Distributed neural control for cooperative tracking of uncertain nonlinear multi-agent systems under a directed network

This paper considers the cooperative tracking problem of uncertain nonlinear multi-agent systems over a directed network where only a fraction of followers have access to the state of the leader. Distributed neural cooperative controller based on the relative state information of neighboring agents is developed. An algebraic Riccati equation is employed to show the stability of overall multi-agent systems. Based on Lyapunov stability analysis, the developed controller guarantees that the state of each agent synchronizes to that of the leader if the leader has directed paths to all followers, and the synchronization errors are uniformly ultimately bounded. The main advantage of the developed controller is that it can be implemented in a distributed manner without requiring the accurate model of each agent. Future works include an extension to the output feedback case.