i,): pre['Q_flow_so'][i] for i in ip[:-1]}, 226 | 'T_em_ini': {None: sta['T_em']}, 227 | 'T_in_ini': {None: sta['T_in']}, 228 | 'C_em': {None: self.parameters['C_em']}, 229 | 'C_in': {None: self.parameters['C_in']}, 230 | 'UA_em_in': {None: self.parameters['UA_em_in']}, 231 | 'UA_in_am': {None: self.parameters['UA_in_am']}, 232 | } 233 | } 234 | 235 | instance = self.model.create_instance(data) 236 | optimizer = pyomo.SolverFactory('ipopt') 237 | results = optimizer.solve(instance) 238 | 239 | # return the contol inputs 240 | sol = { 241 | 'time': np.array([pyomo.value(instance.time[i]) for i in instance.ip]), 242 | 'T_em': np.array([pyomo.value(instance.T_em[i]) for i in instance.ip]), 243 | 'T_in': np.array([pyomo.value(instance.T_in[i]) for i in instance.ip]), 244 | 'Q_flow_hp': np.array([pyomo.value(instance.Q_flow_hp[i]) for i in instance.i]), 245 | } 246 | 247 | return sol 248 | 249 | 250 | # Control parameters 251 | control_parameters = { 252 | 'C_in': emulator_parameters['C_in'], 253 | 'C_em': emulator_parameters['C_em'], 254 | 'UA_in_am': emulator_parameters['UA_in_am'], 255 | 'UA_em_in': emulator_parameters['UA_em_in'], 256 | } 257 | # create an instance 258 | control = LinearProgram(stateestimation, prediction, parameters=control_parameters, horizon=24*3600., timestep=3600.) 259 | # test 260 | print(control(0)) 261 | 262 | 263 | # MPC 264 | mpc = mpcpy.MPC(emulator, control, disturbances, emulationtime=1*24*3600., resulttimestep=60) 265 | res = mpc(verbose=1) 266 | 267 | 268 | # Plot results 269 | fix, ax = plt.subplots(2, 1) 270 | ax[0].plot(res['time']/3600, res['Q_flow_hp'], 'k', label='hp') 271 | ax[0].plot(res['time']/3600, res['Q_flow_so'], 'g', label='sol') 272 | ax[0].set_ylabel('Heat flow rate (W)') 273 | ax[0].legend(loc='lower right') 274 | 275 | ax[1].plot(res['time']/3600, res['T_in']-273.17, 'k', label='in') 276 | ax[1].plot(res['time']/3600, res['T_em']-273.17, 'b', label='em') 277 | ax[1].plot(res['time']/3600, res['T_am']-273.17, 'g', label='amb') 278 | ax[1].set_ylabel('Temperature ($^\circ$C)') 279 | ax[1].set_xlabel('Time (h)') 280 | ax[1].legend(loc='lower right') 281 | 282 | 283 | # Using the default emulator 284 | # The default emulator simply reuses the control solution. The results are a bit different due to model mismatch. 285 | def_emulator = mpcpy.Emulator(['T_am', 'Q_flow_so', 'Q_flow_hp'],initial_conditions=emulator_initial_conditions) 286 | emulator.initialize() 287 | 288 | def_stateestimation = StateestimationPerfect(def_emulator) 289 | def_control = LinearProgram(def_stateestimation, prediction, 290 | parameters=control_parameters, horizon=24*3600., timestep=3600.) 291 | def_mpc = mpcpy.MPC(def_emulator, def_control, disturbances, emulationtime=1*24*3600., resulttimestep=60) 292 | def_res = def_mpc(verbose=1) 293 | 294 | fix, ax = plt.subplots(2, 1) 295 | ax[0].plot(def_res['time']/3600, def_res['Q_flow_hp'], 'k', label='hp') 296 | ax[0].plot(res['time']/3600, res['Q_flow_hp'], 'k--') 297 | ax[0].plot(def_res['time']/3600, def_res['Q_flow_so'], 'g', label='sol') 298 | ax[0].set_ylabel('Heat flow rate (W)') 299 | ax[0].legend(loc='lower right') 300 | 301 | ax[1].plot(def_res['time']/3600, def_res['T_in']-273.17, 'k', label='in') 302 | ax[1].plot(def_res['time']/3600, def_res['T_em']-273.17, 'b', label='em') 303 | ax[1].plot(def_res['time']/3600, def_res['T_am']-273.17, 'g', label='amb') 304 | ax[1].plot(res['time']/3600, res['T_in']-273.17, 'k--') 305 | ax[1].plot(res['time']/3600, res['T_em']-273.17, 'b--') 306 | ax[1].set_ylabel('Temperature ($^\circ$C)') 307 | ax[1].set_xlabel('Time (h)') 308 | ax[1].legend(loc='lower right') 309 | 310 | 311 | if __name__ == '__main__': 312 | plt.show() 313 | -------------------------------------------------------------------------------- /mpcpy/__init__.py: -------------------------------------------------------------------------------- 1 | from .__version__ import version as __version__ 2 | 3 | __all__ = ['disturbances.py', 'control', 'emulator', 'mpc', 'prediction', 'stateestimation'] 4 | 5 | from .disturbances import Disturbances 6 | from .control import * 7 | from .emulator import * 8 | from .mpc import MPC 9 | from .prediction import Prediction 10 | from .stateestimation import Stateestimation 11 | -------------------------------------------------------------------------------- /mpcpy/__version__.py: -------------------------------------------------------------------------------- 1 | version = '0.3.0' -------------------------------------------------------------------------------- /mpcpy/control.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | ################################################################################ 3 | # Copyright 2015 Brecht Baeten 4 | # This file is part of mpcpy. 5 | # 6 | # mpcpy is free software: you can redistribute it and/or modify 7 | # it under the terms of the GNU General Public License as published by 8 | # the Free Software Foundation, either version 3 of the License, or 9 | # (at your option) any later version. 10 | # 11 | # mpcpy is distributed in the hope that it will be useful, 12 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 13 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14 | # GNU General Public License for more details. 15 | # 16 | # You should have received a copy of the GNU General Public License 17 | # along with mpcpy. If not, see . 18 | ################################################################################ 19 | 20 | import sys 21 | import numpy as np 22 | 23 | class Control(object): 24 | """ 25 | Base class for defining the control for an mpc simulation 26 | 27 | the :code:`formulation` and :code:`solution` methods must be redefined in a 28 | child class to generate control signals over the control horizon 29 | 30 | """ 31 | 32 | def __init__(self, stateestimation, prediction, 33 | parameters=None, horizon=None, timestep=None, receding=None, savesolutions=0): 34 | """ 35 | Initializes the control object 36 | 37 | Parameters 38 | ---------- 39 | stateestimation : mpcpy.Stateestimation 40 | The object used to determine the state at the beginning of the 41 | control horizon. 42 | 43 | prediction : mpcpy.Prediction object 44 | The object used to determine the predictions over the control 45 | horizon. 46 | 47 | parameters : dict 48 | Dictionary specifying control parameters. 49 | 50 | horizon : number 51 | The length of the control horizon. 52 | 53 | timestep : number 54 | The length of the control timestep. 55 | 56 | receding : number 57 | The receding time, i.e. the time between subsequent calls to 58 | this control object. 