Mathieu Beauchemin-Turcotte, G. Gauthier, R. Sabourin
{"title":"Design of Fuzzy Terminal Iterative Learning Control based on internal model control","authors":"Mathieu Beauchemin-Turcotte, G. Gauthier, R. Sabourin","doi":"10.1109/MED.2014.6961416","DOIUrl":null,"url":null,"abstract":"This paper presents an innovative approach to the design of a cycle to cycle control algorithm: Fuzzy Terminal Iterative Learning Control (f-TILC). This is the first fuzzy Terminal Iterative Learning Control (TILC) ever proposed up to now. This fuzzy controller is built from a fuzzy model of the process, based on the 1st order Takagi Sugeno Kwan Fuzzy Inference System. The rule consequents are expressed as matricial equations, and obtained from experimental results and kriging interpolation. Simulation results show the effectiveness of our fuzzy TILC, especially in terms of providing a good initial guess as to the inputs to apply to the control process. This control approach can help to reduce the wastage of products in thermoforming processes.","PeriodicalId":127957,"journal":{"name":"22nd Mediterranean Conference on Control and Automation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"22nd Mediterranean Conference on Control and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MED.2014.6961416","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract
This paper presents an innovative approach to the design of a cycle to cycle control algorithm: Fuzzy Terminal Iterative Learning Control (f-TILC). This is the first fuzzy Terminal Iterative Learning Control (TILC) ever proposed up to now. This fuzzy controller is built from a fuzzy model of the process, based on the 1st order Takagi Sugeno Kwan Fuzzy Inference System. The rule consequents are expressed as matricial equations, and obtained from experimental results and kriging interpolation. Simulation results show the effectiveness of our fuzzy TILC, especially in terms of providing a good initial guess as to the inputs to apply to the control process. This control approach can help to reduce the wastage of products in thermoforming processes.
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