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Taming the fluidic pinball with artificial intelligence control

Abstract : The aim of this work is to develop a generic control strategy for nonlinear dynamics. This strategy is based on genetic programming, a machine learning technique for regression problems, that maps the sensor signals to the actuators in a unsupervised manner. It's a biological inspired method mimicking Darwin's natural selection: through and evolution process it derives a control law minimizing a given objective. Genetic programming has been applied to a DNS of a 2D fluidic mechanic system, the fluidic pinball. Several search spaces including control laws built from periodic functions, sensor signals and time-delay sensor signals have been explored. For the fluidic pinball genetic programming managed a 46% net drag saving, outperforming by 3.3% the best open-loop control law found with a parametric study. Our contribution has been the acceleration of the learning process by avoiding the evaluations of redundant control laws, thus improving the learning rate by a factor 3.
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Submitted on : Friday, November 29, 2019 - 7:25:51 PM
Last modification on : Saturday, June 25, 2022 - 10:41:58 PM


  • HAL Id : hal-02387544, version 1


Guy Yoslan Cornejo Maceda, Bernd R. Noack, François Lusseyran, Marek Morzynski, Luc Pastur, et al.. Taming the fluidic pinball with artificial intelligence control. European Fluid Mechanics Conference, Sep 2018, Vienne, Austria. ⟨hal-02387544⟩



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