AI 中文总结
研究如何对高维动态系统进行实时最优控制，利用SHRED-ROM模型，通过专家示例训练，以有限传感器读数合成闭环控制器，减轻维度诅咒，还引入传感器预测器，经三个高维案例评估，该策略表现良好。
AI 中文摘要
实时控制跨多种场景的动态系统对于实现自适应控制策略、确保稳定性和效率至关重要。传统最优控制问题通常需要多次系统模拟，计算量很大。本文利用基于浅循环解码器网络的降阶建模（SHRED-ROM）来合成实时闭环控制器，仅依赖有限的状态传感器读数。在专家演示给出的几个最优示例上训练模型后，SHRED-ROM在新场景中通过有效的分布式控制动作模仿专家行为，减轻维度诅咒。还合成了传感器预测器以在潜在层面闭合回路，减轻传感器故障或延迟。最后在三个处理参数密度控制或流体流动控制的具有挑战性的高维案例上评估了所提出的最优控制策略的性能。
英文摘要
Controlling dynamical systems in real-time across multiple scenarios is critical to enabling adaptive control strategies, ensuring stability and efficiency. However, to tailor control actions in response to varying scenarios, traditional optimal control problems typically require several system simulations, which are often computationally demanding due to the high-dimensionality of the underlying spatio-temporal dynamics. In this work, we exploit SHallow REcurrent Decoder networks-based Reduced Order Modeling (SHRED-ROM) to synthesize a real-time closed-loop controller for high-dimensional and parametric dynamics, relying solely on limited state sensor readings. After training the model on a few optimal examples given by an expert demonstrator, SHRED-ROM mimics the expert behavior with effective distributed control actions in new scenarios, alleviating the curse of dimensionality. Moreover, a sensor forecaster is synthesized and used to close the loop at the latent level, thus efficiently mitigating possible sensor failures or delays. The performance of the proposed optimal control strategy is finally assessed on three challenging high-dimensional cases dealing with either parametric density control or fluid flow control.
