DISTRIBUTED ADAPTIVE NEURAL NETWORK CONTROL FOR A CLASS OF UNCERTAIN HETEROGENEOUS MULTI-AGENT SYSTEMS

In this study, a distributed adaptive robust control scheme of the cooperative tracking stabilization based on radial basis function neural network (RBFNN) is developed for a class of uncertain multi-agent systems with different subsystems. Multi-agent dynamical systems in followers are supposed to be different dynamical behaviors due to their different equations, and each follower has the leader system with a series of similar parameters. By using the properties of similarity among each agent, the feedback control with robust terms, coupling weights adaptive laws and the neural network weights are designed for the consensus of heterogeneous multi-agent systems, which break the limitation of existing works for heterogeneous multi-agent systems with the same structure. The states of each follower synchronize to the dynamical behavior of the leader reference model, and all signals in the closed-loop systems can be guaranteed to be uniformly ultimately bounded (UUB). Finally, by employing the relationship of undirected connected communication graphs for every multi-agent system, three simulation examples are verified by good tracking performances.