Adaptive Control for Distributed Leader-Following Consensus of Multi-Agent Systems

The Distributed leader-following consensus problem for multi-agent systems has drawn increasing attention recently. Consensus is a fundamental approach for distributed coordination. It means that a group of agents are made to reach an agreement on some common states using certain local information. In the leader-following consensus problem, there exists an active leader which specifies the movement of the whole group. A majority of existing research is focused on the leader-following consensus problem assuming that the parameters of follower agents are uncertain, while few papers consider the leader dynamic uncertainty at the same time.

This thesis studies the distributed leader-following consensus problem of multi-agent systems in which the leader and followers both have parametric uncertainties and bounded external disturbances. Follower agents are controlled to follow an active leader with a reference input signal, despite such uncertainties. An Adaptive control method is adopted to solve this problem. This research starts from the basic case that there are one leader and one follower in a multi-agent system. A new adaptive scheme is proposed for dealing with parametric uncertainties. Furthermore, in order to cancel the effect of disturbances, an adaptive disturbance compensator is developed. Then, expanding the size of the multi-agent system under a directed graph, a new distributed control protocol only using local information is adopted, generalizing the previous control scheme. The proposed distributed control protocol has the capability to guarantee that all agents can reach an agreement asymptotically with disturbances acting on the follower agents. Comparing with the classical fixed gain control method, the adaptive control method is capable of effectively handling system and disturbance uncertainties. Extensive numerical simulation results illustrate the effectiveness of the proposed adaptive control scheme.