Nash Bargaining Game-Based Coordinated Control of Path Tracking and Roll Stability for Distributed Drive Heavy-Duty Vehicles with Coupled Lateral–Roll Dynamics

Distributed drive heavy-duty vehicles with four-wheel independent driving and steering offer superior maneuverability, but their large mass and high center of gravity make them prone to rollover during cornering. Since lateral and roll motions are inherently coupled through lateral acceleration, yet existing path tracking controllers generally treat them separately and rely on fixed weightings, this study aims to coordinate path tracking and roll stability simultaneously. A reduced-order six-state prediction model is developed to explicitly capture the lateral–roll coupling, and a Nash bargaining game-based model predictive control strategy is proposed, in which two virtual players sharing the same control inputs are coordinated through a Pareto-optimal bargaining solution. A roll risk indicator-driven safety mapping law then adaptively adjusts the game weight online, forming a dual-layer protection architecture of game optimality plus safety coverage. TruckSim–MATLAB/Simulink co-simulation under extreme maneuvers shows that, compared with a tracking-only MPC, the proposed strategy maintains tracking accuracy while reducing the peak roll risk indicator by 45.2% and the maximum roll angle by 12.4%. The maximum absolute load transfer ratio is reduced from 1.0000 to 0.8944, so that the inner wheels remain clear of lift-off throughout the maneuver. The adaptive weight confirms the intended tracking-priority and stability-priority behavior, achieving a Pareto-optimal trade-off between the two objectives.