A novel prescribed performance controller for strict-feedback nonlinear systems with input constraints

被引：4

作者：

Zhao, Nan-Nan ^{[1
]}

Zhang, Ai-Min ^{[2
]}

Ouyang, Xin-Yu ^{[1
]}

Wu, Li-Bing ^{[3
]}

Xu, Hai-Bo ^{[1
]}

机构：

[1] Univ Sci & Technol Liaoning, Sch Elect & Informat Engn, Anshan 114051, Liaoning, Peoples R China

[2] Faw Volkswagen Automot Co Ltd, Changchun 130011, Jilin, Peoples R China

[3] Univ Sci & Technol Liaoning, Sch Sci, Anshan 114051, Liaoning, Peoples R China

来源：

ISA TRANSACTIONS | 2023年 / 132卷

关键词：

Nonlinear systems; Non-symmetric input saturation; Unknown nonlinearities; Prescribed performance control (PPC); FINITE-TIME STABILIZATION; NEURAL-NETWORK CONTROL; TRACKING CONTROL; ADAPTIVE-CONTROL; OBSERVER; STABILITY;

D O I：

10.1016/j.isatra.2022.06.013

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 is devoted to the prescribed performance control (PPC) for a class of strict-feedback nonlinear systems with input saturation constraints. With the help of an improved tuning function, the system can achieve the desired steady-state and transient performance in the pre-designed time. A new error transformation function is introduced, which has inherent robustness, so it does not need to use any approximation technique or calculate the analytical derivative. Compared with the relevant results, the proposed scheme has the same lower complexity, but better transient and steady-state performance, although there exists uncertain nonlinearity and uncertain disturbances in the system. Finally, the correctness of the above algorithm is verified by simulation experiments.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

引用

页码：258 / 266

页数：9

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