Distributed adaptive output consensus control of nonlinear strict-feedback systems using neural networks

In this paper, we address the output consensus problem of tracking a desired trajectory for a group of nonlinear strict-feedback subsystems over a directed graph with a fixed topology. Each subsystem is modeled by a higher-order nonünear system with unknown nonünear dynamics. Only a subset of the subsystems is given direct access to the desired trajectory information. A distributed adaptive consensus protocol driving each subsystem to track the desired trajectory is presented using the backstepping technique and neural networks (NN). The Lyapunov theory is applied to guarantee that all signals in the closed loop system are uniformly ultimately bounded and that all subsystems' outputs synchronize to the desired trajectory with bounded residual errors. It is also demonstrated that arbitrary small tracking errors can be achieved by appropriately choosing design parameters. Simulation results validate the effectiveness of the proposed methods.