Adaptive Neural Network-Based Finite-Time Control of Stochastic Nonlinear Systems with Unmodeled Dynamics and Input Time Delay
Li Fengyan, Zhang Xinghui, Ancai Zhang · 2025
In this paper, we consider a class of stochastic nonlinear systems with input delay, unmodeled dynamics, and dynamic uncertainties. Firstly, by utilizing the approximation capability of radial basis function neural networks, the approach eliminates the need for upper bounds on the nonlinear delay term. Dynamic signal processing is introduced to handle the unmodeled dynamics. Secondly, an auxiliary function is designed to compensate for the effect of input delays. By applying finite-time Lyapunov control theory and the backstepping method, a finite-time adaptive fuzzy controller is proposed. This controller ensures that the tracking error converges to a predefined region within a finite time and guarantees the boundedness of all signals in the closed-loop system. Finally, a simulation example is included to verify the validity and feasibility of the control method.