ParNMPC Version 1903-1

New Features in Version 1903-1:

• Primal-dual interior-point method
• Improved user interface
• Better performance
• Line search

Introduction

Homepage: https://deng-haoyang.github.io/ParNMPC/

ParNMPC is a MATLAB real-time optimization toolkit for nonlinear model predictive control (NMPC). The purpose of ParNMPC is to provide an easy-to-use environment for NMPC problem formulation, closed-loop simulation, and deployment.

With ParNMPC, you can define your own NMPC problem in a very easy way and ParNMPC will automatically generate self-contained C/C++ code for single- or multi-core CPUs.

ParNMPC is very fast even with only one core (the computation time is usually in the range of $\mu$s), and a high speedup can be achieved when parallel computing is enabled.

Highlights

• Symbolic problem representation
• Automatic parallel C/C++ code generation with OpenMP
• Fast rate of convergence (up to be superlinear)
• Highly parallelizable (capable of using at most N cores, N is the # of discretization steps)
• High speedup ratio
• MATLAB & Simulink

Installation

1. Clone or download ParNMPC.
2. Extract the downloaded file.

Requirements

• MATLAB 2016a or later
• MATLAB Coder
• MATLAB Optimization Toolbox
• MATLAB Parallel Computing Toolbox
• MATLAB Symbolic Math Toolbox
• Simulink Coder
• C/C++ compiler supporting parallel code generation

Getting Started (MATLAB 2018b)

1. Select the Microsoft Visual C++ 2017 (C) compiler:

>> mex -setup

2. Navigate to the Quadrotor/ folder.

>> cd  Quadrotor/

3. Open NMPC_Problem_Formulation.m and run.

4. Open Simu_Simulink_Setup.m and run.

5. Open Simu_Simulink.slx and run.

Citing ParNMPC

Citing the parallel algorithm:

@article{deng2019parallel,
  title={A parallel Newton-type method for nonlinear model predictive control},
  author={Deng, Haoyang and Ohtsuka, Toshiyuki},
  journal={Automatica},
  volume={109},
  pages={108560},
  year={2019}}

Citing the toolbox (conference version):

@inproceedings{deng2018parallel,
  title={A parallel code generation toolkit for nonlinear model predictive control},
  author={Deng, Haoyang and Ohtsuka, Toshiyuki},
  booktitle={Proceedings of the 57th {IEEE} {C}onference on {D}ecision and {C}ontrol},
  pages={4920--4926},
  year={2018},
  address={Miami, USA}}

License

ParNMPC is distributed under the BSD 2-Clause "Simplified" License.