Active suspension systems are critical for enhancing vehicle comfort, safety, and stability, yet their performance is often limited by fixed hardware designs and control strategies that cannot adapt to uncertain and dynamic operating conditions. Recent advances in digital twins (DTs) and deep reinforcement learning (DRL) offer new opportunities for real-time, data-driven optimization across a vehicle's lifecycle. However, integrating these technologies into a unified framework remains an open challenge. This work presents a DT-based control co-design (CCD) framework for full-vehicle active suspensions using multi-generation design concepts. By integrating automatic differentiation into DRL, we jointly optimize physical suspension components and control policies under varying driver behaviors and environmental uncertainties. DRL also addresses the challenge of partial observability, where only limited states can be sensed and fed back to the controller, by learning optimal control actions directly from available sensor information. The framework incorporates model updating with quantile learning to capture data uncertainty, enabling real-time decision-making and adaptive learning from digital-physical interactions. The approach demonstrates personalized optimization of suspension systems under two distinct driving settings (mild and aggressive). Results show that the optimized systems achieve smoother trajectories and reduce control efforts by approximately 43% and 52% for mild and aggressive, respectively, while maintaining ride comfort and stability. Contributions include: developing a DT-enabled CCD framework integrating DRL and uncertainty-aware model updating for full-vehicle active suspensions, introducing a multi-generation design strategy for self-improving systems, and demonstrating personalized optimization of active suspension systems for distinct driver types.

翻译：主动悬架系统对于提升车辆舒适性、安全性和稳定性至关重要，但其性能常受限于固定的硬件设计和无法适应不确定动态工况的控制策略。数字孪生与深度强化学习的最新进展为车辆全生命周期内实时数据驱动优化提供了新机遇。然而，将这些技术整合至统一框架仍面临挑战。本研究提出一种基于数字孪生的整车主动悬架控制协同设计框架，采用多代设计理念。通过将自动微分融入深度强化学习，我们在不同驾驶行为和环境不确定性下联合优化物理悬架部件与控制策略。深度强化学习通过直接从可用传感器信息学习最优控制动作，解决了部分可观测性挑战（即仅有限状态可被感知并反馈至控制器）。该框架结合基于分位数学习的模型更新以捕捉数据不确定性，实现数字-物理交互中的实时决策与自适应学习。该方法在两种典型驾驶场景（温和型与激进型）下展示了悬架系统的个性化优化。结果表明：优化后的系统在保持乘坐舒适性与稳定性的同时，实现了更平滑的轨迹，并将控制能耗分别降低约43%（温和型）和52%（激进型）。主要贡献包括：开发了集成深度强化学习与不确定性感知模型更新的数字孪生控制协同设计框架，提出了面向自优化系统的多代设计策略，并实现了针对不同驾驶类型的主动悬架系统个性化优化。