Designs of Feedback Controllers for Fluid Flows Based On Model Predictive Control and Regression Analysis
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 extr...
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| Format: | Article |
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MDPI AG
2020-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/6/1325 |
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| author | Yasuo Sasaki Daisuke Tsubakino |
| author_facet | Yasuo Sasaki Daisuke Tsubakino |
| author_sort | Yasuo Sasaki |
| collection | DOAJ |
| description | 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. |
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| format | Article |
| id | doaj.art-544a8b19609e4aa4b05d16b3c156bcb9 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T00:50:59Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-544a8b19609e4aa4b05d16b3c156bcb92022-12-22T03:09:53ZengMDPI AGEnergies1996-10732020-03-01136132510.3390/en13061325en13061325Designs of Feedback Controllers for Fluid Flows Based On Model Predictive Control and Regression AnalysisYasuo Sasaki0Daisuke Tsubakino1Department of Aerospace Engineering, Graduate School of Engineering, Nagoya University, Nagoya, Aichi 464-8603, JapanDepartment of Aerospace Engineering, Graduate School of Engineering, Nagoya University, Nagoya, Aichi 464-8603, JapanComplexity 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.https://www.mdpi.com/1996-1073/13/6/1325active flow controlmodel predictive controladjoint-based methodridge regressiongaussian process regression |
| spellingShingle | Yasuo Sasaki Daisuke Tsubakino Designs of Feedback Controllers for Fluid Flows Based On Model Predictive Control and Regression Analysis Energies active flow control model predictive control adjoint-based method ridge regression gaussian process regression |
| title | Designs of Feedback Controllers for Fluid Flows Based On Model Predictive Control and Regression Analysis |
| title_full | Designs of Feedback Controllers for Fluid Flows Based On Model Predictive Control and Regression Analysis |
| title_fullStr | Designs of Feedback Controllers for Fluid Flows Based On Model Predictive Control and Regression Analysis |
| title_full_unstemmed | Designs of Feedback Controllers for Fluid Flows Based On Model Predictive Control and Regression Analysis |
| title_short | Designs of Feedback Controllers for Fluid Flows Based On Model Predictive Control and Regression Analysis |
| title_sort | designs of feedback controllers for fluid flows based on model predictive control and regression analysis |
| topic | active flow control model predictive control adjoint-based method ridge regression gaussian process regression |
| url | https://www.mdpi.com/1996-1073/13/6/1325 |
| work_keys_str_mv | AT yasuosasaki designsoffeedbackcontrollersforfluidflowsbasedonmodelpredictivecontrolandregressionanalysis AT daisuketsubakino designsoffeedbackcontrollersforfluidflowsbasedonmodelpredictivecontrolandregressionanalysis |