Adaptive Neural-Network-Based Dynamic Surface Control for Stochastic Interconnected Nonlinear Nonstrict-Feedback Systems With Dead Zone

In this paper, an adaptive neural-network-based dynamic surface control (DSC) method is proposed for a class of stochastic interconnected nonlinear nonstrict-feedback systems with unmeasurable states and dead zone input. First, an appropriate state observer is constructed to estimate the unmeasured state variables of the stochastic interconnected system. Then radial basis function neural networks combined with adaptive backstepping technique arc applied to model the unknown nonlinear system functions of the stochastic interconnected system; and the DSC method is adopted to ensure the computation burden is greatly reduced. Furthermore, the proposed controllers guarantee that the closed-loop stochastic interconnected system is semi-globally bounded stable in probability. In the end, two simulation examples are provided to show the effectiveness and practicability of the proposed control scheme.