Henning, L.Becker, R.Feuerbach, G.Muminovic, R.King, RudibertBrunn, A.Nitsche, W.2019-01-082019-01-0820080959-6518https://depositonce.tu-berlin.de/handle/11303/8873http://dx.doi.org/10.14279/depositonce-8002Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.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).en620 Ingenieurwissenschaften und zugeordnete Tätigkeitenactive flow controlextremum-seeking controlslope-seeking controlKalman filterleast squareAhmed bodybluff bodyArticle2041-3041