Adaptive neural network control for a class of interconnected pure-feedback time-delay nonlinear systems with full-state constraints and unknown measurement sensitivities

Abstract

This paper investigates the problem of the adaptive neural network control design for full state constrained interconnected pure-feedback time-delay systems with unknown measurement sensitivity. Based on the dynamic surface design approach, constrained transform functions are used to deal with the asymmetric state constraints, which can remove the feasibility conditions. The problem of unknown measurement sensitivity is considered in interconnected systems, and the inaccurate information of states and output brings more difficulties and challenges to this design. Through transforming the nonlinearities caused by unknown measurement sensitivity into bounded nonlinear functions, radial basis function neural network can be utilized to approximate the unknown nonlinear terms. And the proposed control strategy can ensure all signals of the system are semi-globally ultimately uniformly bounded and the asymmetric state constraints are strictly maintained. Finally, simulation examples are given to show the effectiveness of the proposed method.