Finite-time adaptive neural network command filtered controller design for nonlinear system with time-varying full-state constraints and input quantization

被引：23

作者：

Wei, Shu-Yi ^{[1
]}

Li, Yuan-Xin ^{[1
]}

机构：

[1] Liaoning Univ Technol, Coll Sci, Jinzhou 121001, Peoples R China

来源：

INFORMATION SCIENCES | 2022年 / 613卷

关键词：

Uncertain nonlinear systems; Command filtered backstepping; Input quantization; Adaptive control; Time-varying barrier Lyapunov function; Finite-time convergence; TRACKING CONTROL; BACKSTEPPING CONTROL; ROBOTIC MANIPULATOR; ASYMPTOTIC TRACKING; FUZZY CONTROL; STABILIZATION; DISTURBANCE;

D O I：

10.1016/j.ins.2022.08.114

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, an adaptive finite-time command filtered control scheme is investigated for a class of nonlinear systems with asymmetric time-varying full state constraints and input quantization. Firstly, by fusing the backstepping control method and command filter tech-nique, a novel adaptive neural networks (NNs) command filtered control approach is pro-posed. Moreover, a modified error compensation mechanism is constructed to compensate for the filtering error. Secondly, a smooth nonlinear transformation is constructed to elim-inate the influence brought by the actuator subject to input quantization. Based on the con-structed asymmetric time-varying barrier Lyapunov function (TVBLF), the strict constraints are guaranteed not to be violated and the stability of the closed-loop system is achieved in finite-time. Finally, simulations are carried out to illustrate the effectiveness of the theoret-ical results. (c) 2022 Elsevier Inc. All rights reserved.

引用

页码：871 / 887

页数：17

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