Event-Triggered Adaptive Tracking Control for Multi-Agent Systems With Multiple Uncertainties

The existing distributed tracking control for heterogeneous multi-agent systems employs a method that involves designing distributed observers relying on precise models. When the system matrices, controller gains, and coupling parameters are unknown, existing control methods struggle to handle multiple unknown parameters concurrently. To address this challenge, we propose a direct adaptive control approach featuring the simplest discrete communication and control structure for studying the event-triggered tracking control problem in heterogeneous and uncertain multi-agent systems. Firstly, we establish two fundamental lemmas pertinent to event-triggered distributed tracking control. Subsequently, we propose a new adaptive event-triggered control strategy featuring the simplest communication architecture, grounded in the two fundamental lemmas established earlier. The proposed architecture enables online adaptive adjustment of both feedback and coupling gains without requiring any additional communication beyond the states of neighboring agents. Furthermore, we extend our findings to dynamic event-triggered adaptive tracking control, ensuring that Zeno behavior is avoided. Unlike similar adaptive tracking control studies that design feedback or coupling gains exclusively for homogeneous or heterogeneous dynamics, our algorithms account for multiple adaptive gains in heterogeneous and uncertain dynamics, thereby eliminating the need for a distributed observer. Lastly, we provide a numerical example to validate our theoretical algorithms.