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

Nonlinear Dynamics (Impact Factor: 3.01). 01/2012; 77:267-274. 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.

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