Adaptive neural control for strict-feedback stochastic nonlinear systems with time-delay

ArticleinNonlinear Dynamics 77(1):267-274 · February 2012with12 Reads
Impact Factor: 2.85 · DOI: 10.1016/j.neucom.2011.08.020 · Source: DBLP

    Abstract

    The problem of robust stabilization is investigated for strict-feedback stochastic nonlinear time-delay systems via adaptive neural network approach. Neural networks are used to model the unknown packaged functions, then the adaptive neural control law is constructed by a novel Lyapunov–Krasovskii functional and backstepping. It is shown that all the variables in the closed-loop system are semi-globally stochastic bounded, and the state variables converge into a small neighborhood in the sense of probability.