Lionel Mathelin from LIMSI-CNRS will give a talk about Flow Control and Uncertainty Quantification.

Title:

An Occam’s razor paradigm for the control of complex systems

Abstract:

Efficient control of complex systems involves ingredients such as robustness, reduced order modeling, observation and command. In this talk, we will discuss some of these ingredients with the concern that one only has a limited information about the system at hand. A stochastic identification strategy, relying on sparsity exploiting techniques, will first be discussed. It allows modeling the uncertain parameters of the system and accurately quantifying the uncertainty associated with quantities of interest. An observer for fluid flows, dedicated to an experimental context, will next be presented. It relies on an offline/online strategy. An approximation basis of the field to be estimated is first learnt (offline step) using some knowledge on the flow (PIV, simulations, etc.) and information provided by a few, wall-mounted, sensors. Online, the estimation is achieved by sparse recovery from the sole sensors information. Finally, a controller is derived based on an optimal control approach. Exploiting the sparsity of the response surface of the control command allows, once again, performance and efficiency.