Predictor-based adaptive dynamic surface control for consensus of uncertain nonlinear systems in strict-feedback form

This paper investigates the leader-follower consensus problem of uncertain nonlinear systems in strict-feedback form. By parameterizations of unknown nonlinear dynamics of the agents, an adaptive dynamic surface control with the aid of predictors, tracking differentiators is proposed to realize output consensus of the multi-agent systems. Unlike the existing adaptive consensus methods, the predictor errors are used to learn the unknown parameters, which can achieve fast learning without high-frequency signals in control inputs. As a fast precise signal filter, the tracking differentiator is used in the control design instead of first-order filters, which can further improve the control performance. Based on graph theory and Lyapunov stability theory, it is shown that the outputs of all followers ultimately synchronize to that of the leader with bounded tracking errors. Simulation results are provided to validate the effectiveness and advantage of the proposed consensus algorithm. Copyright (C) 2016 John Wiley & Sons, Ltd.