A statistical learning strategy for closed-loop control of fluid flows
This provides a robust, computation-free control strategy for experimental fluid dynamics, though it appears incremental as it combines existing techniques like hashing and reinforcement learning.
The paper tackles the problem of controlling complex fluid flows using scarce, streaming sensor data by building a discrete embedding space and applying reinforcement learning, achieving good performance on a Lorenz 63 system and cylinder flow drag control.
This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system's dynamics. A control strategy is then learned using reinforcement learning once rewards relevant with respect to the control objective are identified. This method is designed for experimental configurations, requiring no computations nor prior knowledge of the system, and enjoys intrinsic robustness. It is illustrated on two systems: the control of the transitions of a Lorenz 63 dynamical system, and the control of the drag of a cylinder flow. The method is shown to perform well.