dc.contributor.author | Romano, Rodrigo Alvite | |
dc.contributor.author | Pait, Felipe | |
dc.date.accessioned | 2024-10-15T21:11:10Z | |
dc.date.available | 2024-10-15T21:11:10Z | |
dc.date.issued | 2017 | |
dc.identifier.issn | 0018-9286 | |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018309755&doi=10.1109%2fTAC.2016.2602891&partnerID=40&md5=8f54f0080e9bc42f48846d8146145bd0 | |
dc.identifier.uri | https://repositorio.maua.br/handle/MAUA/1282 | |
dc.description.abstract | Identification of linear time-invariant multivariable systems can best be understood as comprising three separate problems: selection of system model structure, filter design, and parameter estimation itself. Approaching the first using matchable-observable models originally developed in the adaptive control literature and the second via direct or derivative-free optimization, effective least-squares algorithms can be used for parameter estimation. The accuracy, robustness and moderate computational demands of the methods proposed are demonstrated via simulations with randomly generated models and applied to identification using real process data. The results obtained are comparable or superior to the best results obtained using standard implementations of the algorithms described in the literature. © 1963-2012 IEEE. | en |
dc.language | Inglês | pt_BR |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en |
dc.relation.ispartof | IEEE Transactions on Automatic Control | |
dc.rights | Acesso Restrito | |
dc.source | Scopus | en |
dc.subject | Direct optimization | en |
dc.subject | multivariable systems | en |
dc.subject | parameter estimation | en |
dc.subject | system identification | en |
dc.subject | Bandpass filters | en |
dc.subject | Identification (control systems) | en |
dc.subject | Multivariable systems | en |
dc.subject | Optimization | en |
dc.subject | Adaptive Control | en |
dc.subject | Computational demands | en |
dc.subject | Derivative-free optimization | en |
dc.subject | Direct optimization | en |
dc.subject | Filter designs | en |
dc.subject | Least squares algorithm | en |
dc.subject | Linear time invariant | en |
dc.subject | Multivariable identifications | en |
dc.subject | Parameter estimation | en |
dc.title | Matchable-Observable Linear Models and Direct Filter Tuning: An Approach to Multivariable Identification | en |
dc.type | Artigo de Periódico | pt_BR |
dc.identifier.doi | 10.1109/TAC.2016.2602891 | |
dc.description.affiliation | Escola de Engenharia Maua, Instituto Maua de Tecnologia Sao Caetano Do sul SP, Brazil | |
dc.description.affiliation | Escola Politecnica, Universidade de S Ao Paulo Sao, Paulo SP, Brazil | |
dc.identifier.scopus | 2-s2.0-85018309755 | |
dc.citation.issue | 5 | |
dc.citation.epage | 2193 | |
dc.citation.spage | 2180 | |
dc.citation.volume | 62 | |