Adaptive neural containment control of nonstrict‐feedback multi‐agent systems with unmodeled dynamics. (16th May 2022)

• Record Type: Journal Article
• Authors: Shang, Kunfeng; Zhang, Tianping; Zhang, Enze
• Is Part Of: International journal of adaptive control and signal processing. Volume 36:Number 8(2022)
• Page Start: 1879
• Page End: 1908
• Publication Date: 2022-05-16
• Subjects: containment control -- dynamic surface control -- multi‐agent systems -- nonstrict‐feedback systems -- unmodeled dynamics; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.3426
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: In this article, an adaptive containment control scheme is put forward for uncertain nonstrict‐feedback multi‐agent systems (MASs) under directed graphs. The dynamic surface control (DSC) approach is extended to the containment control of a class of nonstrict‐feedback MASs, and time‐varying gains are used to replace constant gains in the design process. Using the property of Gaussian function and Young's inequality, and introducing an auxiliary dynamic signal, nonstrict‐feedback structure problem and unmodeled dynamics are effectively resolved. The time‐varying gain controller can make all followers converge to the convex hull spanned by multiple leaders with smaller containment error, and all the signals are bounded. Finally, compared with the constant gain control method, the validity of the time‐varying gain control scheme is illustrated by numerical and actual simulation results.