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ampyc -- Advanced Model Predictive Control in Python
General Python package for control theory research, including some reference implementations of various advanced model predictive control (MPC) algorithms.
Features:
- Implements dynamical systems and control interfaces to allow seamless interactions
- Provides abstract base classes to allow custom implementation of any type of dynamical system and controller
- Reference implementations of many advanced MPC algorithms; for a full list of implemented algorithms see below
- Global parameter management for easy experiment setup and management
- Various utility tools for set computations, polytope manipulation, and plotting
- Lecture-style notes and notebook tutorials explaining advanced predictive control concepts
ampyc requires Python 3.10 or higher. Just use pip for Python 3 to install ampyc and its dependencies:
python3 -m pip install ampyc
- Clone this repository using
git clone git@github.com:IntelligentControlSystems/ampyc.git
- Install all dependencies (preferably in a virtual environment) using
python3 -m pip install -r requirements.txt
- Install
ampycin editable mode for development. Navigate to this top-level folder and run
pip install -e .
To get started with the ampyc package, run the tutorial notebook, which provides an introduction to all parts of the package.
For specific control algorithms implemented in ampyc, run the associated notebook in the notebook folder.
| Year | Authors | Method/Paper | AMPyC |
|---|---|---|---|
| - | - | Linear Model Predictive Control | code |
| - | - | Nonlinear Model Predictive Control | code |
| 2001 | Chisci et al. | Systems with persistent disturbances: predictive control with restricted constraints | code |
| 2005 | Mayne et al. | Robust model predictive control of constrained linear systems with bounded disturbances | code |
| 2013 | Bayer et al. | Discrete-time incremental ISS: A framework for robust NMPC | code |
| 2016 | Kouvaritakis & Cannon | Stochastic constraint-tightening Model Predictive Control | code |
| 2018 | Hewing & Zeilinger | Stochastic Model Predictive Control for Linear Systems Using Probabilistic Reachable Sets | code |
| 2020 | Hewing et al. | Recursively feasible stochastic model predictive control using indirect feedback | code |
If you find this package/repository helpful, please cite our work:
@software{ampyc,
title = {AMPyC: Advanced Model Predictive Control in Python},
author = {Sieber, Jerome and Didier, Alexandre and Rickenbach, Rahel and Zeilinger, Melanie},
url = {https://github.com/IntelligentControlSystems/ampyc},
month = jun,
year = {2025}
}