59 | 60 | savesolutions : int 61 | Number of control solutions to be saved in the control object. Set 62 | to -1 to save all solutions. 63 | 64 | """ 65 | 66 | self.stateestimation = stateestimation 67 | self.prediction = prediction 68 | 69 | if horizon is None: 70 | raise Exception('horizon parameter must be supplied') 71 | else: 72 | self.horizon = horizon 73 | 74 | if timestep is None: 75 | raise Exception('timestep parameter must be supplied') 76 | else: 77 | self.timestep = timestep 78 | 79 | self.receding = receding 80 | if self.receding is None: 81 | self.receding = self.timestep 82 | 83 | self.parameters = {} 84 | if not parameters is None: 85 | self.parameters = parameters 86 | 87 | self.savesolutions = savesolutions 88 | self.solutions = [] 89 | 90 | self._formulated = False 91 | 92 | def time(self,starttime): 93 | """ 94 | Returns a time vector over the control horizon with the defined timestep 95 | 96 | Parameters 97 | ---------- 98 | starttime : real 99 | Time at the beginning of the control horizon. 100 | 101 | """ 102 | return np.arange(starttime, starttime+self.horizon+0.01*self.timestep, self.timestep, dtype=np.float) 103 | 104 | def formulation(self): 105 | """ 106 | Performs actions the first time the control is run. 107 | 108 | Can be used to set up an optimal control problem. The set-up and 109 | solution are separated as sometimes the set-up takes a significant 110 | amount of time and performs actions which must not be repeated. 111 | 112 | Should be redefined in a child class to set up the actual control 113 | algorithm. 114 | 115 | """ 116 | pass 117 | 118 | 119 | def solution(self,state,prediction): 120 | """ 121 | Returns the control profiles ("the plan"). 122 | 123 | Should be redefined in a child class to set up the actual control 124 | algorithm. 125 | 126 | Parameters 127 | ---------- 128 | state : dict 129 | Dictionary with the states at the start of the control horizon as 130 | returned by the stateestimation object. 131 | 132 | prediction : dict 133 | Dictionary with the values of predictions over the control horizon 134 | as returned by the prediction object. 135 | 136 | Returns 137 | ------- 138 | dict 139 | Dictionary with the solution, "time" must be a key. 140 | """ 141 | sol = {} 142 | return sol 143 | 144 | 145 | def __call__(self,starttime): 146 | """ 147 | Calculate the value of the control signal over the control horizon. 148 | Calls the :code:`solution` method. 149 | 150 | Parameters 151 | ---------- 152 | starttime : real 153 | Time at the beginning of the control horizon. 154 | 155 | Returns 156 | ------- 157 | dict 158 | A dictionary with representing the control signals with time. 159 | 160 | """ 161 | 162 | # get the state and the predictions 163 | state = self.stateestimation(starttime) 164 | prediction = self.prediction(self.time(starttime)) 165 | 166 | # formulate the ocp during the first call 167 | if not self._formulated: 168 | tempsolution = self.formulation() 169 | if tempsolution != None: 170 | print('Warning: returning a solution function from the "formulation" method is depreciated. Overwrite the "solution" method instead.') 171 | self.solution = tempsolution 172 | 173 | self._formulated = True 174 | 175 | # solve the ocp 176 | solution = self.solution(state,prediction) 177 | 178 | 179 | if self.savesolutions == -1: 180 | # save all solutions 181 | self.solutions.append(solution) 182 | 183 | elif self.savesolutions > 0: 184 | # save only the last x solutions 185 | self.solutions.append(solution) 186 | if len(self.solutions) > self.savesolutions: 187 | self.solutions.pop(0) 188 | 189 | return solution 190 | 191 | 192 | def cplex_infeasibilityanalysis(ocp): 193 | """ 194 | Give information about infeasible constraints in cplex. 195 | 196 | Parameters 197 | ---------- 198 | ocp : CPlex object 199 | A CPlex optimization problem which is infeasible 200 | 201 | """ 202 | 203 | ocp.conflict.refine(ocp.conflict.all_constraints()) 204 | 205 | try: 206 | for conflict,v in enumerate(ocp.conflict.get()): 207 | if v > 0: 208 | for c in ocp.conflict.get_groups(conflict)[1]: 209 | if c[0]==1: 210 | print( 'Lower bound constraint on variable {}: {}'.format(c[1],ocp.variables.get_names(c[1])) ) 211 | if c[0]==2: 212 | print( 'Upper bound constraint on variable {}: {}'.format(c[1],ocp.variables.get_names(c[1])) ) 213 | if c[0]==3: 214 | print( 'Linear constraint {}: {}'.format(c[1],ocp.linear_constraints.get_names(c[1])) ) 215 | else: 216 | print(ocp.conflict.get_groups(conflict)) 217 | except Exception as e: 218 | pass -------------------------------------------------------------------------------- /mpcpy/disturbances.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | ################################################################################ 3 | # Copyright 2015 Brecht Baeten 4 | # This file is part of mpcpy. 5 | # 6 | # mpcpy is free software: you can redistribute it and/or modify 7 | # it under the terms of the GNU General Public License as published by 8 | # the Free Software Foundation, either version 3 of the License, or 9 | # (at your option) any later version. 10 | # 11 | # mpcpy is distributed in the hope that it will be useful, 12 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 13 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14 | # GNU General Public License for more details. 15 | # 16 | # You should have received a copy of the GNU General Public License 17 | # along with mpcpy. If not, see . 18 | ################################################################################ 19 | 20 | import numpy as np 21 | 22 | 23 | class Disturbances(object): 24 | """ 25 | A class to define disturbances in the format required by mpcpy. 26 | 27 | """ 28 | 29 | def __init__(self, data, periodic=True, extra_time=7*24*3600., zoh_keys=None): 30 | """ 31 | Create a disturbances object. 32 | 33 | Parameters 34 | ---------- 35 | data : dict 36 | Actual boundary conditions and a time vector. 37 | 38 | periodic : boolean, optional 39 | Determines how to determine values when time is larger than the 40 | boundary conditions time. 41 | 42 | extra_time : float, optional 43 | Maximum allowed time outside the boundary conditions time, should be 44 | at least as large as the control horizon. 45 | 46 | zoh_keys : list of strings, optional 47 | Keys which will be interpolated with zero-order hold. All other 48 | values are interpolated linearly. 49 | 50 | Examples 51 | -------- 52 | >>> dst = Disturbances({'time': np.arange(0.,24*3600.