Collaborative Control of Rear-Wheel Independent Drive Electric Vehicles During Tire Blowouts Using Broad-Extreme Reinforcement Learning: Simulation and Scaled Prototype Verification

Tire blowouts represent one of the most hazardous fault scenarios for electric vehicles (EVs). While collaborative active steering control (ASC) and direct yaw moment control (DYC) can theoretically maintain stability during these events, the strong coupling effects between them make controller design challenging. To address this, an adaptive control algorithm based on broad-extreme reinforcement learning (RL), named broad critic extreme actor (BCEA), is proposed. Compared to traditional controllers, the proposed BCEA architecture is simpler to design and demonstrates enhanced robustness. Crucially, it achieves significantly faster training speed than traditional RL methods such as deep deterministic policy gradient (DDPG). Both simulation and scaled prototype tests verify the ability of the BCEA-based controller to maintain vehicle stability during different types of tire blowout scenarios. Furthermore, compared to traditional RL methods, the training efficiency is improved by more than 80%. These results indicate that the proposed BCEA controller is a promising advancement for vehicle stability control under critical failure conditions.