Batch-to-batch strategies for cooling crystallization

M. Forgione, A. Mesbah, X. Bombois, P.M.J. Hof, Van den

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

3 Citations (Scopus)


Two batch-to-batch (B2B) algorithms for supersaturation control in cooling crystallization are presented in this paper. In Iterative Learning Control (ILC) a nominal process model is adjusted with an additive correction term which depends on the error in the last batch. In Iterative Identification Control (IIC) the physical parameters of the process model are recursively estimated by adopting a Bayesian identification framework. Both B2B algorithms compute an optimized input for the next batch that is fed to a lower level PI feedback controller in order to reject the process disturbances. The tracking performance of these B2B+PI control schemes is investigated in a simulation study.
Original languageEnglish
Title of host publicationProceedings of the 51st IEEE Conference on Decision and Control (CDC 2012), 10-13 December 2012, Maui, Hawai
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Publication statusPublished - 2012
Event51st IEEE Conference on Decision and Control, CDC 2012 - Maui, United States
Duration: 10 Dec 201213 Dec 2012
Conference number: 51


Conference51st IEEE Conference on Decision and Control, CDC 2012
Abbreviated titleCDC 2012
Country/TerritoryUnited States


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