+1,3600.), 'T_amb':np.random.random(25)}) 53 | >>> dst(12.1*3600) 54 | 55 | """ 56 | 57 | self.data = {} 58 | 59 | # create a new time vector including the extra time 60 | # this method is about 2 times faster than figuring out the correct time during interpolation 61 | ind = np.where(data['time']-data['time'][0] < extra_time)[0] 62 | self.data['time'] = np.concatenate((data['time'][:-1], data['time'][ind]+data['time'][-1]-data['time'][0])) 63 | 64 | if periodic: 65 | # the values at time lower than extra_time are repeated at the end of the dataset 66 | # extra_time should thus be larger than the control horizon 67 | for key in data: 68 | if key != 'time': 69 | self.data[key] = np.concatenate((data[key][:-1], data[key][ind])) 70 | else: 71 | # last value is repeated after the actual data 72 | for key in data: 73 | if key != 'time': 74 | self.data[key] = np.concatenate((data[key][:-1], data[key][-1]*np.ones(len(ind)))) 75 | 76 | if zoh_keys is None: 77 | self.zoh_keys = [] 78 | else: 79 | self.zoh_keys = zoh_keys 80 | 81 | def interp(self, key, time): 82 | """ 83 | Interpolate a value to an array of timesteps 84 | 85 | Parameters 86 | ---------- 87 | key : str 88 | The key to interpolate. 89 | 90 | time : np.array 91 | An array of times to interpolate to. 92 | 93 | """ 94 | 95 | if len(np.array(self.data[key]).shape) == 1: 96 | if key in self.zoh_keys: 97 | value = interp_zoh(time, self.data['time'], self.data[key]) 98 | else: 99 | value = np.interp(time, self.data['time'], self.data[key]) 100 | 101 | elif len(np.array(self.data[key]).shape) == 2: 102 | # 2d boundary conditions support 103 | value = np.zeros((len(time), self.data[key].shape[1])) 104 | if key in self.zoh_keys: 105 | for j in range(self.data[key].shape[1]): 106 | value[:, j] = interp_zoh(time, self.data['time'], self.data[key][:, j]) 107 | else: 108 | for j in range(self.data[key].shape[1]): 109 | value[:, j] = np.interp(time, self.data['time'], self.data[key][:, j]) 110 | else: 111 | raise Exception('Only 1D or 2D data allowed as boundary conditions') 112 | 113 | return value 114 | 115 | def __call__(self, time): 116 | """ 117 | Return the interpolated boundary conditions 118 | 119 | Parameters 120 | ---------- 121 | time : number or np.array 122 | true value for time 123 | 124 | Returns 125 | ------- 126 | dict 127 | Dictionary with interpolated boundary conditions. 128 | 129 | """ 130 | 131 | dst_int = {} 132 | for key in self.data: 133 | dst_int[key] = self.interp(key, time) 134 | 135 | return dst_int 136 | 137 | def __getitem__(self, key): 138 | return self.data[key] 139 | 140 | def has_key(self, key): 141 | return self.data.has_key(key) 142 | 143 | def __contains__(self, item): 144 | return item in self.data 145 | 146 | def __iter__(self): 147 | return self.data.__iter__() 148 | 149 | 150 | def interp_zoh(x, xp, fp): 151 | """ 152 | Interpolate with zero order hold 153 | 154 | Parameters 155 | ---------- 156 | x : np.array 157 | An array of independent variables where the values are required. 158 | 159 | xp : np.array 160 | An array of independent variables where the values are known, must be 161 | monotonic and increasing. 162 | 163 | fp : np.array 164 | The known values at points xp. 165 | 166 | Returns 167 | ------- 168 | np.array 169 | The interpolated values 170 | 171 | """ 172 | 173 | return np.array([fp[int((len(xp)-1)*(xi-xp[0])/(xp[-1]-xp[0]))] for xi in x]) 174 | -------------------------------------------------------------------------------- /mpcpy/emulator.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | ################################################################################ 3 | # Copyright 2015 Brecht Baeten 4 | # This file is part of mpcpy. 5 | # 6 | # mpcpy is free software: you can redistribute it and/or modify 7 | # it under the terms of the GNU General Public License as published by 8 | # the Free Software Foundation, either version 3 of the License, or 9 | # (at your option) any later version. 10 | # 11 | # mpcpy is distributed in the hope that it will be useful, 12 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 13 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14 | # GNU General Public License for more details. 15 | # 16 | # You should have received a copy of the GNU General Public License 17 | # along with mpcpy. If not, see . 18 | ################################################################################ 19 | 20 | import numpy as np 21 | 22 | 23 | class Emulator(object): 24 | """ 25 | Base class for defining an emulator object 26 | 27 | """ 28 | 29 | def __init__(self, input_keys, parameters=None, initial_conditions=None): 30 | """ 31 | Initializes the emulator object 32 | :code:`self.inputs` and :code:`self.res` attributes must be defined 33 | 34 | Parameters 35 | ---------- 36 | input_keys : list of strings 37 | list of strings of inputs. This is done so that not all data 38 | from the boundary conditions and control signals have to be 39 | transferred to the simulation. Only the boundary conditions and 40 | control signals in the :code:`inputs` attribute are interpolated and 41 | passed to the :code:`__call__` method. 42 | 43 | parameters : dict 44 | A dictionary of parameters used by the emulator. 45 | 46 | initial_conditions : dict 47 | A dictionary of initial conditions of the system under 48 | consideration. 49 | """ 50 | 51 | self.inputs = input_keys 52 | self.initial_conditions = {} 53 | if not initial_conditions is None: 54 | # set only the last value for each key 55 | for key in initial_conditions: 56 | try: 57 | self.initial_conditions[key] = initial_conditions[key][-1] 58 | except: 59 | self.initial_conditions[key] = initial_conditions[key] 60 | self.parameters = {} 61 | if not parameters is None: 62 | self.parameters = parameters 63 | self.res = {} 64 | 65 | def initialize(self): 66 | """ 67 | Redefine in a child class 68 | 69 | This method is called once before the start of the MPC and by default 70 | clears the results dictionary and then add the initial conditions to it 71 | at time 0. 72 | 73 | """ 74 | 75 | self.res = { 76 | 'time': np.array([0.]) 77 | } 78 | for key in self.initial_conditions: 79 | self.res[key] = np.array([self.initial_conditions[key]]) 80 | 81 | def simulate(self, starttime, stoptime, input): 82 | """ 83 | Redefine in a child class 84 | 85 | This method runs the simulation and should return a dictionary with 86 | results for times between the :code:`starttime` and :code:`stoptime` 87 | given the inputs. 