Integrated identification and model predictive control using iterative refinement

L. Yang, Daniel Rivera

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


An iterative refinement approach is presented for designing a high performance control system for an imprecisely known plant. The design procedure involves the integration of identification and model predictive control (MPC) using repeated closed-loop identification tests and successively improving the model (and ultimately, closed-loop performance) with each successive iteration. The method is appealing to industrial practice because real-time closed-loop data can be used directly to enhance the performance of a predictive controller without the need to deactivate the control loop during identification testing. The iterative refinement strategy is "plant-friendly" in that it tries to keep the identification test as short as possible while keeping the plant within operating limits and constraint. Constraint enforcement (on controlled, associated and manipulated variables) is naturally implemented through the use of MPC. The application of the method is demonstrated on a highly nonlinear diabatic (i.e., non-adiabatic) continuous stirred tank reactor problem.

Original languageEnglish (US)
Title of host publicationProceedings of the American Control Conference
Number of pages6
StatePublished - 2001
Event2001 American Control Conference - Arlington, VA, United States
Duration: Jun 25 2001Jun 27 2001


Other2001 American Control Conference
Country/TerritoryUnited States
CityArlington, VA

ASJC Scopus subject areas

  • Control and Systems Engineering


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