Switching supervisory control using calibrated forecasts

Ibrahim Al-Shyoukh*, Jeff S. Shamma

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


In this paper, we approach supervisory control as an online decision problem. In particular, we introduce "calibrated forecasts" as a mechanism for controller selection in supervisory control. The forecasted event is whether or not a controller will be effective over a finite implementation horizon. Controller selection is based on using the controller with the maximum calibrated forecast of the reward. Assuming the existence of a stabilizing controller within the set of candidate controllers, we show that under the proposed supervisory controller, the output of the system remains bounded for any bounded disturbance, even if the disturbance is chosen in an adversarial manner. The use of calibrated forecasts enables one to establish overall performance guarantees for the supervisory scheme even though non-stabilizing controllers may be persistently selected by the supervisor because of the effects of initial conditions, exogenous disturbances, or random selection. The main results are obtained for a general class of system dynamics and specialized to linear systems.

Original languageEnglish (US)
Title of host publicationProceedings of the 46th IEEE Conference on Decision and Control 2007, CDC
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)1424414989, 9781424414987
StatePublished - 2007
Externally publishedYes
Event46th IEEE Conference on Decision and Control 2007, CDC - New Orleans, LA, United States
Duration: Dec 12 2007Dec 14 2007

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Other46th IEEE Conference on Decision and Control 2007, CDC
Country/TerritoryUnited States
CityNew Orleans, LA

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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