# Adaptive Neural Control for a Class of Stochastic Nonlinear Systems with Unknown Parameters, Unknown Nonlinear Functions and Stochastic Disturbances

## TL;DR

This paper develops an adaptive neural control method for stochastic nonlinear systems with unknown parameters and disturbances, ensuring global stability and demonstrating effectiveness through simulations.

## Contribution

It introduces a novel adaptive neural network controller using backstepping and Lyapunov methods for complex stochastic systems with unknown elements.

## Key findings

- Achieves global asymptotic stability in probability.
- Effectively handles unknown nonlinearities and disturbances.
- Validated by simulation results.

## Abstract

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.