Cooperative Learning for Switching Networks With Nonidentical Nonlinear Agents

This paper is aimed at realizing cooperative learning for networked multi-agent systems subject to uncertain nonlinear dynamics and switching topologies. A distributed control protocol is proposed by integrating the nearest neighbor rules and iterative updating rules. Thanks to cooperative learning, all agents can be ensured to track any prescribed reference robustly over any finite interval, regardless of nonidentical locally Lipschitz nonlinearities of agents, initial state shifts, and external disturbances. Moreover, a convergence analysis approach to cooperative learning is given by exploring the properties for the products of stochastic matrices that are associated with switching digraphs.