Optimality condition decomposition approach to distributed model predictive control
| Autor: | Frederic Hamelin, Joseph J. Yame, Tejaswinee Darure, Nathalie Sauer, Tushar Jain, Farah Gabsi |
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| Přispěvatelé: | Centre de Recherche en Automatique de Nancy (CRAN), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Indian Institute of Technology Mandi (IIT Mandi), Laboratoire de Génie Informatique, de Production et de Maintenance (LGIPM), Université de Lorraine (UL), gabsi, farah |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization [SPI] Engineering Sciences [physics] Computer science Control (management) Structure (category theory) 02 engineering and technology [SPI.AUTO]Engineering Sciences [physics]/Automatic Matrix (mathematics) [SPI]Engineering Sciences [physics] [SPI.AUTO] Engineering Sciences [physics]/Automatic 020901 industrial engineering & automation 020401 chemical engineering Distributed model predictive control Decomposition (computer science) 0204 chemical engineering |
| Zdroj: | American Control Conference, ACC 2019 American Control Conference, ACC 2019, Jul 2019, Philadelphia, PA, United States ACC Scopus-Elsevier |
| Popis: | International audience; This paper presents a new methodology for distributed model predictive control of large-scale systems. The methodology involves two distinct stages, i.e., the decomposition of large-scale systems into subsystems and the design of subsystem controllers. Two procedures are used: in the first stage, the structure of the Karush-Kuhn-Tucker matrix resulting from the necessary optimality conditions is exploited to yield a decomposition of the large-scale system into several subsystems. In the second stage, a particular technique, the so-called optimality condition decomposition makes it possible to synthesize distributed coordinated subcontrollers thus achieving an optimal distributed control of the large-scale system. The convergence of the proposed approach is stated. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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