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<p>With the rapid advancement of intelligent connected vehicles and autonomous driving technologies, vehicle lateral stability under high-speed or emergency steering conditions has become a critical safety concern. Traditional control strategies often exhibit limited performance in complex driving scenarios. This study proposes a novel co-simulation framework integrating digital twin technology with constrained Model Predictive Control (MPC) to enhance lateral stability and torque distribution. A high-fidelity digital twin model was constructed to improve real-time accuracy, while an MPC-based controller was designed to optimize handling under extreme conditions. Experimental results demonstrated improved performance: lateral stability error was reduced by 62.5%, and yaw rate error by 57.1%, compared to traditional methods. The key novelty lies in the dynamic, data-driven integration of the digital twin for real-time MPC optimization. These findings provide a robust theoretical foundation and technical support for intelligent vehicle development.</p>

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Data-driven vehicle stability control via co-simulation of digital twin and constrained MPC

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月17日

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  • Mu Lin,
  • Zhengwei Zhang,
  • Min Huang,
  • Yu Ding

摘要

With the rapid advancement of intelligent connected vehicles and autonomous driving technologies, vehicle lateral stability under high-speed or emergency steering conditions has become a critical safety concern. Traditional control strategies often exhibit limited performance in complex driving scenarios. This study proposes a novel co-simulation framework integrating digital twin technology with constrained Model Predictive Control (MPC) to enhance lateral stability and torque distribution. A high-fidelity digital twin model was constructed to improve real-time accuracy, while an MPC-based controller was designed to optimize handling under extreme conditions. Experimental results demonstrated improved performance: lateral stability error was reduced by 62.5%, and yaw rate error by 57.1%, compared to traditional methods. The key novelty lies in the dynamic, data-driven integration of the digital twin for real-time MPC optimization. These findings provide a robust theoretical foundation and technical support for intelligent vehicle development.