Feedback control of transitional shear flows: sensor selection for performance recovery

The choice and placement of sensors actuators is an essential factor determining the performance that can be realized using feedback control. This determination especially important, but difficult, in context controlling transitional flows. highly non-normal nature linearized Navier–Stokes equations makes flow sensitive to small perturbations, with potentially drastic consequences on closed-loop Full-information controllers, such as linear quadratic regulator (LQR), have demonstrated some success reducing transient energy growth suppressing transition; however, sensor-based output controllers comparable been difficult realize. In this study, we propose two methods for sensor selection enable recover full-information control performance: one based a sparse controller synthesis approach, balanced truncation procedure model reduction. Both approaches are investigated within nonlinear simulations sub-critical channel blowing suction actuation at walls. We find configurations identified by both allow static LQR performance, transition. Further, our results indicate resulting exhibit robustness Reynolds number variations.