Temperature Controller Using the Takagi-Sugeno-Kang Fuzzy Inference System for an Industrial Heat Treatment Furnace

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.

• Autor principal: Buele, Jorge (author)
• Outros Autores: Ríos-Cando, Paulina (author), Brito, Geovanni (author), Moreno-P, Rodrigo (author), Salazar, Franklin (author)
• Formato: article
• Idioma: eng
• Publicado em: 2020
• Acesso em linha: https://link.springer.com/chapter/10.1007/978-3-030-58817-5_27; https://hdl.handle.net/20.500.14809/3357