For airborne air-breathing systems, flow separation is a critical factor in efficiency, ease of piloting, and performance capability. Flow separation, or stall, leads to increased drag, decreased lift, and unpredictable vibrations due to unsteadiness. Small aerial vehicle flight is one such application. On these systems, effective control of stall could provide greater maneuverability and performance, and lessened vibration during optical image capture. Separated flow is a macro-scale phenomenon and is governed by complex flow interactions but can be controlled by micro-scale actuation. For many decades, passive control methods such as vortex generators and surface roughness have been designed to mitigate separation under conditions of steady, design-point operation. Not until recently, however, has the emergence of closed loop methods enabled control of separation that is able to respond as flow conditions change. Advances in microprocessor technology have now enabled the use of sophisticated adaptive control methods that achieve separation control with linear time-varying models. While adaptive control methods have improved upon passive and open-loop techniques for steady operation, nonlinear adaptive control has yet to be demonstrated for the dynamic flow conditions of agile flight. Adaptive Sampling Based Model Predictive Control (Adaptive SBMPC), a novel approach to nonlinear Model Predictive Control, is presented. Adaptive SBMPC applies the Minimal Resource Allocation Network algorithm for nonlinear system identification and the Sampling Based Model Predictive Optimization algorithm to achieve effective feedback control of flow separation. By introducing a computationally efficient nonlinear approach to the adaptive control of separation, this research experimentally demonstrates real time control of flow separation for a range of flow conditions.
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