EUROCON 2007 - The International Conference on "Computer as a Tool" 2007
DOI: 10.1109/eurcon.2007.4400274
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Iterative Learning Control Approach to Fuzzy Control Systems Development

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Cited by 1 publication
(2 citation statements)
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“…Neural networks [46], [50], [61], [69], [79], [83] can be employed at the process and/or controller level as well, giving more weight to the learning feature, but in a close connection to optimization. In addition, fuzzy logic can be included in ILC algorithms as suggested in authors' past papers [9], [40], [41], [42], [43], [44] and [45].…”
Section: Discussionmentioning
confidence: 99%
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“…Neural networks [46], [50], [61], [69], [79], [83] can be employed at the process and/or controller level as well, giving more weight to the learning feature, but in a close connection to optimization. In addition, fuzzy logic can be included in ILC algorithms as suggested in authors' past papers [9], [40], [41], [42], [43], [44] and [45].…”
Section: Discussionmentioning
confidence: 99%
“…More than fifteen years ago the authors suggested in [40] the combination of fuzzy control and ILC in order to benefit from the advantageous of these two control strategies and to alleviate their shortcomings. Several low-cost controllers specific to this first approach were proposed in the subsequent papers [9], [41], [42], [43], [44] and [45] focusing on the Proportional-Integral (PI) fuzzy controllers, which replace the linear PI controllers and the performance of fuzzy control systems is improved in terms of ILC. In addition, fuzzy logic was also involved in the learning functions or learning rules specific to ILC algorithms to add more flexibility.…”
Section: Introductionmentioning
confidence: 99%