Practical prescribed time tracking control for a class of nonlinear systems with event triggering and output constraints

被引：2
作者：
Zou, Fangling [1 ]
Wu, Kang [1 ,2 ]
机构：
[1] Qufu Normal Univ, Sch Engn, Rizhao, Peoples R China
[2] Jiangnan Univ, Sch Internet Things Engn, Wuxi 214122, Peoples R China
来源：
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL | 2024年 / 34卷 / 17期
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
event-triggered control; neural networks; practical prescribed time tracking; robust adaptive control; FINITE-TIME; FUZZY CONTROL; GUIDANCE LAW; FEEDBACK; DESIGN; STABILIZATION;
D O I：
10.1002/rnc.7612
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper investigates the practical prescribed time tracking for a class of uncertain nonlinear systems based on neural networks and event-triggered control. Introducing a time-varying constraint function transforms the original practical prescribed time-tracking control issue into a tracking error constraint problem. An event-triggered adaptive control has been proposed, which can effectively reduce the communication burden between the controller and the actuator. Using neural networks to approximate unknown nonlinear functions avoids the differentiation of virtual controllers, thereby reducing the computational burden. In addition, users can independently choose preset time and tracking accuracy without changing the control structure, which remains independent of the initial conditions and any design parameters. Finally, the effectiveness of this method is verified through simulation examples.
引用
收藏
页码：11786 / 11803
页数：18
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