This paper investigates robust path tracking issue of the four-wheel independent driven robot (FWIDR) under time-varying system uncertainties and unavoidable external disturbances. A robust optimal integral sliding mode tracking control (OISMTC) scheme based on double feedback recurrent neural network (DFRNN) is proposed for the FWIDR system. Firstly, the presented OISMTC scheme modifies nominal optimal control part by exploiting an additional integral term to improve the tracking accuracy. Then, the designed DFRNN utilizes a double feedback loops structure to enhance the robustness against large system uncertainties by learning to approximate nonlinear systems. The adaptive law of the DFRNN is presented based on the Lyapunov theory to obtain favourable approximation performance in the presence of the time-varying operating conditions. Moreover, the asymptotic stability of the resultant FWIDR system is guaranteed by mathematical analysis. Finally, practical experiments are conducted to demonstrate the advantages of the proposed DFRNN-OISMTC method.
CITATION STYLE
Zhang, X., Huang, Y., Rong, Y., Li, G., Wang, H., & Liu, C. (2021). Recurrent neural network based optimal integral sliding mode tracking control for four-wheel independently driven robots. IET Control Theory and Applications, 15(10), 1346–1363. https://doi.org/10.1049/cth2.12125
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