Iterative Learning Consensus for Discrete-time Multi-Agent Systems with Measurement Saturation and Random Noises

This paper investigates the consensus tracking problem for a class of multi-agent systems with measurement saturation and random noises. A distributed iterative learning control algorithm is proposed by utilizing the input signals and the measured output information from previous iterations. The considered multi-agent systems have a fixed topology of the communication graph and the desired trajectory is only accessible to a subset of agents. With the help of a decreasing gain sequence, it is proved that the input sequence will converge to the desired one in an almost sure sense as the iteration number goes to infinity. Simulation results are given to verify the effectiveness of the proposed algorithm.