88 | 89 | Parameters 90 | ---------- 91 | starttime : number 92 | time to start the simulation 93 | 94 | stoptime : number 95 | time to stop the simulation 96 | 97 | input : dict 98 | dictionary with values for the inputs for the simulation, 'time' 99 | and all values in self.inputs must be keys 100 | 101 | Returns 102 | ------- 103 | dict 104 | dictionary with the simulation results 105 | 106 | """ 107 | 108 | return {} 109 | 110 | def __call__(self, time, input): 111 | """ 112 | Simulates the system and updated the results dictionary, calls the 113 | :code:`simulate`method. 114 | 115 | Parameters 116 | ---------- 117 | time : numpy array 118 | Times at which the results are requested. 119 | 120 | input : dict 121 | Dictionary with values for the inputs of the model, time must be a 122 | part of it. 123 | 124 | Returns 125 | ------- 126 | dict 127 | Dictionary with the complete simulation results. 128 | 129 | Examples 130 | -------- 131 | >>> em = Emulator(['u1']) 132 | >>> t = np.arange(0., 3600.1, 600.) 133 | >>> u1 = 5.*np.ones_like(t) 134 | >>> em(t, ['time': t, 'u1': u1]) 135 | 136 | """ 137 | 138 | res = self.simulate(time[0], time[-1], input) 139 | 140 | # adding the inputs to the result 141 | for key in input.keys(): 142 | # make sure not to do double adding 143 | if not key in res.keys(): 144 | if key in self.res: 145 | # append the result 146 | if len(input[key]) == 1: 147 | self.res[key] = input[key] 148 | else: 149 | self.res[key] = np.append(self.res[key][:-1], np.interp(time, input['time'], input[key])) 150 | else: 151 | self.res[key] = np.interp(time, input['time'], input[key]) 152 | # interpolate results to the input points in time 153 | for key in res.keys(): 154 | if key in self.res: 155 | # append the result 156 | if len(res[key]) == 1: 157 | self.res[key] = res[key] 158 | else: 159 | if key == 'time': 160 | self.res[key] = np.append(self.res[key][:-1], time) 161 | else: 162 | self.res[key] = np.append(self.res[key][:-1], np.interp(time, res['time'], res[key])) 163 | 164 | else: 165 | if len(res[key]) == 1: 166 | self.res[key] = res[key] 167 | else: 168 | self.res[key] = np.interp(time, res['time'], res[key]) 169 | return self.res 170 | 171 | def set_initial_conditions(self, ini): 172 | print('Warning: Depreciated,' 173 | 'set the initial conditions during the object creation with the "initial_conditions" keyword parameter.') 174 | # set only the last value for each key 175 | for key in ini: 176 | try: 177 | self.initial_conditions[key] = ini[key][-1] 178 | except: 179 | self.initial_conditions[key] = ini[key] 180 | 181 | def set_parameters(self, par): 182 | print('Warning: Depreciated,' 183 | 'set the initial conditions during the object creation with the "parameters" keyword parameter.') 184 | self.parameters = par 185 | 186 | 187 | class DympyEmulator(Emulator): 188 | """ 189 | A class defining an emulator object using dympy for the simulation 190 | 191 | """ 192 | 193 | def __init__(self, dymola, inputs, initializationtime=1, **kwargs): 194 | """ 195 | Initialize a dympy object for use as an MPC emulation 196 | 197 | Parameters 198 | ---------- 199 | dymola : dympy.Dymola 200 | a dympy object with an opened and compiled dymola model 201 | 202 | inputs : list of strings 203 | a list of strings of the variable names of the inputs 204 | 205 | initializationtime : number 206 | time to run the initialization simulation 207 | 208 | **kwargs : 209 | arguments which can be passed on to dympy 210 | 211 | """ 212 | 213 | self.inputs = inputs 214 | self.initializationtime = initializationtime 215 | 216 | self.dymola = dymola 217 | self.initial_conditions = {} 218 | self.parameters = {} 219 | self.res = {} 220 | 221 | # check for additional dymola arguments 222 | self.simulation_args = {} 223 | for key in kwargs: 224 | if key in ['OutputInterval', 'NumberOfIntervals', 'Tolerance', 'FixedStepSize', 'Algorithm']: 225 | self.simulation_args[key] = kwargs[key] 226 | 227 | def initialize(self): 228 | """ 229 | initializes the dympy model, by simulating it for 230 | :code:`self.initializationtime`. Afterwards the :code:`res` attribute is 231 | populated 232 | 233 | Also, the keys in parameters and initialconditions are checked. If they 234 | do not appear in the dympy results, they are removed and the model is 235 | reinitialized. 236 | 237 | """ 238 | 239 | self.dymola.set_parameters(self.initial_conditions) 240 | self.dymola.set_parameters(self.parameters) 241 | 242 | # clear the result dict 243 | self.res = {} 244 | 245 | # simulate the model for a very short time to get the initial states in the res dict 246 | self.dymola.simulate(StartTime=0, StopTime=self.initializationtime) 247 | res = self.dymola.get_result() 248 | 249 | # remove the initial conditions and parameters which are not in the results file 250 | redosim = False 251 | keystoremove = [] 252 | for key in self.initial_conditions: 253 | if not key in res: 254 | keystoremove.append(key) 255 | redosim = True 256 | 257 | for key in keystoremove: 258 | del self.initial_conditions[key] 259 | 260 | keystoremove = [] 261 | for key in self.parameters: 262 | if not key in res: 263 | keystoremove.append(key) 264 | redosim = True 265 | 266 | for key in keystoremove: 267 | del self.parameters[key] 268 | 269 | # redo the simulation if required 270 | if redosim: 271 | self.dymola.set_parameters(self.initial_conditions) 272 | 273 | self.dymola.set_parameters(self.parameters) 274 | self.dymola.simulate(StartTime=0, StopTime=self.initializationtime) 275 | res = self.dymola.get_result() 276 | for key in res: 277 | self.res[key] = np.array([res[key][0]]) 278 | 279 | def simulate(self, starttime, stoptime, input): 280 | """ 281 | """ 282 | 283 | self.dymola.write_dsu(input) 284 | try: 285 | self.dymola.dsfinal2dsin() 286 | except: 287 | pass 288 | 289 | try: 290 | self.dymola.simulate(StartTime=starttime, StopTime=stoptime, **self.simulation_args) 291 | except: 292 | print('Ignoring error during simulation at time {}'.format(starttime)); 293 | 294 | try: 295 | res = self.dymola.get_result() 296 | except: 297 | print('Ignoring error while loading dymola res file at time {}'.format(input['time'][0])) 298 | 299 | return res 300 | 301 | 302 | def interp_averaged(t, tp, yp): 303 | y = np.zeros_like(t) 304 | for i in range(len(t)-1): 305 | y[i] = np.mean(yp[np.where((tp >= t[i]) & (tp < t[i+1]))]) 306 | 307 | y[-1] = np.interp(t[-1], tp, yp) 308 | 309 | return y 310 | -------------------------------------------------------------------------------- /mpcpy/mpc.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | ################################################################################ 3 | # Copyright 2015 Brecht Baeten 4 | # This file is part of mpcpy. 