Event-Triggered Iterative Learning Control for Multi-Agent Systems with Quantization

The emergence of networked control systems urges the digital control design to integrate communication constraints efficiently. In order to accommodate this requirement, this paper investigates the joint design of tracking problem for multi-agent system (MAS) in the presence of resource-limited communication channel and quantization. An event-triggered robust learning control with quantization is firstly proposed and employed for MAS in this paper. The new event-triggered distributed robust learning control system with the introduction of logarithmic quantization guarantees the asymptotic tracking property on the finite interval. Convergence analysis is given based on the Lyapunov direct method. Finally, numerical simulations are given to illustrate the efficacy of the event-triggered approach compared with time-triggered controllers.