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
收藏
页码：871 / 887
页数：17
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