Nonlinear Model Predictive Control using a bilinear Carleman linearization-based formulation for chemical processes

Yizhou Fang, Antonios Armaou

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

13 Scopus citations


Model Predictive Control (MPC) has gained widespread acceptance in industry due to its capability of coping with constraints, handling multiple-input-multiple-output systems and evolving control policy. One significant barrier to the development of MPC is its complexity in computation when encountering nonlinear systems, the resulting feedback delays, and the consequent loss of controller performance as well as stability issues. In this manuscript, we propose a new formulation of MPC for nonlinear systems based on Carleman linearization. The nonlinear dynamic constraints are modeled with bilinear representations. This formulation enables analytical computation of NMPC. Optimization is accelerated by providing sensitivity of the cost function to the control signals. A case study example using a nonlinear isothermal CSTR is presented, demonstrating that the proposed formulation reduces computational efforts.

Original languageEnglish (US)
Title of host publicationACC 2015 - 2015 American Control Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781479986842
StatePublished - Jul 28 2015
Event2015 American Control Conference, ACC 2015 - Chicago, United States
Duration: Jul 1 2015Jul 3 2015

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2015 American Control Conference, ACC 2015
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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