Skip to Main content Skip to Navigation
Conference papers

Optimizing MIMO control for fluidic pinball using machine learning

Abstract : We are looking for wake stabilization in a multi input multi output (MIMO) configuration. The wake results from an obstacle made by three cylinders in an incoming flow. The means of action are the cylinders rotation and the output is the velocity taken downstream. Previous studies have shown that high and low frequency forcing stabilize the wake, revealing the nonlinear interactions. Linear control being not applicable in our case we are looking for an optimal control law regarding drag reduction using genetic programming, a model free MLC approach. Genetic programming can explore a broad spectrum of laws, exploiting the nonlinearities, ranging from open loop control to closed loop control.
Complete list of metadata
Contributor : Limsi Publications Connect in order to contact the contributor
Submitted on : Friday, August 10, 2018 - 1:23:43 PM
Last modification on : Saturday, June 25, 2022 - 10:32:57 PM


  • HAL Id : hal-01856252, version 1


Guy Yoslan Cornejo Maceda, François Lusseyran, Bernd R. Noack, Marek Morzynski. Optimizing MIMO control for fluidic pinball using machine learning. Joint Annual Meeting of GAMM and DMV, Mar 2018, Munchen, Germany. ⟨hal-01856252⟩



Record views