Optimal Multi-Objective Adaptive Coordinated Control via Hierarchical MPC Framework for Autonomous Vehicles

This paper introduces a novel minimum-order, multi-objective centralized MPC control architecture for autonomous vehicles. The contributions are fourfold. First, we propose a hierarchical architecture that integrates path-tracking, speed control, and stability control based on a minimum-order predictive model. Second, stability control is dynamically activated and relaxed using an adaptive weight, based on a stability index. Third, speed control employs an adaptive weight to improve robustness, reduce overshoot and oscillations, and enhance energy efficiency. Fourth, path-tracking control is enhanced with an adaptive weighting scheme that considers lateral error, road adherence, and curvature to ensure smooth convergence and prevent oscillations, while also improving accuracy under uncertain and low-adherence conditions. The architecture is validated in a joint simulation between Simulink/Matlab and SCANeR Studio vehicle dynamics simulator. Our findings demonstrate the effectiveness of the architecture in enhancing stability and comfort at low runtime, while maintaining path-tracking precision and speed control robustness, at high speeds, high curvature, and low adhesion.