PDE-Based Adaptive Consensus Control of Leader–Follower Multiagent Systems With Dynamic Event-Triggered Strategy

This article addresses the leader–follower consensus problem for a class of nonlinear multiagent systems (MASs) whose collective behavior is modeled by a diffusion partial differential equation (PDE). Existing control strategies for such systems often suffer from high communication overhead and a lack of robustness to unknown nonlinearities and disturbances. To overcome these limitations, we introduce a novel adaptive control scheme that integrates a dynamic event-triggered mechanism with a radial basis function neural network (RBFNN) approximator. The dynamic event trigger scheme significantly reduces communication burdens by aperiodically updating the control signal only at specific moments, while the RBFNN is employed to effectively compensate for the unknown boundary function and unmodeled disturbances. We provide a rigorous Lyapunov-based stability analysis to prove that the proposed controller guarantees stability of the closed-loop system. Numerical simulations demonstrate the efficacy of the proposed method, showing a substantial reduction in communication frequency while ensuring precise consensus tracking.