用历史部分数据重建全状态，让控制策略在少传感器时仍有效。
Domain Adaptation of Drag Reduction Policy to Partial Measurements
- 通过历史部分测量重建全状态，实现从完整传感到部分传感的策略迁移。
- 在模拟车体减阻任务中，仅用车身传感器数据就达到接近全传感的控制效果。
- 可自动确定最优历史长度，适合传感器受限的真实流体控制系统部署。
流体系统反馈控制因高维、非线性与多尺度动态而困难，需实时三维多分量测量。虽数字仿真中可实现全状态感知，但现实中传感器常受限于车辆表面，仅能获取部分测量。本文提出方法，将基于全状态测量训练的减阻控制策略适配至仅有部分测量的场景。在简化公路车辆的模拟环境中验证：强化学习在全状态下可通过尾流传感器优化控制；但真实应用中传感器有限，仅能采集车身数据。为此，我们设计领域特定特征迁移（DSFT）映射，从部分测量的历史序列重建全状态。利用该映射，仅基于局部数据即可推导出最优控制策略。此外，本方法可确定最优历史长度，并揭示最优控制策略结构，助力实际中传感器受限场景下的落地实施。
原文摘要 · Abstract (English)
Feedback control of fluid-based systems poses significant challenges due to their high-dimensional, nonlinear, and multiscale dynamics, which demand real-time, three-dimensional, multi-component measurements for sensing. While such measurements are feasible in digital simulations, they are often only partially accessible in the real world. In this paper, we propose a method to adapt feedback control policies obtained from full-state measurements to setups with only partial measurements. Our approach is demonstrated in a simulated environment by minimising the aerodynamic drag of a simplified road vehicle. Reinforcement learning algorithms can optimally solve this control task when trained on full-state measurements by placing sensors in the wake. However, in real-world applications, sensors are limited and typically only on the vehicle, providing only partial measurements. To address this, we propose to train a Domain Specific Feature Transfer (DSFT) map reconstructing the full measurements from the history of the partial measurements. By applying this map, we derive optimal policies based solely on partial data. Additionally, our method enables determination of the optimal history length and offers insights into the architecture of optimal control policies, facilitating their implementation in real-world environments with limited sensor information.
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