Robust neural output-feedback stabilization for stochastic nonlinear process with time-varying delay and unknown dead zone

This article investigates the output-feedback control ofa class of stochastic nonlinear system with time-varying delay and unknown dead zone. Arobust neural stabilizing algorithm is proposed by using the circle criterion, the NNs approximation and the MLP (minimum learning parameter) technique. In the scheme, the nonlinear observeris first designed to estimate the unmeasurable states and the assumption “linear growthof the nonlinear function is released. Furthermore, the uncertainty of the wholesystem (including the perturbation of time-varying delay) is lumped and compensatedby employing one RBF NNs (radial basis function neural networks). Though, only two weight-norm related parameters are required tobe updated online for the merit of the MLP technique. And the gain-inversion relatedadaptive law is targetly designed to mitigate the adverse effect of unknown dead zone.Comparing with the previous work, the proposed algorithm obtains the advantage: a conciseform and easy to implementation due to its less computational burden. The theoretical analysis andcomparison example demonstrate the substantial effectiveness of the proposed scheme.