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The Relationship between Fuzzy Controllers and PID Controllers
Published in Hongxing Li, C.L. Philip Chen, Han-Pang Huang, Fuzzy Neural Intelligent Systems, 2018
Hongxing Li, C.L. Philip Chen, Han-Pang Huang
Because of its simplicity and robust performance, proportional-integral-derivative (PID) controllers are the most commonly used controllers in industrial process control. The transfer function of a PID controller has the following form: G(s)=KP+KIs+KDs,
A new key performance indicator oriented industrial process monitoring and operating performance assessment method based on improved Hessian locally linear embedding
Published in International Journal of Systems Science, 2022
Hongjun Zhang, Chi Zhang, Jie Dong, Kaixiang Peng
With the development of production technology, the industrial process is developing in the direction of large-scale, high-automation and complex-coupling, which also increases the uncertainty of safety problems (Destro et al., 2020; Ge, 2010; Shen, Wang, Wang & Liu, et al., 2020). In modern industrial manufacturing, the issue that engineers and technicians concerned has gradually transformed from the traditional process control to the comprehensive monitoring and optimisation for a more complex industrial system. Problems concerning process monitoring, fault diagnosis, and integrated production scheduling and optimisation have become the core content of industrial process control. Among them, the performance, risk as well as health indicator of the system have all obtained extensive attentions (Hu et al., 2019; Jiang et al., 2019; Saeed & Karim, 2015; L. Zou et al., 2019a). Furthermore, with the increasingly fierce market competition, the shortage of raw material resources worldwide and the advocacy of the sustainable development, the investigation of key performance indicator (KPI) oriented process monitoring and assessment technologies to reduce the energy consumptions and pursue the maximisation of comprehensive economic benefits has gradually become one of the primary tasks in the process control field.
Shared communication for coordinated large-scale reinforcement learning control
Published in SICE Journal of Control, Measurement, and System Integration, 2023
Nicolas Bougie, Takashi Onishi, Yoshimasa Tsuruoka
Industrial process control is a large and diverse field; its broad range of applications includes chemical, power, or semiconductor plant control. For example, chemical plants consist of several processing units that cooperatively produce chemical products. Such plants often comprise a huge number of sensors and control units. With ever-increasing demand for products in process industries, it is necessary to maintain optimal production in a variety of situations, including when the system encounters disturbances or drifts in process characteristics.