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arXiv · 2205.02975

A Deep Reinforcement Learning-based Sliding Mode Control Design for Partially-known Nonlinear Systems

Sahand Mosharafian·Shirin Afzali·Yajie Bao·Javad Mohammadpour Velni
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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.

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Sahand Mosharafian, Shirin Afzali, Yajie Bao, Javad Mohammadpour Velni. 2022-05-06. A Deep Reinforcement Learning-based Sliding Mode Control Design for Partially-known Nonlinear Systems. https://doi.org/10.23919/ecc55457.2022.9838169

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