dc.contributor.author | Baldi, S. | en |
dc.contributor.author | Michailidis, I. | en |
dc.contributor.author | Jula, H. | en |
dc.contributor.author | Kosmatopoulos, E. B. | en |
dc.contributor.author | Ioannou, Petros A. | en |
dc.creator | Baldi, S. | en |
dc.creator | Michailidis, I. | en |
dc.creator | Jula, H. | en |
dc.creator | Kosmatopoulos, E. B. | en |
dc.creator | Ioannou, Petros A. | en |
dc.date.accessioned | 2019-12-02T10:33:43Z | |
dc.date.available | 2019-12-02T10:33:43Z | |
dc.date.issued | 2013 | |
dc.identifier.isbn | 978-1-4673-5717-3 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/56453 | |
dc.description.abstract | Recently, there has been a growing interest towards simulation-based control design (co-simulation), where the controller utilizes an optimizer to minimize or maximize an objective function (system performance) whose evaluation involves simulation of the system to be controlled. However, existing simulation-based approaches are not able to handle in a computationally efficient way large-scale optimization problems involving hundreds or thousands of states and parameters. In this paper, we propose and analyze a new simulation-based control design approach, employing an adaptive optimization algorithm capable of efficiently handle large-scale control problems. The convergence properties of the proposed algorithm are established. Simulation results exhibit efficiency of the proposed approach when applied to large-scale problems. The simulation results employ two large-scale real-life systems for which conventional popular optimization techniques totally fail to provide an efficient simulation-based control design. © 2013 IEEE. | en |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en |
dc.source | Proceedings of the IEEE Conference on Decision and Control | en |
dc.source | 52nd IEEE Conference on Decision and Control, CDC 2013 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902341249&doi=10.1109%2fCDC.2013.6759920&partnerID=40&md5=d1c48e4d24498ac6b48ac8e33c82960a | |
dc.subject | Optimization | en |
dc.subject | Algorithms | en |
dc.subject | Control | en |
dc.subject | Computational efficiency | en |
dc.subject | Convergence properties | en |
dc.subject | Large scale systems | en |
dc.subject | Computationally efficient | en |
dc.subject | Adaptive optimization algorithm | en |
dc.subject | Co-simulation | en |
dc.subject | Cosimulation | en |
dc.subject | Large scale system | en |
dc.subject | Large-scale nonlinear systems | en |
dc.subject | Large-scale optimization | en |
dc.subject | Optimization techniques | en |
dc.subject | Simulation-based control design | en |
dc.subject | Simulation-based controls | en |
dc.title | A "plug-n-play" computationally efficient approach for control design of large-scale nonlinear systems using co-simulation | en |
dc.type | info:eu-repo/semantics/conferenceObject | |
dc.identifier.doi | 10.1109/CDC.2013.6759920 | |
dc.description.startingpage | 436 | |
dc.description.endingpage | 441 | |
dc.author.faculty | Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Μαθηματικών και Στατιστικής / Department of Mathematics and Statistics | |
dc.type.uhtype | Conference Object | en |
dc.description.notes | <p>Sponsors: et al. | en |
dc.description.notes | Honeywell | en |
dc.description.notes | MathWorks | en |
dc.description.notes | Springer | en |
dc.description.notes | Taylor and Francis Group | en |
dc.description.notes | University of Trieste | en |
dc.description.notes | Conference code: 105599 | en |
dc.description.notes | Cited By :10</p> | en |
dc.contributor.orcid | Ioannou, Petros A. [0000-0001-6981-0704] | |
dc.contributor.orcid | Michailidis, I. [0000-0001-7295-8806] | |
dc.gnosis.orcid | 0000-0001-6981-0704 | |
dc.gnosis.orcid | 0000-0001-7295-8806 | |