Distributed adaptive consensus tracking control of higher-order nonlinear strict-feedback multi-agent systems using neural networks

This paper considers the output consensus problem of tracking a desired trajectory for a group of higher-order nonlinear strict-feedback multi-agent systems over directed communication topologies. Only a subset of the agents is given direct access to the desired trajectory information. A distributed adaptive consensus protocol driving all agents to track the trajectory is presented using the backstepping technique and neural networks. The Lyapunov theory is applied to guarantee that all signals in the closed-loop system are uniformly ultimately bounded and that all agents' outputs synchronize to the desired trajectory with bounded residual errors. Compared with prior work, the dynamics of each agent discussed here is more general and does not require the assumption "linearity in the unknown parameters" or the matching condition. Moreover, the bounded residual errors can be reduced as small as desired by appropriately choosing design parameters. Simulation results are included to demonstrate the effectiveness of the proposed methods.