The Direct Yaw Moment Control (DYC) system plays a vital role in steady-state handling and serves as a fundamental component for trajectory tracking and active safety mechanisms during high-speed driving. In distributed-drive electric vehicles, DYC leverages the full potential of a four in-wheel motor setup by independently controlling the driving forces across all wheels. This allows for the transition from passive intervention under extreme conditions—when the vehicle nears instability—into proactive control, enhancing safety. However, in real-world scenarios, discrepancies between model parameters and actual vehicle behaviour, along with external disturbances and parameter variations, pose significant challenges to the controller’s adaptive performance. To improve lateral stability control in such vehicles, this paper introduces a robust adaptive controller based on H₂/H∞ theory with prescribed performance for direct yaw moment regulation. This study aims to minimize the impact of external disturbances and input noise on the vehicle's dynamic control system. By introducing an online parameter adjustment mechanism and dynamically correcting key vehicle parameters, the feedback gain matrix is updated in real time using the LPV gain scheduling method, with the longitudinal vehicle speed treated as a time-varying parameter. The algorithm ensures the controller's adaptability and robustness, effectively responding to environmental changes and resisting noise. Simulation experiments show that the algorithm performs well under various operating conditions, significantly improving controller performance and reducing system uncertainties, with promising application prospects.