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Close loop control of complex systems : low order linear model approach and nonlinear model free approach
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.
In a first part I will present how it is possible to look for linear behaviours in non-linear systems, and use this in order to control them. Investigating a free shear layer we devise a first order model single input/single output model. The feed-back control of the identified system requires fighting the time- delay due to convection from actuator to sensor. To this end we show how a Smith predictor can be effectively used in order to achieve feed-back tracking of turbulence level in the shear layer.
In a second part I will present a novel machine learning attractor control method which has proven itself remarkably effective for analytical nonlinear examples, numerical simulations and the TUCOROM mixing layer control demonstrator. This method is proposed as a generic model-free approach to control complex systems.
