Adaptive Neural Network Control Design for Uncertain Nonstrict Feedback Nonlinear System With State Constraints

Abstract

In this paper, an adaptive neural network (NN) constraint control method is studied for a class of uncertain nonlinear nonstrict feedback systems with state constraints. The restrictive assumption that the unknown internal dynamics must possess the monotonically increasing characteristics in previous results is removed. The property of radial basis function (RBF)NNs is used to solve the algebraic loop problem based on the approximation structure. In order to achieve full state constraint satisfactions, the barrier Lyapunov functions (BLFs) are employed in each design procedure. Based on the backstepping and less adjustable parameters techniques, the controllers and the adaptive laws are obtained. By using the Lyapunov stability theory, the boundedness of all signals in the closed-loop system is proved. Therefore, the scheme not only solves the stability problem of the nonstrict feedback system but also overcomes the influence of the state constraint on the control performance. Finally, the effectiveness of the control method is verified by two simulation examples.