Adaptive Control of Synthetic Jet Arrays with Unknown Nonlinearities

Active flow control using synthetic jet actuators has been the subject of significant research in recent years. Modulated input signals to achieve flow control objectives have been shown to be highly nonlinear and complex. Developing a comprehensive, wind tunnel-validated model of synthetic jet actuators in a single application is technically challenging and expensive. Given the varied and growing list of synthetic jet applications, it is essential that adaptive control techniques be developed that achieve performance objectives without requiring comprehensive models. In Phase I, Barron Associates and its research partners at the University of Virginia and the University of Wyoming developed adaptive control algorithms for arrays of synthetic jet actuators based on a proven adaptive inverse control technique. We successfully demonstrated these algorithms in simulation and designed a Phase II wind tunnel experiment. In Phase II, we will: (1) expand and formalize the control theory; (2) implement the control algorithms in real-time hardware; (3) fabricate an innovative wind tunnel model with integrated synthetic jet actuators, and; (4) conduct wind tunnel experiments to demonstrate adaptive control to meet both flow separation mitigation and virtual shaping objectives. The wind tunnel experiments will be conducted at the U.S. Air Force Academy.