Active turbulence control is a rapidly evolving, interdisciplinary field of research. In particular, closed-loop control with sensor information can offer distinct benefits over blind open-loop forcing. The range of current and future engineering applications of closed-loop turbulence control has truly epic proportions, including cars, trains, airplanes, jet noise, air conditioning, medical applications, wind turbines, combustors, and energy systems. A key feature, opportunity and technical challenge of closed-loop turbulence control is the inherent nonlinearity of the actuation response. For instance, excitation at a given frequency will affect also other frequencies, a phenomenon which is not accessible in any linear control framework. We propose a novel nonlinear control strategy with reduced-order modelling (ROM) for known actuation mechanisms and machine learning techniques for discovering unknown mechanisms. Bernd Noack will review successful studies for drag reduction of a bluff body and lift increase of an airfoil. Then, Eurika Kaiser will propose a novel cluster-based ROM to distil nonlinear mechanisms in an unsupervised manner. Last but not least, Thomas Duriez will present a novel machine learning control method which has proven itself remarkably effective for analytical examples, numerical simulations and the TUCOROM mixing layer control demonstrator.