Robust adaptive backstepping control for a class of non-affine nonlinear system with full states constraints and input saturation

被引：0

作者：

Zhang Q. ^{[1
]}

Wang C. ^{[1
]}

Xu D.-Z. ^{[2
]}

机构：

[1] School of Electrical Engineering, University of Jinan, Jinan

[2] Key Laboratory of Advanced Control for Light Industry Processes of Ministry of Education, Jiangnan University, Wuxi

来源：

Kongzhi yu Juece/Control and Decision | 2020年 / 35卷 / 04期

关键词：

Barrier Lyapunov function; Disturbance observer; Input constraint; Non-affine nonlinear systems; States constraints;

D O I：

10.13195/j.kzyjc.2018.0637

中图分类号：

学科分类号：

摘要：

Robust adaptive backstepping control for a class of non-affine nonlinear system with full states constraints and input saturation A robust adaptive backstepping control scheme is proposed for a class of pure-strict non-affine nonlinear system with state constraint and input saturation. Taylor series expansion technique is applied to the non-affine system to convert it to affine-like expression with high accuracy. The recurrent perturbation fuzzy neural networks disturbance observer (RPFNNDO) based on the projection algorithm is designed to approximate the unknown compound disturbance online. Backstepping control is used with the barrier Lyapunov function, the tanh function and the Nussbaum function to design controllers, which handles states constraints, input saturation in the system. The stability of the closed loop system is analyzed by using the Lyapunov theory. Simulation results of the unmanned aerial vehicle track control show the effectiveness of the proposed method. © 2020, Editorial Office of Control and Decision. All right reserved.

引用

页码：769 / 780

页数：11

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