Adaptive neural dynamic surface control of strict-feedback nonlinear systems with full state constraints and unmodeled dynamics. (July 2017)

• Record Type: Journal Article
• Authors: Zhang, Tianping; Xia, Meizhen; Yi, Yang
• Is Part Of: Automatica. Volume 81(2017)
• Page Start: 232
• Page End: 239
• Publication Date: 2017-07
• Subjects: State constraint -- Unmodeled dynamics -- Adaptive control -- Neural networks -- Dynamic surface control -- Strict-feedback nonlinear systems; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2017.03.033
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: In this paper, the problem of adaptive neural network (NN) dynamic surface control (DSC) is discussed for a class of strict-feedback nonlinear systems with full state constraints and unmodeled dynamics. By introducing a one to one nonlinear mapping, the strict-feedback system with full state constraints is transformed into a novel pure-feedback system without state constraints. Radial basis function (RBF) neural networks (NNs) are used to approximate unknown nonlinear continuous functions. Unmodeled dynamics is dealt with by introducing a dynamical signal. Using modified DSC and introducing integral-type Lyapunov function, adaptive NN DSC is developed. Using Young's inequality, only one parameter is adjusted at each recursive step in the design. It is shown that all the signals in the closed-loop system are semi-global uniform ultimate boundedness (SGUUB), and the full state constraints are not violated. Simulation results are provided to verify the effectiveness of the proposed approach.