Learning-based distributed adaptive control of heterogeneous multi-agent systems with unknown leader dynamics

This study focuses on the distributed adaptive tracking control of heterogeneous multi-agent systems with unknown leader dynamics in a directed graph. In contrast to the reported leader-following consensus studies, the prior knowledge of the leader is supposed to be cognised to some or all of followers, the situation that the leader's dynamics is totally unrecognised but can be learnt for each individual follower is considered. A data-driven learning algorithm using the systems data is developed to reconstruct the unknown systems matrix. Then, an adaptive distributed dynamic compensator is exploited to provide the leader's state estimation in a directed graph. Afterwards, a dynamic output feedback control law for each agent is projected. Theoretical analysis shows that the proposed algorithms not only ensure that all followers can identify the unknown system matrix, but also guarantee that the distributed output leader-following consensus control with heterogeneous dynamics is achieved without any global information. Finally, a numerical example is provided to testify the proposed algorithms.