10:20   Classical and Machine methods in flow control I Chair: Abel-John Buchner Active control of pitching airfoil flow based on deep reinforcement learning Shixiong Zhang, Honglei Bai Abstract: Dynamic stall can be frequently encountered in the aerospace engineering applications e.g., rotorcraft, small manned aerial vehicles (MAVs), and pitching airfoils. We present an experimental work on the active control of the flow over a pitching NACA0012 airfoil, based on deep reinforcement learning (DRL), aiming to improve the control effect and seek the optimal control parameters. The experiments are conducted in a low-speed wind tunnel at Reynolds number Re = U_∞c/ν = 1.6 × 105, where U_∞ is incoming flow velocity, c is the chord length of the airfoil and ν is the kinematic viscosity of air. The reduced frequency k = πfc/U_∞ is 0.075, where f is the oscillation frequency of the pitching airfoil. The function of the pitching motion is defined by α(t) = 10+15sin⁡(2πft), where α is the angle of attack and t is time. The pulsation jet with 0.7%c width covering 73% of spanwise length of the airfoil is located at 5%c from the leading edge with an angle of 45° relative to the chord line. The control parameters include the driving pressure P_j^* (=P_j/P_a, where P_a is the atmospheric pressure), jet frequency F_j^* (= F_j X_e/U_∞, where X_e is the distance between the exit of the jet and the trailing edge of the airfoil) and duty cycle of the jet DC. The results show that the time-mean lift coefficients C ̅_L of the airfoil increase significantly. The maximum C ̅_L is up to 0.78 at step = 577, increasing by 59% of C ̅_Lo. The corresponding optimal control parameters are P_j^* = 2.31, F_j^* = 0.61 and DC = 58.5%. However, the convergence of F_j^* is not as well as the other parameters, which may be determined by the reward functions and the parameters of the DRL. It is indicated that the maximum lift coefficient is increased and the dynamic stall angle is delayed to 18° by the DRL-based control. The lift coefficient at α > 18° in the upstroke and in the down stroke increases significantly. Model-Free Displacement Controller for a Tap-Water-Driven Artificial Muscle with Fuzzy Logic-Based Time-Varying Parameter Design Ayaka Kosugi, Satoshi Tsuruhara, Kazuhisa Ito Abstract: McKibben-type artificial muscle has several advantages for practical application; however, it is difficult to control with high precision due to its hysteresis characteristics. For this problem, model-free control (MFC) is a data-driven controller that can handle such a nonlinear system. On the other hand, it shows significant performance deterioration with rapid changes in reference value. In this study, to solve this problem, a time-varying parameter update law, which is a combination of MFC and fuzzy logic, is proposed in this study. The updated parameter plays an important role in MFC, which is a denominator of the control input equation. While the conventional adaptive control method has been widely known to have lower control performance in the transient response, the proposed method allows for designing update laws heuristically, and it is confirmed that it suppresses the overshoot, and maintains the performance in the steady-state response via experimental validation using the tap-water-driven artificial muscle.

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