5 | # 6 | # mpcpy is free software: you can redistribute it and/or modify 7 | # it under the terms of the GNU General Public License as published by 8 | # the Free Software Foundation, either version 3 of the License, or 9 | # (at your option) any later version. 10 | # 11 | # mpcpy is distributed in the hope that it will be useful, 12 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 13 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14 | # GNU General Public License for more details. 15 | # 16 | # You should have received a copy of the GNU General Public License 17 | # along with mpcpy. If not, see . 18 | ################################################################################ 19 | 20 | import sys 21 | import numpy as np 22 | 23 | 24 | class MPC(object): 25 | 26 | def __init__(self, emulator, control, disturbances, 27 | emulationtime=7 * 24 * 3600, resulttimestep=600, nextstepcalculator=None, plotfunction=None): 28 | """ 29 | initialize an MPC object 30 | 31 | Parameters 32 | ---------- 33 | emulator : mpcpy.Emulator 34 | The emulator object to be used. 35 | 36 | control : mpcpy.Control 37 | The control object to be used. 38 | 39 | disturbances : mpcpy.Disturbances 40 | The disturbances object to be used. 41 | 42 | emulationtime : number 43 | The total time of the simulation. 44 | 45 | resulttimestep : number 46 | The timestep for which the results are returned. 47 | 48 | nextstepcalculator : function 49 | Function returning an integer representing the number of receding 50 | timesteps to skip. When not specified, no steps are skipped and the 51 | next step is 1. 52 | 53 | plotfunction : function 54 | A function which creates or updates a plot for live viewing of 55 | results, probably broken, untested. 56 | 57 | """ 58 | 59 | self.emulator = emulator 60 | self.control = control 61 | self.disturbances = disturbances 62 | 63 | self.emulationtime = emulationtime 64 | self.resulttimestep = resulttimestep 65 | 66 | if nextstepcalculator == None: 67 | def nextstepcalculator(controlsolution): 68 | return 1 69 | 70 | self.nextstepcalculator = nextstepcalculator 71 | 72 | self.plotfunction = plotfunction 73 | 74 | self.res = {} 75 | self.appendres = {} 76 | 77 | def __call__(self, verbose=0): 78 | """ 79 | Runs the mpc simulation 80 | 81 | Parameters 82 | ---------- 83 | verbose: optional, int 84 | Controls the amount of print output 85 | 86 | Returns 87 | ------- 88 | dict 89 | A dictionary with results, also stored in the res attribute. 90 | 91 | """ 92 | 93 | # initialize the emulator 94 | self.emulator.initialize() 95 | starttime = 0 96 | 97 | if self.plotfunction: 98 | (fig,ax,pl) = self.plotfunction() 99 | 100 | # prepare a progress bar 101 | barwidth = 80-2 102 | barvalue = 0 103 | if verbose > 0: 104 | print('Running MPC') 105 | print('[' + (' '*barwidth) + ']', end='') 106 | 107 | while starttime < self.emulationtime: 108 | 109 | # calculate control signals for the control horizon 110 | control = self.control(starttime) 111 | 112 | # create a simulation time vector 113 | nextStep = self.nextstepcalculator(control) 114 | time = np.arange( 115 | starttime, 116 | min(self.emulationtime+self.resulttimestep, starttime+nextStep*self.control.receding+0.01*self.resulttimestep), 117 | self.resulttimestep, dtype=np.float 118 | ) 119 | time[-1] = min(time[-1], self.emulationtime) 120 | 121 | # create input of all controls and the required boundary conditions 122 | # add times at the control time steps minus 1e-6 times the result time step to achieve zero order hold 123 | ind = np.where( 124 | (control['time']-1e-6*self.resulttimestep > time[0]) 125 | & (control['time']-1e-6*self.resulttimestep <= time[-1]) 126 | ) 127 | inputtime = np.sort(np.concatenate((time, control['time'][ind]-1e-6*self.resulttimestep))) 128 | input = {'time': inputtime} 129 | 130 | # add controls first 131 | for key in control: 132 | if not key in input: 133 | input[key] = interp_zoh(input['time'], control['time'], control[key]) 134 | 135 | # add the rest of the inputs from the boundary conditions 136 | for key in self.emulator.inputs: 137 | if not key in input and key in self.disturbances: 138 | input[key] = self.disturbances.interp(key, input['time']) 139 | elif not key in input: 140 | print('Warning {} not found in disturbances object'.format(key)) 141 | 142 | # prepare and run the simulation 143 | self.emulator(time, input) 144 | 145 | # plot results 146 | if self.plotfunction: 147 | self.plotfunction(pl=pl, res=self.emulator.res) 148 | 149 | # update starting time 150 | starttime = self.emulator.res['time'][-1] 151 | 152 | # update the progress bar 153 | if verbose > 0: 154 | if starttime/self.emulationtime*barwidth >= barvalue: 155 | barvalue += int(round(starttime/self.emulationtime*barwidth-barvalue)) 156 | print('\r[' + ('='*barvalue) + (' '*(barwidth-barvalue)) + ']', end='') 157 | 158 | # copy the results to a local res dictionary 159 | self.res.update(self.emulator.res) 160 | 161 | # interpolate the boundary conditions and add them to self.res 162 | self.res.update(self.disturbances(self.res['time'])) 163 | if verbose > 0: 164 | print(' done') 165 | 166 | return self.res 167 | 168 | 169 | def interp_zoh(x, xp, fp): 170 | return np.array([fp[int((len(xp)-1)*(xi-xp[0])/(xp[-1]-xp[0]))] for xi in x]) 171 | -------------------------------------------------------------------------------- /mpcpy/prediction.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | ################################################################################ 3 | # Copyright 2015 Brecht Baeten 4 | # This file is part of mpcpy. 5 | # 6 | # mpcpy is free software: you can redistribute it and/or modify 7 | # it under the terms of the GNU General Public License as published by 8 | # the Free Software Foundation, either version 3 of the License, or 9 | # (at your option) any later version. 10 | # 11 | # mpcpy is distributed in the hope that it will be useful, 12 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 13 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14 | # GNU General Public License for more details. 15 | # 16 | # You should have received a copy of the GNU General Public License 17 | # along with mpcpy. If not, see . 18 | ################################################################################ 19 | 20 | import sys 21 | import numpy as np 22 | 23 | class Prediction(object): 24 | """ 25 | Base class for defining the predictions for an mpc the "prediction" method 26 | can be redefined in a child class. If not it returns a perfect prediction of 27 | all future disturbances. 