TR2022-172

Rapid Energy Optimization of Vapor Compression Systems Using Probabilistic Machine Learning and Extremum Seeking Control

    •  Chakrabarty, A., Burns, D.J., Guay, M., Laughman, C.R., "Rapid Energy Optimization Of Vapor Compression Systems Using Probabilistic Machine Learning And Extremum Seeking Control", International Refrigeration and Air Conditioning Conference (IRACC), July 2022.
      BibTeX TR2022-172 PDF
      • @inproceedings{Chakrabarty2022jul,
      • author = {Chakrabarty, Ankush and Burns, Daniel J. and Guay, Martin and Laughman, Christopher R.},
      • title = {Rapid Energy Optimization Of Vapor Compression Systems Using Probabilistic Machine Learning And Extremum Seeking Control},
      • booktitle = {International Refrigeration and Air Conditioning Conference (IRACC)},
      • year = 2022,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2022-172}
      • }
  • MERL Contacts:
  • Research Areas:

    Control, Multi-Physical Modeling, Optimization

Abstract:

Extremum seeking control (ESC) is a popular data­driven approach for optimizing the energy consumption of vapor compression systems (VCS). Tuning ESC control parameters can present a challenge to implementation, especially in advanced variants of ESC, because time­consuming and problem­specific manual tuning is often required to eliminate numerical and dynamical instabilities. In this paper, we propose an automatic ESC tuning mechanism based on a Bayesian optimization framework that systematically leverages closed­loop ESC experiments to compute highperforming ESC parameters. We validate the proposed Bayesian­optimized ESC on a physics­based Modelica model of a VCS. This new approach is six times faster and yields a 9% higher coefficient of performance than a state­of­the­art time­varying ESC method under identical experimental conditions.

 

  • Related Publication

  •  Chakrabarty, A., Burns, D.J., Guay, M., Laughman, C.R., "Extremum seeking controller tuning for heat pump optimization using failure-robust Bayesian optimization", Journal of Process Control, DOI: 10.1016/​j.jprocont.2022.11.006, Vol. 120, pp. 86-96, November 2022.
    BibTeX TR2022-144 PDF
    • @article{Chakrabarty2022nov2,
    • author = {Chakrabarty, Ankush and Burns, Daniel J. and Guay, Martin and Laughman, Christopher R.},
    • title = {Extremum seeking controller tuning for heat pump optimization using failure-robust Bayesian optimization},
    • journal = {Journal of Process Control},
    • year = 2022,
    • volume = 120,
    • pages = {86--96},
    • month = nov,
    • doi = {10.1016/j.jprocont.2022.11.006},
    • url = {https://www.merl.com/publications/TR2022-144}
    • }