| dc.creator | Serra, GLO | |
| dc.creator | Bottura, CP | |
| dc.date | 2006 | |
| dc.date | MAR | |
| dc.date | 2014-11-14T06:51:38Z | |
| dc.date | 2015-11-26T16:05:01Z | |
| dc.date | 2014-11-14T06:51:38Z | |
| dc.date | 2015-11-26T16:05:01Z | |
| dc.date.accessioned | 2018-03-28T22:54:08Z | |
| dc.date.available | 2018-03-28T22:54:08Z | |
| dc.identifier | Engineering Applications Of Artificial Intelligence. Pergamon-elsevier Science Ltd, v. 19, n. 2, n. 157, n. 167, 2006. | |
| dc.identifier | 0952-1976 | |
| dc.identifier | WOS:000235480200005 | |
| dc.identifier | 10.1016/j.engappai.2005.08.003 | |
| dc.identifier | http://www.repositorio.unicamp.br/jspui/handle/REPOSIP/82169 | |
| dc.identifier | http://www.repositorio.unicamp.br/handle/REPOSIP/82169 | |
| dc.identifier | http://repositorio.unicamp.br/jspui/handle/REPOSIP/82169 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/1265632 | |
| dc.description | This work proposes a gain scheduling adaptive control scheme based on fuzzy systems, neural networks and genetic algorithms for nonlinear plants. A fuzzy PI controller is developed, which is a discrete time version of a conventional one. Its data base as well as the constant PI control gains are optimally designed by using a genetic algorithm for simultaneously satisfying the following specifications: overshoot and settling time minimizations and output response smoothing. A neural gain scheduler is designed, by the backpropagation algorithm, to tune the optimal parameters of the fuzzy PI controller at some operating points. Simulation results are shown to demonstrate the efficiency of the proposed structure for a DC servomotor adaptive speed control system used as an actuator of robotic manipulators. (c) 2005 Elsevier Ltd. All rights reserved. | |
| dc.description | O TEXTO COMPLETO DESTE ARTIGO, ESTARÁ DISPONÍVEL À PARTIR DE NOVEMBRO DE 2014. | |
| dc.description | 19 | |
| dc.description | 2 | |
| dc.description | 157 | |
| dc.description | 167 | |
| dc.language | en | |
| dc.publisher | Pergamon-elsevier Science Ltd | |
| dc.publisher | Oxford | |
| dc.publisher | Inglaterra | |
| dc.relation | Engineering Applications Of Artificial Intelligence | |
| dc.relation | Eng. Appl. Artif. Intell. | |
| dc.rights | embargo | |
| dc.rights | http://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy | |
| dc.source | Web of Science | |
| dc.subject | neural-genetic-fuzzy systems | |
| dc.subject | adaptive control | |
| dc.subject | multiobjective optimization | |
| dc.subject | Gain | |
| dc.title | Multiobjective evolution based fuzzy PI controller design for nonlinear systems | |
| dc.type | Artículos de revistas | |
