Status: The implementation code for corresponding papers will be merged here and new papers will be added in an inverse order of submission.

Introduction

In this repository, a collection of our work is presented where nonlinear model predictive control (NMPC) with control Lyapunov functions (CLFs) and control barrier functions (CBFs) are applied.

Dependencies

The packages needed for running the code are Yalmip and IPOPT.

We also provide the zipped version of precompiled .mex files for IPOPT in the folder packages in case you don't have it. Unzip the file based on the operating system and add those .mex files into your MATLAB path.

Citing

Theoretical Publications

If you find this project useful in your work, please consider citing following work:

• S. Liu, J. Zeng, K. Sreenath and C. Belta. "Iterative Convex Optimization for Model Predictive Control with Discrete-Time High-Order Control Barrier Functions." 2023 IEEE American Control Conference (ACC). [arXiv] [Docs] [Code] [BibTex]

• A. Thirugnanam, J. Zeng, K. Sreenath. "Safety-Critical Control and Planning for Obstacle Avoidance between Polytopes with Control Barrier Functions." 2022 IEEE International Conference on Robotics and Automation (ICRA). [IEEE] [arXiv] [Video] [BibTex]

• J. Zeng, Z. Li, K. Sreenath. "Enhancing Feasibility and Safety of Nonlinear Model Predictive Control with Discrete-Time Control Barrier Functions." 2021 IEEE Conference on Decision and Control (CDC). [IEEE] [arXiv] [Docs] [Code] [BibTex]

• J. Zeng, B. Zhang and K. Sreenath. "Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function." 2021 IEEE American Control Conference (ACC). [IEEE] [arXiv] [Docs] [Code] [BibTex]

Applicational Publications

• Z. Li, J. Zeng, A. Thirugnanam, K. Sreenath. "Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Models." 2022 Proceedings of Robotics: Science and Systems (RSS). [RSS] [arXiv] [BibTex] [Webpage]