Fixed-time Consensus for High-order Multi-agent Systems with Nonlinear Uncertainties and Disturbances via Event-triggered Control

This paper delves into the fixed-time consensus tracking problem within high-order multi-agent systems characterized by unknown uncertainties and disturbances. To mitigate the impact of unknown terms, we leverage the approximation capabilities of RBF neural networks to approximate the nonlinear uncertainties and design disturbance observers to estimate external disturbances. To reserve communication and computing resource utilization, we propose a fixed-time event-triggered controller and validate that the systems avoid exhibiting Zeno behavior. Ultimately, the efficacy of our proposed approach is substantiated through simulation.