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

Purpose - The purpose of this paper is to present an adaptive neuro-sliding mode control scheme for uncertain nonlinear systems with Lyapunov approach. Design/methodology/approach - The 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. Findings - Simulation results show that the adaptive neuro-sliding mode control approach works satisfactorily for nonlinear systems in the presence of parametric uncertainties. Originality/value - The 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.