Fixed-Relative-Switched Threshold Strategies for Consensus Tracking Control of Nonlinear Multiagent Systems

TL;DR

This paper develops adaptive event-triggered control schemes for nonlinear multi-agent systems, ensuring leader-follower consensus with bounded errors and avoiding Zeno behavior, using neural networks and Lyapunov stability analysis.

Contribution

It introduces fixed, relative, and switched threshold event-triggered control strategies for nonlinear multi-agent systems with proven stability and Zeno behavior prevention.

Findings

Agents asymptotically track leader trajectory

Error signals remain uniformly bounded

Control schemes prevent Zeno behavior

Abstract

This paper investigates event-triggered consensus tracking in nonlinear semi-strict-feedback multi-agent systems involving one leader and multiple followers. We first employ radial basis function neural networks and backstepping techniques to approximate the unknown nonlinear dynamics, facilitating the design of dual observers to measure the unknown states and disturbances. Then three adaptive event-triggered control schemes are proposed: fixed-threshold, relative-threshold, and switched-threshold configurations, each featuring specialized controller architectures and triggering mechanisms. Through Lyapunov stability analysis, we establish that the follower agents can asymptotically track the reference trajectory of the leader, meanwhile all error signals remain uniform bounded. Our proposed control strategies effectively prevent Zeno behaviors through stringent exclusion criteria. Finally, an illustrative example is presented, demonstrating the competitive performance of our control framework in achieving consensus tracking and optimizing triggering efficiency.