Extensions of adaptive slope-seeking for active flow control

Abstract

To speed up gradient estimation in a slope-seeking controller two different modifications are proposed in this study. In a first approach, the gradient estimation is based on a locally identified black-box model. A further improvement is obtained by applying an extended Kalman filter to estimate the local gradient of an input—output map. Moreover, a simple method is outlined to adapt the search radius in the classical extremum- and slope-seeking approach to reduce the perturbations near the optimal state. To show the versatility of the slope-seeking controller for flow control applications two different wind tunnel experiments are considered, namely with a two-dimensional bluff body and a generic three-dimensional car model (Ahmed body).