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Adaptive dynamic surface control of stochastic pure-feedback nonlinear systems including dynamic uncertainties

A more general class of stochastic nonlinear systems with unmodeled dynamics and dynamic disturbances are considered in this paper. Utilizing the approximation capability of neural networks and Young's inequality, an adaptive dynamic surface control scheme is proposed, which extends the approach of dynamic surface control to the stochastic systems. By theoretical analysis, it is shown that all signals in the closed-loop systems are bounded in probability. A simulation example further demonstrates the effectiveness of the control scheme.