28 | """ 29 | 30 | def __init__(self,boundaryconditions,parameters=None): 31 | """ 32 | 33 | Parameters 34 | ---------- 35 | boundaryconditions: mpcpy.Disturbances 36 | An :code:`mpcpy.Boundaryconditions` object to derive the predictions from. 37 | 38 | parameters : dict 39 | A dictionary of parameters used by the prediction algorithm. 40 | """ 41 | 42 | self.boundaryconditions = boundaryconditions 43 | 44 | self.parameters = {} 45 | if not parameters is None: 46 | self.parameters = parameters 47 | 48 | def prediction(self,time): 49 | """ 50 | Defines perfect predictions, returns the exact boundary conditions dict 51 | Can be redefined in a child class to contain an actual prediction 52 | algorithm. 53 | 54 | Parameters 55 | ---------- 56 | time : np.array 57 | The times at which the predictions are required 58 | 59 | Returns 60 | ------- 61 | dict 62 | A dictionary with the predictions at the specified times 63 | 64 | """ 65 | return self.boundaryconditions(time) 66 | 67 | 68 | def __call__(self,time): 69 | return self.prediction(time) -------------------------------------------------------------------------------- /mpcpy/stateestimation.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | ################################################################################ 3 | # Copyright 2015 Brecht Baeten 4 | # This file is part of mpcpy. 5 | # 6 | # mpcpy is free software: you can redistribute it and/or modify 7 | # it under the terms of the GNU General Public License as published by 8 | # the Free Software Foundation, either version 3 of the License, or 9 | # (at your option) any later version. 10 | # 11 | # mpcpy is distributed in the hope that it will be useful, 12 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 13 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14 | # GNU General Public License for more details. 15 | # 16 | # You should have received a copy of the GNU General Public License 17 | # along with mpcpy. If not, see . 18 | ################################################################################ 19 | 20 | import sys 21 | import numpy as np 22 | 23 | class Stateestimation(object): 24 | """ 25 | Base class for defining the state estimation for an mpc 26 | the "stateestimation" method must be redefined in a child class. 27 | 28 | """ 29 | 30 | def __init__(self,emulator,parameters=None): 31 | """ 32 | Parameters 33 | ---------- 34 | emulator : mpcpy.Emulator 35 | An :code:`mpcpy.Emulator` object from which the state should be estimated. 36 | 37 | parameters : dict, optional 38 | Optional parameter dictionary. 39 | 40 | """ 41 | 42 | self.emulator = emulator 43 | self.parameters = parameters 44 | 45 | 46 | def stateestimation(self,time): 47 | """ 48 | Must be redefined in a child class to return a dictionary with the 49 | estimated state at the specified time as required by the control object. 50 | 51 | Parameters 52 | ---------- 53 | time : number 54 | The time at which the state should be estimated. 55 | 56 | Returns 57 | ------- 58 | dict 59 | A dictionary with key-value pairs representing the state. 60 | 61 | """ 62 | 63 | return None 64 | 65 | 66 | def __call__(self,time): 67 | """ 68 | Returns the state at a given time as computed by the 69 | :code:`stateestimation` method. 70 | 71 | Parameters 72 | ---------- 73 | time : number 74 | The time at which the state should be estimated. 75 | 76 | Returns 77 | ------- 78 | dict 79 | A dictionary with key-value pairs representing the state. 80 | 81 | """ 82 | return self.stateestimation(time) -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | from setuptools import setup, find_packages 3 | import os 4 | 5 | setuppath = os.path.dirname(os.path.abspath(__file__)) 6 | 7 | # retrieve the version 8 | try: 9 | versionfile = os.path.join(setuppath, 'mpcpy', '__version__.py') 10 | f = open(versionfile, 'r') 11 | content = f.readline() 12 | splitcontent = content.split('\'') 13 | version = splitcontent[1] 14 | f.close() 15 | except: 16 | raise Exception('Could not determine the version from mpcpy/__version__.py') 17 | 18 | 19 | # run the setup command 20 | setup( 21 | name='mpcpy', 22 | version=version, 23 | license='GPLv3', 24 | description='A package to run MPC, moving horizon simulations in Python.', 25 | long_description=open(os.path.join(setuppath, 'README.rst')).read(), 26 | url='https://github.com/BrechtBa/mpcpy', 27 | author='Brecht Baeten', 28 | author_email='brecht.baeten@gmail.com', 29 | packages=find_packages(), 30 | install_requires=['numpy'], 31 | extras_require={ 32 | 'dev': [ 33 | 'pyomo', 34 | 'matplotlib' 35 | ] 36 | }, 37 | classifiers=['Programming Language :: Python :: 2.7'], 38 | ) -------------------------------------------------------------------------------- /tests/.gitignore: -------------------------------------------------------------------------------- 1 | dsin.txt -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BrechtBa/mpcpy/108b7a19bc07f17b1fae30ee8ecfdd1e732c1c47/tests/__init__.py -------------------------------------------------------------------------------- /tests/all.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | ################################################################################ 3 | # Copyright 2015 Brecht Baeten 4 | # This file is part of mpcpy. 5 | # 6 | # mpcpy is free software: you can redistribute it and/or modify 7 | # it under the terms of the GNU General Public License as published by 8 | # the Free Software Foundation, either version 3 of the License, or 9 | # (at your option) any later version. 10 | # 11 | # mpcpy is distributed in the hope that it will be useful, 12 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 13 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14 | # GNU General Public License for more details. 15 | # 16 | # You should have received a copy of the GNU General Public License 17 | # along with mpcpy. If not, see . 18 | ################################################################################ 19 | 20 | import unittest 21 | 22 | from .boundaryconditions import * 23 | from .prediction import * 24 | from .stateestimation import * 25 | from .emulator import * 26 | from .examples import * 27 | 28 | if __name__ == '__main__': 29 | unittest.main() 30 | -------------------------------------------------------------------------------- /tests/boundaryconditions.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | ################################################################################ 3 | # Copyright 2015 Brecht Baeten 4 | # This file is part of mpcpy. 