HILO-MPC
========
[](https://www.python.org/downloads/)
[](https://hilo-mpc.github.io/hilo-mpc/)
[](https://github.com/hilo-mpc/hilo-mpc/actions?query=workflow%3ATests)
<!--[](https://codecov.io/gh/hilo-mpc/hilo-mpc)-->
[](https://lgtm.com/projects/g/hilo-mpc/hilo-mpc/context:python)
[](https://doi.org/10.48550/arXiv.2203.13671)
[](https://github.com/hilo-mpc/hilo-mpc/blob/master/LICENSE)
[](https://GitHub.com/hilo-mpc/hilo-mpc/releases/)
HILO-MPC is a Python toolbox for **easy, flexible and fast realization of machine-learning-supported optimal control, and
estimation problems** developed mainly at the [Control and Cyber-Physical Systems Laboratory, TU Darmstadt](https://www.ccps.tu-darmstadt.de), and the [Laboratory for Systems Theory and Control, Otto von Guericke University](http://ifatwww.et.uni-magdeburg.de/syst/). It can be used for model predictive control, moving horizon estimation, Kalman filters, solving optimal control problems and has interfaces to embedded model predictive control tools.
HILO-MPC can interface directly to [TensorFlow](https://www.tensorflow.org/) and [PyTorch](https://pytorch.org/)
to create machine learning models and the [CasADi](https://web.casadi.org/) framework to efficiently
build control and estimation problems. The machine learning models can be used (almost) everywhere in the setup
of these problems.
Currently the following machine learning models are supported:
- Feedforward neural networks
- Gaussian processes
The following machine learning models are currently under development:
- Bayesian neural network
- Recurrent neural network
At the moment the following MPC and optimal control problems can be solved:
- Reference tracking nonlinear MPC
- Trajectory tracking nonlinear MPC
- Path following nonlinear MPC
- Economic nonlinear MPC
- Linear MPC
- Traditional optimal control problems
All the nonlinear MPCs support soft constraints, time-variant systems, time-varying parameters and can be used to solve
minimum-time problems. They work for continuous-time and discrete-time models, in DAE or ODE form. Linear MPC is currently limited towards discrete-time models.
A rich set of [examples](https://github.com/hilo-mpc/examples) is available, spanning:
- NMPC for bioreactors using hybrid first principle and learned models
- Trajectory tracking and path following model predictive control with learning and obstacle avoidance
- Output feedback MPC of a continuous stirred tank reactor with a Gaussian process prediction model
- Learning NMPC control using a neural network
- Simple LQR, PID
- Moving horizon estimation, extended Kalman filter, unscented Kalman filter, and particle filter for a continuous stirred tank reactor
Installation
------------
You can use pip to install HILO-MPC as follows
```shell
pip install hilo-mpc
```
Additional Packages
-------------------
If you want to make use of the complete functionality of the toolbox, you may want to install one of the following
packages
| Package | Version | Usage |
|--------------------------------------------------|------------------|------------------------------------------------|
| [TensorFlow](https://www.tensorflow.org) | \>=2.3.0, <2.8.0 | Training of neural networks |
| [PyTorch](https://pytorch.org) | \>=1.2.0 | Training of neural networks |
| [scikit-learn](https://scikit-learn.org/stable/) | \>=0.19.2 | Normalizing of training data |
| [Bokeh](https://bokeh.org) | \>=2.3.0 | Plotting |
| [Matplotlib](https://matplotlib.org) | \>=3.0.0 | Plotting |
| [pandas](https://pandas.pydata.org) | \>=1.0.0 | Providing data for training of neural networks |
Documentation
-------------
A preliminary documentation can be found [here](https://hilo-mpc.github.io/hilo-mpc/). Note that this documentation is
not complete and will be updated over time.
Citing HILO-MPC
---------------
If you use HILO-MPC for your research, please cite the following publication:
* J. Pohlodek, B. Morabito, C. Schlauch, P. Zometa, R. Findeisen. **[Flexible development and evaluation of
machine-learning-supported optimal control and estimation methods via HILO-MPC](https://arxiv.org/abs/2203.13671)**.
arXiv. 2022.
