Adaptive neural tracking control for a class of uncertain nonstrict-feedback nonlinear systems

This paper investigates adaptive neural tracking control problem for nonstrict-feedback nonlinear systems with completely unknown uncertainties. Superior to the existing results that only bounded error tracking performance can be achieved, the designed controllers of this paper will guarantee the asymptotic tracking performance under the neural network approximation framework. This is accom-plished by using a new control strategy where a proportional-integral (PI) compensator that can be conveniently implemented in practice is introduced. Meanwhile, a novel Lyapunov function is developed, whose upper-right Dini derivative will be used to construct the desired controllers and adaptive laws. Finally, simulation results are given to show the advantages and effectiveness of the proposed new design technique over some existing ones. (C) 2017 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.