Open AccessRecurrent neural network based optimal integral sliding mode tracking control for four‐wheel independently driven robots
Author(s) -
Zhang Xiaolong,
Huang Yu,
Rong Youmin,
Li Gen,
Wang Hui,
Liu Chao
Publication year - 2021
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/cth2.12125
Subject(s) - zhàng , chinese academy of sciences , engineering , science and engineering , china , tracking (education) , artificial intelligence , engineering management , computer science , engineering ethics , sociology , political science , pedagogy , law
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.