Designs of Feedback Controllers for Fluid Flows Based On Model Predictive Control and Regression Analysis

Yasuo Sasaki and Daisuke Tsubakino
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Yasuo Sasaki: Department of Aerospace Engineering, Graduate School of Engineering, Nagoya University, Nagoya, Aichi 464-8603, Japan
Daisuke Tsubakino: Department of Aerospace Engineering, Graduate School of Engineering, Nagoya University, Nagoya, Aichi 464-8603, Japan

Energies, 2020, vol. 13, issue 6, 1-24

Abstract: Complexity of online computation is a drawback of model predictive control (MPC) when applied to the Navier–Stokes equations. To reduce the computational complexity, we propose a method to approximate the MPC with an explicit control law by using regression analysis. In this paper, we extracted two state-feedback control laws and two output-feedback control laws for flow around a cylinder as a benchmark. The state-feedback control laws that feed back different quantities to each other were extracted by ridge regression, and the two output-feedback control laws, whose measurement output is the surface pressure, were extracted by ridge regression and Gaussian process regression. In numerical simulations, the state-feedback control laws were able to suppress vortex shedding almost completely. While the output-feedback control laws could not suppress vortex shedding completely, they moderately improved the drag of the cylinder. Moreover, we confirmed that these control laws have some degree of robustness to the change in the Reynolds number. The computation times of the control input in all the extracted control laws were considerably shorter than that of the MPC.

Keywords: active flow control; model predictive control; adjoint-based method; ridge regression; Gaussian process regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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