Finite-Time Sliding Mode Adaptive Control for Unknown Nonlinear Beam System With Neural Network Disturbance Observer
Tongming Huo, Xiaoli Li, Lianqing Zhu, Kang Wang
Abstract
This paper proposes a finite-time sliding mode adaptive tracking control strategy with a neural network disturbance observer to address problems of inertia uncertainties, unknown external disturbances, singularity, and chattering. First, an accurate mathematical model of the Bernoulli-Euler beam system is constructed using an in-domain constraint strategy for surface-mounted sensors. Next, a novel finite-time disturbance observer based on the Radial Basis Function (RBF) neural network is designed to handle uncertainties, enabling fast response. In addition, a novel integral sliding surface is designed to resolve the singularity issue in terminal sliding mode. Finally, an adaptive terminal sliding mode controller is developed based on the finite-time disturbance observer and integral sliding surface to ensure finite-time convergence of tracking errors; its stability is rigorously analyzed. Simulation and experimental results demonstrate that the proposed sliding mode control strategy achieves superior tracking performance and finite-time stability compared to traditional methods in industrial beam systems.