Taming shear flows with gradient-enriched machine learning control

We propose an automated gradient-enriched machine learning control (gMLC, [1]) for fast learning of multiple-input multiple-output feedback control laws directly from the plant. gMLC is demonstrated on three shear flows: A DNS of the flow past a cluster of three rotating cylinders—the fluidic pinball—an open cavity flow experiment, and a smart skin separation control experiment. The fluidic pinball has been stabilized reducing the residual fluctuation energy up to 80%. For the open cavity, a mode-switching regime is fully stabilized with low actuation power [2]. For both cases, the need of feedback for the stabilization has been demonstrated. gMLC even learned smart skin separation control with 60 actuation commands and 54 sensors in few hours testing time (more: S. Li in this session). Key enablers are automated machine learning algorithms augmented with intermediate gradient steps: explorative gradient method for parametric optimization and gMLC for feedback law optimization. gMLC learns control laws significantly faster than previously employed feedback control strategies.