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MPC documentation and citation
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.. _mpc_api: | ||
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API Reference | ||
============= | ||
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.. toctree:: | ||
data.rst | ||
conversion.rst | ||
interface.rst | ||
modeling.rst |
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Data Conversion | ||
=============== | ||
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.. automodule:: pyomo.contrib.mpc.data.convert | ||
:members: |
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Data Structures | ||
=============== | ||
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.. automodule:: pyomo.contrib.mpc.data.get_cuid | ||
:members: | ||
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.. automodule:: pyomo.contrib.mpc.data.dynamic_data_base | ||
:members: | ||
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.. automodule:: pyomo.contrib.mpc.data.scalar_data | ||
:members: | ||
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.. automodule:: pyomo.contrib.mpc.data.series_data | ||
:members: | ||
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.. automodule:: pyomo.contrib.mpc.data.interval_data | ||
:members: |
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Interfaces | ||
========== | ||
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.. automodule:: pyomo.contrib.mpc.interfaces.model_interface | ||
:members: | ||
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.. automodule:: pyomo.contrib.mpc.interfaces.var_linker | ||
:members: |
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Modeling Components | ||
=================== | ||
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.. automodule:: pyomo.contrib.mpc.modeling.constraints | ||
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.. automodule:: pyomo.contrib.mpc.modeling.cost_expressions | ||
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.. automodule:: pyomo.contrib.mpc.modeling.terminal | ||
:members: |
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# Pyomo MPC | ||
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Pyomo MPC is an extension for developing model predictive control simulations | ||
using Pyomo models. Please see the | ||
[documentation](https://pyomo.readthedocs.io/en/stable/contributed_packages/mpc/index.html) | ||
for more detailed information. | ||
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Pyomo MPC helps with, among other things, the following use cases: | ||
- Transferring values between different points in time in a dynamic model | ||
(e.g. to initialize a dynamic model to its initial conditions) | ||
- Extracting or loading disturbances and inputs from or to models, and storing | ||
these in model-agnostic, easily JSON-serializable data structures | ||
- Constructing common modeling components, such as weighted-least-squares | ||
tracking objective functions, piecewise-constant input constraints, or | ||
terminal region constraints. | ||
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## Citation | ||
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If you use Pyomo MPC in your research, please cite the following paper, which | ||
discusses the motivation for the Pyomo MPC data structures and the underlying | ||
Pyomo features that make them possible. | ||
```bibtex | ||
@article{parker2023mpc, | ||
title = {Model predictive control simulations with block-hierarchical differential-algebraic process models}, | ||
journal = {Journal of Process Control}, | ||
volume = {132}, | ||
pages = {103113}, | ||
year = {2023}, | ||
issn = {0959-1524}, | ||
doi = {https://doi.org/10.1016/j.jprocont.2023.103113}, | ||
url = {https://www.sciencedirect.com/science/article/pii/S0959152423002007}, | ||
author = {Robert B. Parker and Bethany L. Nicholson and John D. Siirola and Lorenz T. Biegler}, | ||
} | ||
``` |
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