Towards Data-Driven Real-Time Hybrid Simulation: Adaptive Modeling of Control Plants
We present a method for control in real-time hybrid simulation (RTHS) that relies exclusively on data processing. Our approach bypasses conventional control techniques, which presume availability of a mathematical model for the description of the control plant (e.g., the transfer system and the expe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-09-01
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| Series: | Frontiers in Built Environment |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fbuil.2020.570947/full |
| _version_ | 1818929693549658112 |
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| author | Thomas Simpson Vasilis K. Dertimanis Eleni N. Chatzi |
| author_facet | Thomas Simpson Vasilis K. Dertimanis Eleni N. Chatzi |
| author_sort | Thomas Simpson |
| collection | DOAJ |
| description | We present a method for control in real-time hybrid simulation (RTHS) that relies exclusively on data processing. Our approach bypasses conventional control techniques, which presume availability of a mathematical model for the description of the control plant (e.g., the transfer system and the experimental substructure) and applies a simple plug 'n play framework for tuning of an adaptive inverse controller for use in a feedforward manner, avoiding thus any feedback loops. Our methodology involves (i) a forward adaptation part, in which a noise-free estimate of the control plant's dynamics is derived; (ii) an inverse adaptation part that performs estimation of the inverse controller; and (iii) the integration of a standard polynomial extrapolation algorithm for the compensation of the delay. One particular advantage of the method is that it requires tuning of a limited set of hyper-parameters (essentially three) for proper adaptation. The efficacy of our framework is assessed via implementation on a virtual RTHS (vRTHS) benchmark problem that was recently made available to the community. The attained results indicate that data-driven RTHS may form a competitive alternative to conventional control. |
| first_indexed | 2024-12-20T03:48:52Z |
| format | Article |
| id | doaj.art-d4cf3ce3b0f742d69f39f3736d01bf34 |
| institution | Directory Open Access Journal |
| issn | 2297-3362 |
| language | English |
| last_indexed | 2024-12-20T03:48:52Z |
| publishDate | 2020-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Built Environment |
| spelling | doaj.art-d4cf3ce3b0f742d69f39f3736d01bf342022-12-21T19:54:32ZengFrontiers Media S.A.Frontiers in Built Environment2297-33622020-09-01610.3389/fbuil.2020.570947570947Towards Data-Driven Real-Time Hybrid Simulation: Adaptive Modeling of Control PlantsThomas SimpsonVasilis K. DertimanisEleni N. ChatziWe present a method for control in real-time hybrid simulation (RTHS) that relies exclusively on data processing. Our approach bypasses conventional control techniques, which presume availability of a mathematical model for the description of the control plant (e.g., the transfer system and the experimental substructure) and applies a simple plug 'n play framework for tuning of an adaptive inverse controller for use in a feedforward manner, avoiding thus any feedback loops. Our methodology involves (i) a forward adaptation part, in which a noise-free estimate of the control plant's dynamics is derived; (ii) an inverse adaptation part that performs estimation of the inverse controller; and (iii) the integration of a standard polynomial extrapolation algorithm for the compensation of the delay. One particular advantage of the method is that it requires tuning of a limited set of hyper-parameters (essentially three) for proper adaptation. The efficacy of our framework is assessed via implementation on a virtual RTHS (vRTHS) benchmark problem that was recently made available to the community. The attained results indicate that data-driven RTHS may form a competitive alternative to conventional control.https://www.frontiersin.org/article/10.3389/fbuil.2020.570947/fullreal-time hybrid simulationadaptive signal processingadaptive inverse controlfeedforwarddecorrelated LMSDCT-LMS |
| spellingShingle | Thomas Simpson Vasilis K. Dertimanis Eleni N. Chatzi Towards Data-Driven Real-Time Hybrid Simulation: Adaptive Modeling of Control Plants Frontiers in Built Environment real-time hybrid simulation adaptive signal processing adaptive inverse control feedforward decorrelated LMS DCT-LMS |
| title | Towards Data-Driven Real-Time Hybrid Simulation: Adaptive Modeling of Control Plants |
| title_full | Towards Data-Driven Real-Time Hybrid Simulation: Adaptive Modeling of Control Plants |
| title_fullStr | Towards Data-Driven Real-Time Hybrid Simulation: Adaptive Modeling of Control Plants |
| title_full_unstemmed | Towards Data-Driven Real-Time Hybrid Simulation: Adaptive Modeling of Control Plants |
| title_short | Towards Data-Driven Real-Time Hybrid Simulation: Adaptive Modeling of Control Plants |
| title_sort | towards data driven real time hybrid simulation adaptive modeling of control plants |
| topic | real-time hybrid simulation adaptive signal processing adaptive inverse control feedforward decorrelated LMS DCT-LMS |
| url | https://www.frontiersin.org/article/10.3389/fbuil.2020.570947/full |
| work_keys_str_mv | AT thomassimpson towardsdatadrivenrealtimehybridsimulationadaptivemodelingofcontrolplants AT vasiliskdertimanis towardsdatadrivenrealtimehybridsimulationadaptivemodelingofcontrolplants AT eleninchatzi towardsdatadrivenrealtimehybridsimulationadaptivemodelingofcontrolplants |