Finite-time adaptive neural control for nonstrict-feedback stochastic nonlinear systems with input delay and output constraints

Abstract This paper addresses a novel finite-time adaptive neural control (FTANC) problem for nonstrict-feedback stochastic nonlinear systems (NSFSNS), in which the input delay and output constrained problems are considered simultaneously. First, the Pade approximation technique is adopted to transform the delay input system into a delay-free one. Second, the stochastic nonlinear mapping technique is developed to solve the symmetric and asymmetric output constraints in the system. Then, the adaptive neural controller is designed based on backstepping technique, such that the closed-loop systems are semi-globally practical finite-time stable (SGPFTS) in probability, and the tracking error converges to a small neighborhood of the origin after a finite period of time. Two simulation examples show the effectiveness of the proposed approach.