title: Cooperative tracking control of nonlinear multiagent systems using self-structuring neural networks.
abstract: This paper considers a cooperative tracking problem for a group of nonlinear multiagent systems under a directed graph that characterizes the interaction between the leader and the followers. All the networked systems can have different dynamics and all the dynamics are unknown. A neural network (NN) with flexible structure is used to approximate the unknown dynamics at each node. Considering that the leader is a neighbor of only a subset of the followers and the followers have only local interactions, we introduce a cooperative dynamic observer at each node to overcome the deficiency of the traditional tracking control strategies. An observer-based cooperative controller design framework is proposed with the aid of graph tools, Lyapunov-based design method, self-structuring NN, and separation principle. It is proved that each agent can follow the active leader only if the communication graph contains a spanning tree. Simulation results on networked robots are provided to show the effectiveness of the proposed control algorithms.
authors: Gang
authors: G
authors: Chen
authors: Yong-Duan
authors: YD
authors: Song
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003299:Cooperative Behavior; D005246:Feedback; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics