Adaptive Neural Network Control of Uncertain Strict-Feedback Systems with Full-State Constrains by Integral-Barrier Lyapunov Functions

Abstract

In this paper, an adaptive neural network (NN) control method is developed for a class of nonlinear uncertain strict-feedback systems with full-state constrains. Firstly, radial basis function neural networks(RBFNN) are employed in handling uncertainties of the nonlinear strict-feedback system, and the approximate error can be arbitrarily small. Meanwhile, the online computation burden can be greatly reduced with less learning parameters. Then, integral-barrier Lyapunov functions (iBLF) are used to avoid violating full-state constrains, which alleviates the conservatism by using original states directly rather than tracking errors. Subsequently, based on backstepping design procedures, the adaptive neural network controller is proposed, which can guarantee the semi-global uniformly ultimate boundedness of output error. Moreover, all signals of the closed-loop system are proved to be bounded by the Lyapunov analysis. Finally, a numerical simulation illustrates the effectiveness of the proposed method.