A Deep Reinforcement Learning-based Sliding Mode Control Design for Partially-known Nonlinear Systems
| Autor: | Sahand Mosharafian, Shirin Afzali, Yajie Bao, Javad Mohammadpour Velni |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
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| Popis: | 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. Accepted for presentation and publication in the proceedings of the 2022 European Control Conference (ECC), July 12-15, 2022 |
| Databáze: | OpenAIRE |
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