TR2022-144

Extremum seeking controller tuning for heat pump optimization using failure-robust Bayesian optimization

    •  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}
      • }
  • MERL Contacts:
  • Research Areas:

    Control, Machine Learning, Optimization

Abstract:

Extremum seeking controllers have been investigated for multivariable data-driven energy optimization in heat pumps. In particular, proportional-integral extremum seeking control (PI-ESC) has demonstrated potential for significant accel- eration compared to other ESC variants for nonlinear closed-loop control systems. A barrier to PI-ESC’s utilization in self-optimizing control is the fact that the PI-ESC algorithm is fragile. That is, unless the PI-ESC gains (e.g., controller gains, estimator gains) are carefully tuned, small perturbations to these gains can render the closed-loop unstable. Since arbitrary combinations of PI-ESC gains can result in instabilities, we propose a failure-robust Bayesian optimization (FRBO) algorithm that computes PI-ESC gains that ensure the closed-loop system can be driven rapidly to the optimum, while identifying and avoiding regions in the space of PI-ESC gains that are likely to result in instabilities (i.e., failures). The FRBO-tuned PI-ESC is shown to result in rapid closed-loop convergence to optimal values both on benchmark examples and a production-level model of an air conditioning system.

 

  • Related Publication

  •  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}
    • }