Output Modifier Adaptation with Filter-Based Constraints

Aris Papasavvas, Gregory Francois

Research output: Contribution to journalArticlepeer-review


Modifier adaptation (MA) and output modifier adaptation (MAy) are iterative model-based real-time optimization (RTO) algorithms that have the proven ability to drive plants to their optimal operating condition upon convergence despite disturbances and modeling uncertainty, provided the model at hand satisfies model adequacy conditions. But there is no guarantee that constraints are satisfied before convergence. In this article, an improvement of the formulation of MA and MAy is proposed that is proven to bring significant improvements w.r.t. these two limitations. While standard MA or MAy suggests to perform optimization and filtering sequentially, it is proposed to integrate the input filtering stage in the modified model-based optimization problem by means of a duplication of the constraints. The corresponding approach, labeled `KMAy', is (i) proven to preserve constraint qualification despite constraint duplication, (ii) proven to preserve the property of MA methods to converge to the true plant optimal inputs, (iii) proven to significantly relax the model adequacy condition - leading it to be independent of the constraints of the optimization problem, (iv) shown to increase the chances of converging from the safe side of the plant constraints and (v) shown to support the choice of input filtering, rather than output or modifier filtering, if the input filter is appropriately chosen. A method for the automatic selection of the largest filter gain with the five aforementioned assets, while minimizing the filter-induced conservatism, is proposed. It is finally shown that, although KMAy has been first proposed an improvement of MAy, similar improvements can be obtained for MA. The performances of KMAy with and without adaptive gain and of the corresponding MA extensions are successfully illustrated by means of the optimization of a benchmark simulated chemical reactor.
Original languageEnglish
Pages (from-to)37-53
JournalJournal of Process Control
Early online date28 Jan 2020
Publication statusPublished - Mar 2020


  • Rea-time Optimization
  • Modifier Adaptation
  • Model Adequacy


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