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Article type: Research Article
Authors: Sun, Yin-Kuna | Hua, Juna; * | Li, Yan-Nab | Chen, Guang-Weia
Affiliations: [a] College of Mechanical and Electrical Engineering in Northeast Forestry University, Harbin Heilongjiang, China | [b] Harbin Research Institute of Forestry Machinery, the State Forestry Administration, Harbin Heilongjiang, China
Correspondence: [*] Corresponding author. Jun Hua, College of Mechanical and Electrical Engineering in Northeast Forestry University, Harbin Heilongjiang 150040, China. E-mail: sky@nefu.edu.cn.
Abstract: With the rapid development of science and technology, automatic control systems have been applied in more and more fields. At the same time, the requirements for the stability, accuracy, response speed, and self-regulation ability of the system are also increasing. In the wood processing industry, the heating system of factories is an important link to ensure normal production. Therefore, in order to further improve the production efficiency of the wood processing industry and enhance the stability and controllability of the heating system in wood processing production, this article takes into account the delay and coupling effects in the rapid heating process, and combines fuzzy control with temperature control to study and establish a woodworking thermal mechanical coupling model based on fuzzy control algorithm. The results show that compared with traditional PID control, fuzzy control has advantages such as short response time, small overshoot, high steady-state accuracy, fast steady-state recovery, and good dynamic and static performance. Under the condition of rapid heating, the existence of delay effect weakens the effect of Thermal shock, while the coupling effect not only affects the propagation of thermoelastic waves in the elastic body, but also weakens the weakening effect of delay effect on Thermal shock to a certain extent.
Keywords: Woodworking, fuzzy control, thermal-mechanical coupling, thermodynamic coupling, delay effect
DOI: 10.3233/JIFS-232242
Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10183-10192, 2023
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