Quantized Iterative Learning Control for Formation of Multi-agent System

This paper investigates the formation control problem for discrete-time multi-agent systems with switching network topologies and data quantization. It is assumed that the tracking error signals of individual agent are quantized before they are transmitted into the iterative learning controller. However, quantification of data can lead to quantization error, which seriously impacts the performance of multi-agent systems. Based on the nearest neighbor interaction rule, a quantized iterative learning approach is given to overcome the quantization error in the occasion of switching network topologies and guarantee the accurate formation of multi-agent systems simultaneously. Simulation results are provided to verify the effectiveness of the proposed method.