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

Yizhou Fang; Antonios Armaou

doi:10.1109/ACC.2015.7172221

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 proceeding › Conference contribution

13 Scopus citations

Abstract

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 language	English (US)
Title of host publication	ACC 2015 - 2015 American Control Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5629-5634
Number of pages	6
ISBN (Electronic)	9781479986842
DOIs	https://doi.org/10.1109/ACC.2015.7172221
State	Published - Jul 28 2015
Event	2015 American Control Conference, ACC 2015 - Chicago, United States Duration: Jul 1 2015 → Jul 3 2015

Publication series

Name	Proceedings of the American Control Conference
Volume	2015-July
ISSN (Print)	0743-1619

Other

Other	2015 American Control Conference, ACC 2015
Country/Territory	United States
City	Chicago
Period	7/1/15 → 7/3/15

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

Access to Document

10.1109/ACC.2015.7172221

Cite this

Fang, Y., & Armaou, A. (2015). Nonlinear Model Predictive Control using a bilinear Carleman linearization-based formulation for chemical processes. In ACC 2015 - 2015 American Control Conference (pp. 5629-5634). Article 7172221 (Proceedings of the American Control Conference; Vol. 2015-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2015.7172221

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title = "Nonlinear Model Predictive Control using a bilinear Carleman linearization-based formulation for chemical processes",

abstract = "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.",

author = "Yizhou Fang and Antonios Armaou",

note = "Publisher Copyright: {\textcopyright} 2015 American Automatic Control Council.; 2015 American Control Conference, ACC 2015 ; Conference date: 01-07-2015 Through 03-07-2015",

year = "2015",

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doi = "10.1109/ACC.2015.7172221",

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Fang, Y & Armaou, A 2015, Nonlinear Model Predictive Control using a bilinear Carleman linearization-based formulation for chemical processes. in ACC 2015 - 2015 American Control Conference., 7172221, Proceedings of the American Control Conference, vol. 2015-July, Institute of Electrical and Electronics Engineers Inc., pp. 5629-5634, 2015 American Control Conference, ACC 2015, Chicago, United States, 7/1/15. https://doi.org/10.1109/ACC.2015.7172221

Nonlinear Model Predictive Control using a bilinear Carleman linearization-based formulation for chemical processes. / Fang, Yizhou; Armaou, Antonios.
ACC 2015 - 2015 American Control Conference. Institute of Electrical and Electronics Engineers Inc., 2015. p. 5629-5634 7172221 (Proceedings of the American Control Conference; Vol. 2015-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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.

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

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