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Título : Temperature Controller Using the Takagi-Sugeno-Kang Fuzzy Inference System for an Industrial Heat Treatment Furnace
Autor : Buele, Jorge
Ríos-Cando, Paulina
Brito, Geovanni
Moreno-P, Rodrigo
Salazar, Franklin
Fecha de publicación : 2020
Editorial : Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Volume 12254 LNCS, Pages 351 - 366. 20th International Conference on Computational Science and Its Applications, ICCSA 2020. Cagliari. 1 July 2020 through 4 July 2020
Resumen : The industrial welding industry has a high energy consumption due to the heating processes carried out. The heat treatment furnaces used for reheating equipment made of steel require a good regulator to control the temperature at each stage of the process, thereby optimizing resources. Considering dynamic and variable temperature behavior inside the oven, this paper proposes the design of a temperature controller based on a Takagi-Sugeno-Kang (TSK) fuzzy inference system of zero order. Considering the reaction curve of the temperature process, the plant model has been identified with the Miller method and a subsequent optimization based on the descending gradient algorithm. Using the conventional plant model, a TSK fuzzy model optimized by the recursive least square’s algorithm is obtained. The TSK fuzzy controller is initialized from the conventional controller and is optimized by descending gradient and a cost function. Applying this controller to a real heat treatment system achieves an approximate minimization of 15 min with respect to the time spent with a conventional controller. Improving the process and integrated systems of quality management of the service provided. © 2020, Springer Nature Switzerland AG.
URI : https://link.springer.com/chapter/10.1007/978-3-030-58817-5_27
http://repositorio.uti.edu.ec//handle/123456789/3357
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