Adaptive neural data-based compensation control of non-linear systems with dynamic uncertainties and input saturation

• Published in: IET control theory & applications Vol. 9; no. 7; pp. 1058 - 1065
• Main Authors: Wang, Huanqing, Liu, Xiaoping, Liu, Kefu
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 23.04.2015
• Subjects: adaptive control; Adaptive control systems; adaptive neural backstepping control; adaptive neural data‐based compensation control; closed loop systems; closed‐loop system; Computer simulation; Control systems; dynamic disturbances; dynamic signal; Dynamic tests; dynamic uncertainties; Dynamical systems; feedback; input saturation nonlinearity; neurocontrollers; nonaffine structure; nonlinear control systems; Nonlinear dynamics; Nonlinearity; radial basis function networks; radical basis function neural networks; RBF neural networks; Saturation; smooth function; Special Issue: Data-Based Control and Process Monitoring with Industrial Applications; strict‐feedback nonlinear systems; uncertain systems; unmodelled dynamics; smooth function; adaptive neural backstepping control; nonaffine structure; RBF neural networks; uncertain systems; strict-feedback nonlinear systems; adaptive control; dynamic uncertainties; adaptive neural data-based compensation control; feedback; closed loop systems; neurocontrollers; dynamic disturbances; nonlinear control systems; unmodelled dynamics; radial basis function networks; input saturation nonlinearity; radical basis function neural networks; dynamic signal; closed-loop system
• ISSN: 1751-8644; 1751-8652
• DOI: 10.1049/iet-cta.2014.0709

In this study, an adaptive neural backstepping control scheme is proposed for a class of strict-feedback non-linear systems with unmodelled dynamics, dynamic disturbances and input saturation. To solve the difficulties from the unmodelled dynamics and input saturation, a dynamic signal and smooth function in non-affine structure subject to the control input signal are introduced, respectively. Radial basis function (RBF) neural networks are used to approximate the packaged unknown non-linearities, and an adaptive neural control approach is developed via backstepping, which guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded in mean square. The main contributions of this note lie in that a control strategy is provided for a class of strict-feedback non-linear systems with unmodelled dynamics uncertainties and input saturation, and the proposed control scheme does not require any information of the bound of input saturation non-linearity. Simulation results are used to show the effectiveness of the proposed control scheme.