Adaptive Dynamic Event-triggered Control Strategy for Nonlinear Multi-agent Systems

With the wide application of multi-agent system in various fields, the research on its control algorithm becomes particularly important. In this article, we propose an adaptable dynamic event-triggered control strategy for dealing with nonlinear multi-agent systems where state limitations exist and unknown disruptions are present. The strategy combines dynamic surface control, event-triggered mechanisms, and neural network techniques to adapt to complex system dynamics and reduce computational resource consumption. By setting the concept of time consistency, the proposed algorithm ensures that the system state is consistent within any preset time, and avoids the Zeno behavior problem in traditional event-triggered control. In addition, the internal dynamic variables introduced further reduce the number of triggers, reducing the frequency of controller update and the wear of the actuator. Theoretical study and simulation experiments confirm the performance and advantage of the proposed scheme control strategy.