Distributed cooperative tracking of uncertain nonlinear multi-agent systems with fast learning

In this paper, a distributed adaptive control architecture is proposed for cooperative tracking of uncertain dynamical multi-agent systems over an undirected network. Adaptive cooperative tracking controllers with both static and adaptive coupling gains are developed using local information obtained from neighboring agents. Neural networks together with filtering adaptive laws are employed to enable fast learning in the presence of unmodeled uncertainties and external disturbances. Moreover, this result is extended to the output feedback case in which only partial state information of each agent can be measured. Observer-based adaptive cooperative tracking controllers with static and adaptive coupling are developed, and a parameter dependent Riccati equation is employed to derive the stability of the overall multi-agent systems. Compared with the existing results, a distinct feature of the developed controllers enables fast learning using large adaptive gains with guaranteed low frequency control signals. Two illustrative examples are given to validate the efficacy of the proposed approaches.