Adaptive NN feedback control for a class of stochastic nonlinear systems with uncertain measurement functions

This paper investigates the feedback controller design issue for a class of stochastic nonlinear systems whose state cannot be measured accurately. By generalizing the notion of HWMD to the uncertain case, certain assumptions are imposed on the uncertain powers. With a series of neural network (NN) functions to deal with the unknown nonlinear terms, an adaptive feedback controller is designed by adopt in the adding one power integrator technique. The controller assures that all signals in the closed-loop system are bounded in probability. Furthermore, the effectiveness of the proposed control approach is verified by numerical simulation