Neural-network-based output-feedback adaptive dynamic surface control for a class of stochastic nonlinear time-delay systems with unknown control directions

This paper focuses on the problem of output-feedback adaptive stabilization for a class of stochastic nonlinear time-delay systems with unknown control directions. First, based on a linear state transformation, the unknown control coefficients are lumped together and the original system is transformed to a new system for which control design becomes feasible. Then, after the introduction of an observer, an adaptive neural network (NN) output-feedback control scheme is presented for such systems by using dynamic surface control (DSC) technique and Lyapunov-Krasovskii method. The designed controller ensures that all the signals in the closed-loop system are 4-Moment (or 2-Moment) semi-globally uniformly ultimately bounded. Finally, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed control design.

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