Iterative Learning Control for Consensus of Discrete Time-varying Multi-agent System with Data Dropout

Aiming at the consensus control problem of the discrete time-varying multi-agent system with data dropout, this paper first introduces a diagonal random matrix to handle the system output data dropout which is modeled by Bernoulli distribution. Then, under a fixed communication topology, a distributed P-type iterative learning control algorithm considering data dropout is designed by using the local information that the follower can obtain. Furtherly, according to the tracking error of the system, a sufficient condition is given for the convergence of all follower outputs to virtual leader output, and the convergence is strictly proved by using the λ-norm. Finally, an example simulation is carried out. Both theoretical analysis and example simulation verify the effectiveness of the proposed algorithm.