```
@misc{pohlodek2022hilompc,
title = {Flexible development and evaluation of machine-learning-supported optimal control and estimation methods via {HILO-MPC}},
author = {Pohlodek, Johannes and Morabito, Bruno and Schlauch, Christian and Zometa, Pablo and Findeisen, Rolf},
publisher = {arXiv},
year = {2022},
doi = {10.48550/ARXIV.2203.13671}
}
```
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"description": "HILO-MPC \n========\n\n[](https://www.python.org/downloads/)\n[](https://hilo-mpc.github.io/hilo-mpc/)\n[](https://github.com/hilo-mpc/hilo-mpc/actions?query=workflow%3ATests)\n<!--[](https://codecov.io/gh/hilo-mpc/hilo-mpc)-->\n[](https://lgtm.com/projects/g/hilo-mpc/hilo-mpc/context:python)\n[](https://doi.org/10.48550/arXiv.2203.13671)\n[](https://github.com/hilo-mpc/hilo-mpc/blob/master/LICENSE)\n[](https://GitHub.com/hilo-mpc/hilo-mpc/releases/)\n\nHILO-MPC is a Python toolbox for **easy, flexible and fast realization of machine-learning-supported optimal control, and \nestimation problems** developed mainly at the [Control and Cyber-Physical Systems Laboratory, TU Darmstadt](https://www.ccps.tu-darmstadt.de), and the [Laboratory for Systems Theory and Control, Otto von Guericke University](http://ifatwww.et.uni-magdeburg.de/syst/). It can be used for model predictive control, moving horizon estimation, Kalman filters, solving optimal control problems and has interfaces to embedded model predictive control tools.\n\nHILO-MPC can interface directly to [TensorFlow](https://www.tensorflow.org/) and [PyTorch](https://pytorch.org/)\nto create machine learning models and the [CasADi](https://web.casadi.org/) framework to efficiently\nbuild control and estimation problems. The machine learning models can be used (almost) everywhere in the setup\nof these problems. \n\n\n\nCurrently the following machine learning models are supported:\n\n- Feedforward neural networks\n- Gaussian processes\n \nThe following machine learning models are currently under development:\n \n- Bayesian neural network\n- Recurrent neural network\n\nAt the moment the following MPC and optimal control problems can be solved:\n\n- Reference tracking nonlinear MPC\n- Trajectory tracking nonlinear MPC\n- Path following nonlinear MPC\n- Economic nonlinear MPC\n- Linear MPC\n- Traditional optimal control problems\n\nAll the nonlinear MPCs support soft constraints, time-variant systems, time-varying parameters and can be used to solve \nminimum-time problems. They work for continuous-time and discrete-time models, in DAE or ODE form. Linear MPC is currently limited towards discrete-time models. \n\nA rich set of [examples](https://github.com/hilo-mpc/examples) is available, spanning:\n- NMPC for bioreactors using hybrid first principle and learned models\n- Trajectory tracking and path following model predictive control with learning and obstacle avoidance\n- Output feedback MPC of a continuous stirred tank reactor with a Gaussian process prediction model\n- Learning NMPC control using a neural network\n- Simple LQR, PID \n- Moving horizon estimation, extended Kalman filter, unscented Kalman filter, and particle filter for a continuous stirred tank reactor\n\nInstallation\n------------\nYou can use pip to install HILO-MPC as follows \n\n```shell\npip install hilo-mpc\n```\n\nAdditional Packages\n-------------------\nIf you want to make use of the complete functionality of the toolbox, you may want to install one of the following \npackages\n\n| Package | Version | Usage |\n|--------------------------------------------------|------------------|------------------------------------------------|\n| [TensorFlow](https://www.tensorflow.org) | \\>=2.3.0, <2.8.0 | Training of neural networks |\n| [PyTorch](https://pytorch.org) | \\>=1.2.0 | Training of neural networks |\n| [scikit-learn](https://scikit-learn.org/stable/) | \\>=0.19.2 | Normalizing of training data |\n| [Bokeh](https://bokeh.org) | \\>=2.3.0 | Plotting |\n| [Matplotlib](https://matplotlib.org) | \\>=3.0.0 | Plotting |\n| [pandas](https://pandas.pydata.org) | \\>=1.0.0 | Providing data for training of neural networks |\n\nDocumentation\n-------------\nA preliminary documentation can be found [here](https://hilo-mpc.github.io/hilo-mpc/). Note that this documentation is\nnot complete and will be updated over time.\n\nCiting HILO-MPC\n---------------\nIf you use HILO-MPC for your research, please cite the following publication:\n\n* J. Pohlodek, B. Morabito, C. Schlauch, P. Zometa, R. Findeisen. **[Flexible development and evaluation of \nmachine-learning-supported optimal control and estimation methods via HILO-MPC](https://arxiv.org/abs/2203.13671)**. \narXiv. 2022.\n\n```\n@misc{pohlodek2022hilompc,\n title = {Flexible development and evaluation of machine-learning-supported optimal control and estimation methods via {HILO-MPC}},\n author = {Pohlodek, Johannes and Morabito, Bruno and Schlauch, Christian and Zometa, Pablo and Findeisen, Rolf},\n publisher = {arXiv},\n year = {2022},\n doi = {10.48550/ARXIV.2203.13671}\n}\n```\n",
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