Barrier Function-Based Neural Network Adaptive Integral Sliding Mode Control for Multiaxle Steering Vehicles’ Lateral Dynamics Control

Abstract

To achieve precise lateral dynamics control for electric and autonomous vehicles under system model uncertainties and unknown external disturbances, this article introduces a novel barrier function (BF)-based neural network (NN) adaptive integral sliding mode control (ISMC) for multiaxle independent steering vehicles (MISVs). Initially, NNs are designed to approximate the unknown parameters associated with uncertainties in the MISV dynamics model. Subsequently, an adaptive ISMC based on BF is developed to ensure robust vehicle state tracking. Unlike traditional adaptive sliding mode controllers, the proposed approach guarantees rapid convergence of variables within a finite time, even without prior knowledge of the upper bounds of unknown external disturbances, thereby significantly reducing vibration and oscillation phenomena. Finally, the Lyapunov stability theory is applied to rigorously demonstrate that the closed-loop system of the MISV remains stable within finite time. The efficacy of the proposed controller is validated through hardware-in-the-loop (HIL) experiments, with results indicating a reduction in maximum tracking errors for the desired yaw rate and sideslip angle by 8.24% and 40.44%, respectively, compared to the conventional control method.