Robust Lateral Trajectory Tracking of Intelligent Vehicles by Using Fuzzy Adaptive Dynamic Model Predictive Control

To further improve the smoothness and robustness of lateral trajectory tracking for intelligent vehicles under complex operating conditions, this study proposes and experimentally validates a fuzzy adaptive dynamic model predictive control (FADMPC) strategy on the basis of model predictive control (MPC) framework. Thereinto, a three-degrees-of-freedom vehicle dynamics model serves as the predictive model, and a recursive least-squares algorithm with a forgetting factor is used to estimate tire cornering stiffness, thereby improving model fidelity. A whale optimization algorithm (WOA)–based adaptive horizon scheduler is devised to address the sensitivity of the prediction horizon to vehicle speed and road friction, and a fuzzy regulator adjusts the weight on the lateral displacement error in the objective function in real time. Hardware-in-the-loop tests on jointed and split-road surfaces show that compared with adaptive dynamic MPC, traditional MPC, and linear quadratic regulator, the FADMPC markedly reduces the lateral tracking error and enhances vehicle stability while maintaining performance under variations in tire cornering stiffness and localization noise and satisfying on-board real-time constraints. As a unified control framework that combines model adaptation with online scheduling, the FADMPC offers an engineering pathway to robust trajectory tracking and provides theoretical and technical bases for on-board deployment and large-scale application.