Adaptive Neural Control for a Class of Stochastic Nonlinear Systems with Unknown Parameters, Unknown Nonlinear Functions and Stochastic Disturbances
It addresses the control problem for a class of stochastic nonlinear systems with multiple uncertainties, but the method is an incremental combination of existing techniques (neural networks, backstepping, adaptive bounding).
This paper proposes an adaptive neural control method for strict-feedback stochastic nonlinear systems with unknown parameters, nonlinear functions, and disturbances, achieving global asymptotic stability in probability. Simulation results confirm the effectiveness of the approach.
In this paper, adaptive neural control (ANC) is investigated for a class of strict-feedback nonlinear stochastic systems with unknown parameters, unknown nonlinear functions and stochastic disturbances. The new controller of adaptive neural network with state feedback is presented by using a universal approximation of radial basis function neural network and backstepping. An adaptive neural network state-feedback controller is designed by constructing a suitable Lyapunov function. Adaptive bounding design technique is used to deal with the unknown nonlinear functions and unknown parameters. It is shown that, the global asymptotically stable in probability can be achieved for the closed-loop system. The simulation results are presented to demonstrate the effectiveness of the proposed control strategy in the presence of unknown parameters, unknown nonlinear functions and stochastic disturbances.