Neural network adaptive control scheme for nonlinear systems with Lyapunov approach and sliding mode

Abstract

PurposeThe purpose of this paper is to present an adaptive neuro‐sliding mode control scheme for uncertain nonlinear systems with Lyapunov approach.Design/methodology/approachThe paper focuses on neural network (NN) adaptive control for nonlinear systems in the presence of parametric uncertainties. The plant model structure is represented by a NNs system. The essential idea of the online parametric estimation of the plant model is based on a comparison of the measured state with the estimated one. The proposed adaptive neural controller takes advantages of both the sliding mode control and proportional integral (PI) control. The chattering phenomenon is attenuated and robust performances are ensured. Based on Lyapunov stability theorem, the proposed adaptive neural control system can guarantee the stability of the whole closed‐loop system and obtain good‐tracking performances. Adaptive laws are proposed to adjust the free parameters of the neural models.FindingsSimulation results show that the adaptive neuro‐sliding mode control approach works satisfactorily for nonlinear systems in the presence of parametric uncertainties.Originality/valueThe proposed adaptive neuro‐sliding mode control approach is a mixture of classical neural controller with a supervisory controller. The PI controller is used to attenuate the chattering phenomena. Based on the Lyapunov stability theorem, it is rigorously proved that the stability of the whole closed‐loop system is ensured and the tracking performance is achieved.

An approach to the dynamic modeling and sliding mode control of the constrained robot

“…For these characteristics, neural adaptive control and sliding mode control are regularly used to control the constrained robot, and lots of researchers have achieved good results. S Frikha et al 15 proposed an adaptive neural sliding mode control scheme with Lyapunov criterion for typical uncertain nonlinear systems, and neural network was used to estimate the structural model of the system. H Wei et al 16 used adaptive neural network control with fullstate feedback for an uncertain constrained robot, which can effectively guarantee the performance and improve the robustness of closed-loop system.…”