Neural-network-based tracking Control for a Class of time-delay nonlinear systems with unmodeled dynamics
This paper is concerned with the tracking control problem of a class of non-strict-feedback nonlinear systems with unmodeled dynamics and time-delay. In the backstepping procedure, a dynamic signal is designed to handle the unmodeled dynamics and the Lyapunov–Krasovskii functions are applied to compensate for the effect of time delay. Meanwhile, a neural network-based approximator is used to approximate the unknown nonlinear functions in the system. It is proved by the theoretical analysis that the presented controller guarantees the semi-global boundedness of all signals in the closed-loop systems, and the output tracking error eventually converges to a small area around zero. Simulation results are presented to illustrate the validity of the proposed approach.
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|Organisation||Department of Systems and Computer Engineering|
Wang, H. (Huanqing), Zou, Y. (Yuchun), Liu, P, Zhao, X. (Xudong), Bao, J. (Jialei), & Zhou, Y. (Yucheng). (2019). Neural-network-based tracking Control for a Class of time-delay nonlinear systems with unmodeled dynamics. Neurocomputing. doi:10.1016/j.neucom.2018.10.091