Cooperative output feedback adaptive control of uncertain nonlinear multi-agent systems with a dynamic leader

This paper considers the cooperative tracking problem of uncertain nonlinear multi-agent systems with unmeasurable states and a dynamic leader whose input is unknown to all follower agents. By approximating the uncertain nonlinear dynamics via neural network and constructing a local observer to estimate the unmeasurable states, distributed output feedback adaptive controllers are proposed, based on the relative observed states of neighboring agents. It is proved that with the developed controllers, the state of each agent synchronizes to that of the leader for any undirected connected graphs even when only a fraction of the agents have access to the state information of the leader, and the tracking errors are guaranteed to be uniformly ultimately bounded. A sufficient condition to the existence of the controllers is that each agent is stabilizable and detectable. The main advantage, compared with existing results, lies in the fact that cooperative tracking of nonlinear systems can be achieved in the presence of unmeasurable states without knowing the input of the leader. Two illustrative examples are given to show the efficacy of the proposed methods.