Event-Based Finite-Time Control for Nonlinear Multiagent Systems With Asymptotic Tracking

• Published in: IEEE transactions on automatic control Vol. 68; no. 6; pp. 3790 - 3797
• Main Authors: Li, Yongming, Li, Yuan-Xin, Tong, Shaocheng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Backstepping; Closed loops; command filter; Complexity theory; Convergence; event-triggered control (ETC); Explosions; Feedback control; finite-time stability; Graph theory; Multi-agent systems; Multiagent systems; multiagent systems (MASs); Neural networks; Nonlinear control; Nonlinear dynamical systems; Nonlinear systems; Singularity (mathematics); Stability criteria; Tracking errors; Tracking problem
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/TAC.2022.3197562

In this article, an adaptive neural finite-time event-triggered consensus tracking problem is studied for nonlinear multiagent systems (MASs) under directed graphs. First, the unknown nonlinear functions of MASs can be approximated by neural networks. Then, a distributed adaptive event-triggered control scheme is proposed via command filter and backstepping technique. The newly designed control scheme cannot only circumvent the problem of the explosion of complexity, but also remove the singularity issue typical of conventional backstepping technique. In the meanwhile, an event-triggered mechanism with a dynamic threshold is devised to reduce the waste of network resources. Moreover, by using a novel finite-time stability criterion, it can be proved that the closed-loop system is finite-time stable and the consensus tracking errors can reach zero as time approaches to infinity. Finally, a numerical example is given to validate the feasibility of the proposed scheme.