NEWS    MERL researchers win ASME Energy Systems Technical Committee Best Paper Award at 2022 American Control Conference

Date released: June 14, 2022


  •  NEWS    MERL researchers win ASME Energy Systems Technical Committee Best Paper Award at 2022 American Control Conference
  • Date:

    June 8, 2022

  • Where:

    2022 American Control Conference

  • Description:

    Researchers from EPFL (Wenjie Xu, Colin Jones) and EMPA (Bratislav Svetozarevic), in collaboration with MERL researchers Ankush Chakrabarty and Chris Laughman, recently won the ASME Energy Systems Technical Committee Best Paper Award at the 2022 American Control Conference for their work on "VABO: Violation-Aware Bayesian Optimization for Closed-Loop Performance Optimization with Unmodeled Constraints" out of 19 nominations and 3 finalists. The paper describes a data-driven framework for optimizing the performance of constrained control systems by systematically re-evaluating how cautiously/aggressively one should explore the search space to avoid sustained, large-magnitude constraint violations while tolerating small violations, and demonstrates these methods on a physics-based model of a vapor compression cycle.

  • MERL Contacts:
  • Research Areas:

    Control, Machine Learning, Multi-Physical Modeling, Optimization

    •  Xu, W., Jones, C., Svetozarevic, B., Laughman, C.R., Chakrabarty, A., "VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints", American Control Conference (ACC), June 2022, pp. 5288-5293.
      BibTeX TR2022-064 PDF
      • @inproceedings{Xu2022jun,
      • author = {Xu, Wenjie and Jones, Colin and Svetozarevic, Bratislav and Laughman, Christopher R. and Chakrabarty, Ankush},
      • title = {VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints},
      • booktitle = {American Control Conference (ACC)},
      • year = 2022,
      • pages = {5288--5293},
      • month = jun,
      • isbn = {978-1-6654-5197-0},
      • url = {https://www.merl.com/publications/TR2022-064}
      • }