Content area

Abstract

In this paper, the fixed-time tracking control (FTTC) problem is discussed for a type of uncertain high-order nonlinear systems. Compared with the existing works, the studied system is affected by time-varying parameters and unknown input nonlinearity. By applying neural network (NN) approximation method together with the adaptive control method, the fixed-time control theory, the backstepping control method, and the Nussbaum gain function (NGF) technique, an adaptive NN-based FTTC scheme is presented to achieve fixed time tracking. Especially, the NGF is utilized to handle the unknown control gain caused by unknown input nonlinearity. Furthermore, some adaptive control laws are formulated to estimate unknown parameters. Under the influence of different input nonlinearity, it can be inferred that the designed control strategy guarantees that the tracking error converges to a small neighborhood of zero within a fixed time, while also maintaining the boundedness of all signals of the closed-loop system. Finally, three simulation cases are presented to validate the availability of the theoretical results.

Details

1009240
Title
Approximation-based adaptive fixed-time tracking control for uncertain high-order nonlinear systems subject to time-varying parameters and unknown input nonlinearity
Volume
15
Issue
1
Pages
10504
Publication year
2025
Publication date
2025
Publisher
Nature Publishing Group
Place of publication
London
Country of publication
United States
Publication subject
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-03-26
Milestone dates
2025-02-17 (Registration); 2024-11-07 (Received); 2025-02-17 (Accepted)
Publication history
 
 
   First posting date
26 Mar 2025
ProQuest document ID
3181554162
Document URL
https://www.proquest.com/scholarly-journals/approximation-based-adaptive-fixed-time-tracking/docview/3181554162/se-2?accountid=208611
Copyright
Copyright Nature Publishing Group 2025
Last updated
2025-03-27
Database
ProQuest One Academic