Robust iterative learning control of multi-agent systems with logarithmic quantizer

In order to accommodate the requirement of digital control design, this paper investigates the consensus problem of leader-following multi-agent systems (MAS) by using the robust learning control approach (RLC), and also gives a visible distributed quantized protocol to update the dynamic systems. A quantizer is firstly proposed and employed when designing an iterative learning control algorithm. Because of the existence of the quantizer, even linear systems turn to be nonlinear ones. A robust control scheme with the help of Lyapunov direct method is utilized to overcome the difficulty. As well as a new RLC system guarantees the asymptotic tracking property on the finite interval. What's more, the criteria for the asymptotical convergence analysis and robust controller of quantized MAS are established by utilizing Lyapunov function. Simulation results are provided to verify the effectiveness of the proposed approach.