Adaptive neural network asymptotic tracking control for nonstrict feedback stochastic nonlinear systems.

Abstract

The adaptive neural network asymptotic tracking control issue of nonstrict feedback stochastic nonlinear systems is studied in our article by adopting backstepping algorithm. Compared with the existing research, the hypothesis about unknown virtual control coefficients (UVCC) is overcome in the control design. By using the bound estimation scheme and some smooth functions, associating with approximation-based neural network, the asymptotic tracking controller is recursively constructed. With the aid of Lyapunov function and beneficial inequalities, the asymptotic convergence character and stability with stochastic disturbance and unknown UVCC can be ensured. Finally, the theoretical finding is verified via a simulation example.