Machine-learned control-oriented flow estimation for multiactuator multi-sensor systems exemplified for the fluidic pinball

TL;DR

This paper introduces a novel machine learning-based control-oriented flow estimation method for multiactuator multi-sensor systems, demonstrated on the fluidic pinball, outperforming linear models especially in chaotic regimes.

Contribution

It presents the first control-oriented flow estimation approach using machine learning for MIMO plants, combining simple nonlinear mapping, database interpolation, and deep neural networks.

Findings

Machine learning methods outperform linear models in flow estimation.

DNN performs better than kNN in chaotic flow conditions.

The approach is generalizable to closed-loop flow control systems.

Abstract

We propose the first machine-learned control-oriented flow estimation for multiple-input multiple-output plants. Starting point is constant actuation with open-loop actuation commands leading to a database with simultaneously recorded actuation commands, sensor signals and flow fields. A key enabler is an estimator input vector comprising sensor signals and actuation commands. The mapping from the sensor signals and actuation commands to the flow fields is realized in an analytically simple, data-centric and general nonlinear approach. The analytically simple estimator generalizes Linear Stochastic Estimation (LSE) for actuation commands. The data-centric approach yields flow fields from estimator inputs by interpolating from the database -- similar to Loiseau et al. (2018) for unforced flow. The interpolation is performed with k Nearest Neighbors (kNN). The general global nonlinear mapping from inputs to flow fields is obtained from a Deep Neural Network (DNN) via an iterative training approach. The estimator comparison is performed for the fluidic pinball plant, which is a multiple-input, multiple-output wake control benchmark (Deng et al. 2020) featuring rich dynamics under steady controls. We conclude that the machine learning methods clearly outperform the linear model. The performance of kNN and DNN estimators are comparable for periodic dynamics. Yet, DNN performs consistently better when the flow is chaotic. Moreover, a thorough comparison regarding to the complexity, computational cost, and prediction accuracy is presented to demonstrate the relative merits of each estimator. The proposed method can be generalized for closed-loop flow control plants.