Output-feedback adaptive consensus tracking control for a class of high-order nonlinear multi-agent systems

In this paper, an output-feedback adaptive consensus tracking control scheme is proposed for a class of high-order nonlinear multi-agent systems. The agents are allowed to have unknown parameters, unknown nonlinearities, and input quantization simultaneously. The desired trajectory to be tracked is available for only a subset of agents, and only the relative outputs and the quantized inputs need to be measured or transmitted as signal exchange among neighbors regardless of the system order. By introducing a kind of high-gain K-filters and a smooth function, the effect among agents caused by the unknown nonlinearities is successfully counteracted, and all closed-loop signals are proved to be globally uniformly bounded. Moreover, it is shown that the tracking errors converge to a residual set that can be made arbitrarily small. Simulation results on robot manipulators are presented to illustrate the effectiveness of the proposed scheme.