Mathematical analysis

Psychology

Artificial intelligence

Singularity

Physics

Nonlinear system

Pedagogy

Event (particle physics)

Mathematics

Backstepping

Quantum mechanics

Computer science

Adaptive control

Control (management)

Tracking (education)

Multi-agent system

Control theory (sociology)

Artificial neural network

IEEE Transactions on Automatic Control

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.

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