Iterative Learning Control for Trailing-Edge Flap Lift Enhancement with Pulsed Blowing

Abstract

A novel iterative learning control algorithm was developed and applied to an active flow control problem. The technique used pulsed air jets applied to a trailing-edge flap to enhance the lift. The iterative learning control algorithm used position-based pressure measurements to update the actuation. The method was experimentally tested on a two-element high-lift wing in a low-speed wind tunnel. Compressed air and fast switching solenoid valves were used as actuators to excite the flow, and the pressure distribution around the chord of the wing was measured as a feedback control signal for the iterative learning controller. Experimental results showed that the actuation was able to delay the separation and increase the overall lift by over the angle of attack range and increase from 2.7 to 3.0 compared to the nonactuated case. By using the iterative learning control algorithms, the controller was able to track the target lift, and by using an optimum control algorithm with an extended reference, the controller was able to maximize the lift enhancement.