A Model-Free Adaptive Parameter Tuning Algorithm Under a Multi-Objective Optimization Model and Its Application in the Motion Control of Intelligent Vehicles

This paper is based on model-free adaptive control with data-driven control and explores its application in the motion control of intelligent vehicles. Based on experiments investigating the feasibility and effectiveness of standard control algorithms, the paper examines four key parameters and error control metrics in the control algorithm. Using methods such as factor perturbation analysis, linear fitting, and correlation analysis, parameter sensitivity analysis was conducted. The parameter tuning problem was transformed into a multi-objective optimisation problem, and an optimisation model was established. An enhanced multi-objective genetic evolutionary algorithm (MO-GA) based on NSGA-II is proposed to address the multi-objective optimization problem. Through three experimental scenarios under the Hammerstein nonlinear system, the effectiveness of the improved algorithm was verified. Based on the MatlabR2024b/Carsim2020 platform, joint simulation experiments were conducted on the Audi A6-Avant models under dual-lane shifting and serpentine operating conditions, verifying the portability of the improved algorithm.