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- W4292070336 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." @default.
- W4292070336 created "2022-08-17" @default.
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- W4292070336 date "2022-07-12" @default.
- W4292070336 modified "2023-09-27" @default.
- W4292070336 title "A Deep Reinforcement Learning-based Sliding Mode Control Design for Partially-known Nonlinear Systems" @default.
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- W4292070336 doi "https://doi.org/10.23919/ecc55457.2022.9838169" @default.
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