5 | # 6 | # mpcpy is free software: you can redistribute it and/or modify 7 | # it under the terms of the GNU General Public License as published by 8 | # the Free Software Foundation, either version 3 of the License, or 9 | # (at your option) any later version. 10 | # 11 | # mpcpy is distributed in the hope that it will be useful, 12 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 13 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14 | # GNU General Public License for more details. 15 | # 16 | # You should have received a copy of the GNU General Public License 17 | # along with mpcpy. If not, see . 18 | ################################################################################ 19 | 20 | import unittest 21 | import mpcpy 22 | import numpy as np 23 | 24 | 25 | # define variables 26 | time = np.arange(0.0,7*24*3600.+1.,900.) 27 | y0 = np.sin(time/(24*3600.)) 28 | y1 = np.random.random(len(time)) 29 | bcs = {'time':time, 'y0':y0, 'y1':y1} 30 | 31 | class TestBoundaryconditions(unittest.TestCase): 32 | 33 | def test_create(self): 34 | boundaryconditions = mpcpy.Disturbances(bcs) 35 | 36 | def test_create_periodic(self): 37 | boundaryconditions = mpcpy.Disturbances(bcs, periodic=True) 38 | boundaryconditions = mpcpy.Disturbances(bcs, periodic=False) 39 | 40 | def test_create_extratime(self): 41 | boundaryconditions = mpcpy.Disturbances(bcs, extra_time=1 * 24 * 3600) 42 | boundaryconditions = mpcpy.Disturbances(bcs, periodic=False, extra_time=1 * 24 * 3600) 43 | 44 | def test_getitem(self): 45 | boundaryconditions = mpcpy.Disturbances(bcs) 46 | temp = boundaryconditions['y0'] 47 | 48 | def test_value(self): 49 | boundaryconditions = mpcpy.Disturbances(bcs) 50 | 51 | t0 = 1*24*3600. 52 | val = boundaryconditions(t0) 53 | 54 | self.assertEqual(val,{'time':t0,'y0':np.interp(t0,time,y0),'y1':np.interp(t0,time,y1)}) 55 | 56 | def test_value_periodic(self): 57 | boundaryconditions = mpcpy.Disturbances(bcs) 58 | 59 | t0 = 1*24*3600. 60 | t1 = t0 + time[-1] 61 | 62 | val0 = boundaryconditions(t0) 63 | val1 = boundaryconditions(t1) 64 | 65 | self.assertEqual(val0['y0'],val1['y0']) 66 | self.assertEqual(val0['y1'],val1['y1']) 67 | 68 | def test_value_periodic_timeoffset(self): 69 | bcs_timeoffset = dict(bcs) 70 | bcs_timeoffset['time'] = bcs_timeoffset['time'] + 2*24*3600. 71 | boundaryconditions = mpcpy.Disturbances(bcs_timeoffset) 72 | 73 | t0 = 3*24*3600. 74 | t1 = t0 + (time[-1]-time[0]) 75 | 76 | val0 = boundaryconditions(t0) 77 | val1 = boundaryconditions(t1) 78 | 79 | self.assertEqual(val0['y0'],val1['y0']) 80 | self.assertEqual(val0['y1'],val1['y1']) 81 | 82 | def test_value_notperiodic(self): 83 | boundaryconditions = mpcpy.Disturbances(bcs, periodic=False) 84 | 85 | t0 = 1*24*3600. 86 | t1 = t0 + time[-1] 87 | 88 | val0 = boundaryconditions(time[-1]) 89 | val1 = boundaryconditions(t1) 90 | 91 | self.assertEqual(val0['y0'],val1['y0']) 92 | self.assertEqual(val0['y1'],val1['y1']) 93 | 94 | 95 | 96 | if __name__ == '__main__': 97 | unittest.main() -------------------------------------------------------------------------------- /tests/data/example.mo: -------------------------------------------------------------------------------- 1 | within ; 2 | model example 3 | Modelica.Thermal.HeatTransfer.Components.HeatCapacitor C_em(C=1) annotation ( 4 | Placement(transformation( 5 | extent={{-10,-10},{10,10}}, 6 | rotation=180, 7 | origin={-30,-26}))); 8 | Modelica.Thermal.HeatTransfer.Components.HeatCapacitor C_in(C=1) annotation ( 9 | Placement(transformation( 10 | extent={{-10,-10},{10,10}}, 11 | rotation=180, 12 | origin={10,-26}))); 13 | Modelica.Thermal.HeatTransfer.Components.ThermalConductor UA_em_in(G=1) 14 | annotation (Placement(transformation(extent={{-20,-10},{0,10}}))); 15 | Modelica.Thermal.HeatTransfer.Components.ThermalConductor UA_in_amb(G=1) 16 | annotation (Placement(transformation(extent={{20,-10},{40,10}}))); 17 | Modelica.Thermal.HeatTransfer.Sources.PrescribedTemperature 18 | prescribedTemperature_amb annotation (Placement(transformation( 19 | extent={{-10,-10},{10,10}}, 20 | rotation=180, 21 | origin={70,0}))); 22 | Modelica.Thermal.HeatTransfer.Sources.PrescribedHeatFlow 23 | prescribedHeatFlow_hp annotation (Placement(transformation( 24 | extent={{-10,-10},{10,10}}, 25 | rotation=270, 26 | origin={-58,30}))); 27 | Modelica.Blocks.Interfaces.RealInput Q_flow_hp 28 | annotation (Placement(transformation(extent={{-120,30},{-80,70}}))); 29 | Modelica.Blocks.Interfaces.RealInput T_amb annotation (Placement( 30 | transformation( 31 | extent={{-20,-20},{20,20}}, 32 | rotation=270, 33 | origin={50,100}))); 34 | Modelica.Blocks.Interfaces.RealInput Q_flow_sol annotation (Placement( 35 | transformation( 36 | extent={{-20,-20},{20,20}}, 37 | rotation=270, 38 | origin={10,100}))); 39 | Modelica.Thermal.HeatTransfer.Sources.PrescribedHeatFlow 40 | prescribedHeatFlow_sol annotation (Placement(transformation( 41 | extent={{-10,-10},{10,10}}, 42 | rotation=270, 43 | origin={10,30}))); 44 | equation 45 | connect(UA_in_amb.port_b, prescribedTemperature_amb.port) 46 | annotation (Line(points={{40,0},{60,0}}, color={191,0,0})); 47 | connect(UA_in_amb.port_a, C_in.port) 48 | annotation (Line(points={{20,0},{10,0},{10,-16}}, color={191,0,0})); 49 | connect(UA_em_in.port_b, C_in.port) 50 | annotation (Line(points={{0,0},{10,0},{10,-16}}, color={191,0,0})); 51 | connect(UA_em_in.port_a, C_em.port) 52 | annotation (Line(points={{-20,0},{-30,0},{-30,-16}}, color={191,0,0})); 53 | connect(prescribedHeatFlow_hp.port, C_em.port) annotation (Line(points={{-58, 54 | 20},{-58,0},{-30,0},{-30,-16}}, color={191,0,0})); 55 | connect(prescribedHeatFlow_sol.port, C_in.port) 56 | annotation (Line(points={{10,20},{10,-16}}, color={191,0,0})); 57 | connect(Q_flow_hp, prescribedHeatFlow_hp.Q_flow) 58 | annotation (Line(points={{-100,50},{-58,50},{-58,40}}, color={0,0,127})); 59 | connect(Q_flow_sol, prescribedHeatFlow_sol.Q_flow) annotation (Line(points={{ 60 | 10,100},{10,100},{10,40},{10,40}}, color={0,0,127})); 61 | connect(T_amb, prescribedTemperature_amb.T) annotation (Line(points={{50,100}, 62 | {50,100},{50,46},{88,46},{88,0},{82,0}}, color={0,0,127})); 63 | annotation (uses(Modelica(version="3.2.1")), Diagram(coordinateSystem( 64 | preserveAspectRatio=false, extent={{-100,-100},{100,100}}))); 65 | end example; 66 | -------------------------------------------------------------------------------- /tests/emulator.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | ################################################################################ 3 | # Copyright 2015 Brecht Baeten 4 | # This file is part of mpcpy. 