NESO Based Ultra-Local Model Predictive Control for Autonomous Vehicle Path Tracking and Roll Stability

This paper presents a model predictive controller for autonomous vehicles based on ultra-local models and a Nonlinear Extended State Observer (NESO) that simultaneously address path tracking performance and roll stability. The controller consists of several key components: first, ultra-local models are developed to represent the vehicle lateral error dynamics and roll dynamics. Next, a nonlinear extended state observer is designed to estimate integrated disturbances and unmodeled states within the ultra-local models. The parameters of the observer are optimized using the Butterfly Optimization Algorithm (BOA) in a simulation environment to enhance estimation accuracy. Finally, a model predictive control (MPC) scheme is implemented to minimize both lateral tracking error and roll angle, effectively addressing both path tracking performance and roll stability. The effectiveness of the proposed controller is validated through comprehensive comparative simulations conducted on a MATLAB/Simulink and Carsim co-simulation platform.