A Deep Reinforcement Learning-based Sliding Mode Control Design for Partially-known Nonlinear Systems
Abstract
Presence of model uncertainties creates challenges for model-based control design, and complexity of the control design is further exacerbated when coping with nonlinear systems. This paper presents a sliding mode control (SMC) design approach for nonlinear systems with partially known dynamics by blending data-driven and model-based approaches. First, an SMC is designed for the available (nominal) model of the nonlinear system. The closed-loop state trajectory of the available model is used to build the desired trajectory for the partially known nonlinear system states. Next, a deep policy gradient method is used to cope with unknown parts of the system dynamics and adjust the sliding mode control output to achieve a desired state trajectory. The performance (and viability) of the proposed design approach is finally examined through numerical examples.
Cite
@article{arxiv.2205.02975,
title = {A Deep Reinforcement Learning-based Sliding Mode Control Design for Partially-known Nonlinear Systems},
author = {Sahand Mosharafian and Shirin Afzali and Yajie Bao and Javad Mohammadpour Velni},
journal= {arXiv preprint arXiv:2205.02975},
year = {2023}
}
Comments
Accepted for presentation and publication in the proceedings of the 2022 European Control Conference (ECC), July 12-15, 2022