5 | # 6 | # mpcpy is free software: you can redistribute it and/or modify 7 | # it under the terms of the GNU General Public License as published by 8 | # the Free Software Foundation, either version 3 of the License, or 9 | # (at your option) any later version. 10 | # 11 | # mpcpy is distributed in the hope that it will be useful, 12 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 13 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14 | # GNU General Public License for more details. 15 | # 16 | # You should have received a copy of the GNU General Public License 17 | # along with mpcpy. If not, see . 18 | ################################################################################ 19 | 20 | import unittest 21 | import mpcpy 22 | import numpy as np 23 | import sys 24 | import os 25 | 26 | 27 | # current path 28 | modulepath = os.path.abspath(os.path.dirname(sys.modules[__name__].__file__)) 29 | 30 | 31 | class TestEmulator(unittest.TestCase): 32 | def setUp(self): 33 | 34 | self.ini = {'C_em.T': 22+273.15, 'C_in.T': 21+273.15} 35 | self.par = {'C_em.C': 10e6, 36 | 'C_in.C': 5e6, 37 | 'UA_in_amb.G': 200, 38 | 'UA_em_in.G': 1600} 39 | self.inp = { 40 | 'time': np.array([0. , 3600. , 7200.]), 41 | 'T_amb': np.array([273.15, 274.15, 275.15]), 42 | 'Q_flow_sol': np.array([500. , 400. , 300.]), 43 | 'Q_flow_hp': np.array([4000. , 4000. , 4000.]) 44 | } 45 | 46 | def test_create(self): 47 | emulator = mpcpy.Emulator([]) 48 | 49 | 50 | def test_call(self): 51 | emulator = mpcpy.Emulator([]) 52 | emulator(self.inp['time'],self.inp) 53 | 54 | self.assertEqual(emulator.res['time'][1],self.inp['time'][1]) 55 | self.assertEqual(emulator.res['Q_flow_sol'][2],self.inp['Q_flow_sol'][2]) 56 | 57 | if __name__ == '__main__': 58 | unittest.main() -------------------------------------------------------------------------------- /tests/examples.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | ################################################################################ 3 | # Copyright 2015 Brecht Baeten 4 | # This file is part of mpcpy. 5 | # 6 | # mpcpy is free software: you can redistribute it and/or modify 7 | # it under the terms of the GNU General Public License as published by 8 | # the Free Software Foundation, either version 3 of the License, or 9 | # (at your option) any later version. 10 | # 11 | # mpcpy is distributed in the hope that it will be useful, 12 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 13 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14 | # GNU General Public License for more details. 15 | # 16 | # You should have received a copy of the GNU General Public License 17 | # along with mpcpy. If not, see . 18 | ################################################################################ 19 | 20 | 21 | import unittest 22 | 23 | 24 | class TestExamples(unittest.TestCase): 25 | 26 | def test_simple_space_heating_mpc(self): 27 | from examples import simple_space_heating_mpc 28 | 29 | def test_quickstart(self): 30 | from examples import quickstart 31 | 32 | 33 | if __name__ == '__main__': 34 | unittest.main() -------------------------------------------------------------------------------- /tests/prediction.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | ################################################################################ 3 | # Copyright 2015 Brecht Baeten 4 | # This file is part of mpcpy. 5 | # 6 | # mpcpy is free software: you can redistribute it and/or modify 7 | # it under the terms of the GNU General Public License as published by 8 | # the Free Software Foundation, either version 3 of the License, or 9 | # (at your option) any later version. 10 | # 11 | # mpcpy is distributed in the hope that it will be useful, 12 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 13 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14 | # GNU General Public License for more details. 15 | # 16 | # You should have received a copy of the GNU General Public License 17 | # along with mpcpy. If not, see . 18 | ################################################################################ 19 | 20 | import unittest 21 | import mpcpy 22 | import numpy as np 23 | 24 | 25 | # define variables 26 | time = np.arange(0.0,7*24*3600.+1.,900.) 27 | y0 = np.sin(time/(24*3600.)) 28 | y1 = np.random.random(len(time)) 29 | bcs = {'time':time, 'y0':y0, 'y1':y1} 30 | 31 | boundaryconditions = mpcpy.Disturbances(bcs) 32 | 33 | 34 | class TestPrediction(unittest.TestCase): 35 | 36 | def test_create(self): 37 | prediction = mpcpy.Prediction(boundaryconditions) 38 | 39 | def test_value(self): 40 | prediction = mpcpy.Prediction(boundaryconditions) 41 | 42 | t0 = 1*24*3600. 43 | 44 | self.assertEqual(prediction(t0),boundaryconditions(t0)) 45 | 46 | 47 | 48 | if __name__ == '__main__': 49 | unittest.main() -------------------------------------------------------------------------------- /tests/stateestimation.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | ################################################################################ 3 | # Copyright 2015 Brecht Baeten 4 | # This file is part of mpcpy. 5 | # 6 | # mpcpy is free software: you can redistribute it and/or modify 7 | # it under the terms of the GNU General Public License as published by 8 | # the Free Software Foundation, either version 3 of the License, or 9 | # (at your option) any later version. 10 | # 11 | # mpcpy is distributed in the hope that it will be useful, 12 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 13 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14 | # GNU General Public License for more details. 15 | # 16 | # You should have received a copy of the GNU General Public License 17 | # along with mpcpy. If not, see . 18 | ################################################################################ 19 | 20 | import unittest 21 | import mpcpy 22 | import numpy as np 23 | 24 | # define variables 25 | val = 123 26 | 27 | class TestStateestimation(unittest.TestCase): 28 | 29 | def test_create(self): 30 | class Stateestimation(mpcpy.Stateestimation): 31 | def stateestimation(self,time): 32 | return val 33 | 34 | stateestimation = Stateestimation(None) 35 | 36 | def test_value(self): 37 | class Stateestimation(mpcpy.Stateestimation): 38 | def stateestimation(self,time): 39 | return val 40 | 41 | stateestimation = Stateestimation(None) 42 | 43 | self.assertEqual(stateestimation(0),val) 44 | 45 | 46 | 47 | if __name__ == '__main__': 48 | unittest.main() --------------------------------------------------------------------------------