Experimental Demonstration of SDRL Controller for TS Wave Suppression with DBD Actuator
Babak Mohammadikalakoo, Sergio Garcia Villasol, Gabriele Salomone, Marios Kotsonis, and Nguyen Anh Khoa Doan

TL;DR
This paper demonstrates an experimental wind-tunnel implementation of a model-free deep reinforcement learning controller that adaptively suppresses Tollmien-Schlichting waves in a boundary layer using a plasma actuator, showing robustness and effectiveness.
Contribution
It presents the first experimental validation of a real-time, model-free SDRL controller for TS wave suppression in a boundary layer with practical sensing and actuation.
Findings
The SDRL controller consistently reduces downstream disturbance levels.
The controller maintains performance despite moderate variations in flow conditions.
Experimental results show spectral attenuation of TS wave content.
Abstract
An experimental wind-tunnel implementation of a model-free single-step deep reinforcement learning (SDRL) controller is presented for TS wave suppression in a flat plate boundary layer. The controller is deployed in a feedforward layout. The arrangement comprises an upstream reference microphone, a downstream error microphone, and a DBD plasma actuator located between them. The controller updates its policy online from the measured error signal and, in real time, adjusts the coefficients of a finite-impulse-response (FIR) filter that maps the reference signal to the actuation command. TS waves are artificially introduced by a second, upstream-located DBD trigger actuator identical in specification to the control actuator. The trigger actuator is driven with single-frequency, multi-frequency, or broadband white-noise inputs depending on the control cases. Experiments were carried out in…
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