Leader-following consensus protocol for second-order multi-agent systems using neural networks

In this paper, an adaptive leader-following consensus protocol is proposed for second-order multi-agent systems with unknown nonlinear dynamics based on neural networks (NNs), and it is proved that if at least one agent in each connected component of the fixed interaction graph is connected to the leader, all agents can asymptotically track the leader. By estimating the tracking error in terms of L₂ norm, the transient performance of multi-agent systems can be significantly improved. Finally, a numerical example is included to the proposed protocol.