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SODO Based Reinforcement Learning Anti-Disturbance Fault Toler-Ant Control for a Class of Nonlinear Uncertain Systems With Matched and Mismatched Disturbances

Shiyi Huang, Zheng Wang, Zhaohui Yuan and 2 more

IEEE Access · 2021

doi:10.1109/access.2021.3118723 (opens in a new tab)

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Abstract

This paper proposes a reinforcement learning anti-disturbance fault tolerant control structure for a class of nonlinear uncertain systems with time varying matched and mismatched disturbances. To deal with the time varying matched and mismatched disturbances, two second order disturbance observers (SODOs) are designed for the inner and outer loop dynamic equations. For the purpose of enhancing the robustness and adaptivity with respect to the system uncertainties, two long short-term memory (LSTM) networks those possesses perfect fitting ability, have been introduced as the critic and actor networks. Moreover, to overcome the difficulty caused by the unknown perturbations of the control effectiveness, several fault tolerant adaptive laws have been designed. Consequently, a novel reinforcement learning anti-disturbance fault tolerant control structure has been established for the concerned disturbed nonlinear uncertain system. Two numerical examples are provided finally, demonstrating the satisfactory performance of the proposed control structure.

Abstract licence: CC BY-NC-ND

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