Iterative Learning Consensus Tracking for Multi-Agent Systems With Output Constraints and Data Losses

In this paper, the consensus tracking problem for nonlinear multi-agent systems subjected to the output constraint, data loss and switching topologies is considered. Firstly, a consensus term is defined based on the communication information in present of the nonlinear output saturation and time-varying switching topologies. Since the random data loss is taken into consideration, the consensus term is redesigned by an introducing stochastic variable, which obeys the Bernoulli sequence with known probability. Then, a novel distributed ILC algorithm is designed by using the incomplete communication data of agents, which is more universal than the results without considered nonlinear constraint and random factors simultaneously. Through the contraction mapping method, an obtained convergence condition can also guarantee the asymptotic convergence of the agent along the iteration axis under nonlinear saturation factor and random factors. It is verified that the proposed algorithm can handle more complex situations in the consensus control of multi-agent systems. Simulation examples are further provided to verify the effectiveness of the proposed algorithm.