Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/ohtsukalab/autogenu-jupyter

An automatic code generator for nonlinear model predictive control (NMPC) and the continuation/GMRES method (C/GMRES) based numerical solvers for NMPC
https://github.com/ohtsukalab/autogenu-jupyter

c-gmres code-generation code-generator model-predictive-control mpc multiple-shooting nmpc nonlinear-model-predictive-control

Last synced: about 2 months ago
JSON representation

An automatic code generator for nonlinear model predictive control (NMPC) and the continuation/GMRES method (C/GMRES) based numerical solvers for NMPC

Awesome Lists containing this project

README 

        
# AutoGenU for Jupyter



[![build](https://github.com/ohtsukalab/autogenu-jupyter/actions/workflows/build.yaml/badge.svg?branch=master)](https://github.com/ohtsukalab/autogenu-jupyter/actions/workflows/build.yaml)

[![doxygen](https://github.com/ohtsukalab/autogenu-jupyter/actions/workflows/doxygen.yaml/badge.svg)](https://github.com/ohtsukalab/autogenu-jupyter/actions/workflows/doxygen.yaml)



## Introduction

This project provides the continuation/GMRES method (C/GMRES method) based solvers for nonlinear model predictive control (NMPC) and an automatic code generator for NMPC, called `AutoGenU`.



The following C/GMRES based solvers are provided: 

- `MultipleShootingCGMRESSolver` : The multiple shooting based C/GMRES method with condensing of the state and costate directions.

- `SingleShootingCGMRESSolver` : The original C/GMRES method (single shooting).



## Requirement

- C++17 (MinGW or MSYS and PATH to either are required for Windows users)

- CMake, git

- Python 3.8 or later, Jupyter Lab or Jupyter Notebook, SymPy, NumPy, and collection (to generate `ocp.hpp`, `main.cpp`, and `CMakeLists.txt` by `AutoGenU.ipynb`)

- Matplotlib, seaborn (to plot simulation data on `AutoGenU.ipynb`)

- ffmpeg (to generate animations in the example notebooks)

- Doxygen (optional, to generate C++ docs)



## Usage

### 1. Setup requirements

Please confirm that you clone this repository as 

```

git clone https://github.com/ohtsukalab/autogenu-jupyter --recursive

```

Otherwise, please do the following command:

```

git submodule update --init --recursive

```

The python modules can be installed via

```

python3 -m pip install -r requirements.txt

```



### 2. Code generation

`AutoGenU.ipynb` generates the following source files under your setting state equation, constraints, cost function, and parameters: 

- `ocp.hpp` : A definition of the optimal control problem (OCP).

- `main.cpp` : An executablb of the closed-loop simulation.

- `CMakeLists.txt` : Scripts to build C++ projects. 

- Files in `python` directory : Source files of Python interface via pybind11.



You can generate these files, run simulations, plot results, and install the Python interfaces through `AutoGenU.ipynb`.



### 3. Python bindings

Python bindings are installed via `.ipynb` files. 

To use the installed Python bindings, set `PYTHONPATH` as 

```

export PYTHONPATH=$PYTHONPATH:$DESTINATION/lib/python3.x/site-packages

``` 

Then you can use python interfaces as 

```

import cgmres.common # this includes horizon, solver settings, etc.

import cgmres.your_ocp_name # this includes OCP definition and NMPC solvers 

```



### 4. Install header-only `cgmres` C++ library

Aside from the notebook for the code-generation, the C++ `cgmres` library, which is a header-only library, can be installed by running

```

mkdir build

cd build

cmake .. -DCMAKE_INSTALL_PREFIX=YOUR_INSTALL_DESTINATION

make install 

```

at the project root directory of `autogenu-jupyter`.  

Then you can build the NMPC code with the generated `ocp.hpp` file and without `.ipynb` notebook files.   

The examples are found in `examples/cpp` directory.  



### 5. Install `autogenu` Python module

The pythton module `autogenu` can be instatlled by running

```

python3 -m pip install setuptools

python3 -m pip install .

```

at the project root directory of `autogenu-jupyter`.

Further, if you install have installed header-only `cgmres` C++ library as step 4, then you can run `.ipynb` files for the code generation in everywhere.



### Documentation

C++ API documentation of `cgmres` library is found at https://ohtsukalab.github.io/autogenu-jupyter/.   

Python interfaces are almost the same as the C++ API, so please refere to https://ohtsukalab.github.io/autogenu-jupyter/ even for Python interfaces as well as the [tips for conversions between C++ and Python](https://ohtsukalab.github.io/autogenu-jupyter/md__github_workspace_examples_python__r_e_a_d_m_e.html).



## Demos

Demos are presented in `cartpole.ipynb`, `pendubot.ipynb`, `hexacopter.ipynb`, and `mobilerobot.ipynb`. You can obtain the following simulation results jusy by runnig these `.ipynb` files. The details of the each OCP formulations are described in each `.ipynb` files.



  

 



  



 



## License

MIT



## Citing autogenu-jupyter



We'd appriciate if you use cite the following conference paper:



```

@inproceedings{katayama2020autogenu,

  title={Automatic code generation tool for nonlinear model predictive control with {J}upyter},

  author={Sotaro Katayama and Toshiyuki Ohtsuka},

  booktitle={{The 21st IFAC World Congress 2020}},

  pages={7033-7040},

  year={2020}}

```



## References

1. [T. Ohtsuka A continuation/GMRES method for fast computation of nonlinear receding horizon control, Automatica, Vol. 40, No. 4, pp. 563-574 (2004)](https://doi.org/10.1016/j.automatica.2003.11.005)

2. [C. T. Kelly, Iterative methods for linear and nonlinear equations, Frontiers in Apllied Mathematics, SIAM (1995)](https://doi.org/10.1137/1.9781611970944)

3. [Y. Shimizu, T. Ohtsuka, M. Diehl, A real‐time algorithm for nonlinear receding horizon control using multiple shooting and continuation/Krylov method, International Journal of Robust and Nonlinear Control, Vol. 19, No. 8, pp. 919-936 (2008)](https://doi.org/10.1002/rnc.1363)