A comparison study of fuzzy and PI control system for temperature control of superheated steam

2015 ◽  
Author(s):  
Tomas Nahlovsky
Keyword(s):  
Control System ◽  
Temperature Control ◽  
Superheated Steam ◽  
Pi Control ◽  
Comparison Study

Modeling and Development of Copper Wire Annealing Fuzzy Control System Based on Temperature Detecting

2014 ◽  
Vol 487 ◽  
pp. 587-590 ◽  
Author(s):  
Xue Jun Xu ◽  
Yong Zhong Lin ◽  
Hong Yang Sun ◽  
Yi Gen Lu ◽  
Tian Lv
Keyword(s):  
Control System ◽  
Temperature Control ◽  
Annealing Temperature ◽  
Copper Wire ◽  
Pi Controller ◽  
Temperature Control System ◽  
Pi Control ◽  
Continuous Annealing ◽  
Fuzzy Pi Controller

Copper wire continuous annealing is a key process of the cable production line. The accuracy of the temperature control directly determines whether the quality of copper wire meets the requirements. In this paper, the fuzzy-PI compound controller was selected and the structure of the annealing temperature fuzzy-PI control system was designed based on temperature measurement. Through the simulation of traditional PI control system and the improved Fuzzy-PI control system, the results show that the Fuzzy-PI controller is better than traditional PI controller. It verifies the feasibility and effectiveness of the Fuzzy -PI controller and proves that Fuzzy-PI algorithm can be successfully applied to the copper wire continuous annealing temperature control system.

Improved CMAC Neural Network Control for Superheated Steam Temperature

2011 ◽  
Vol 383-390 ◽  
pp. 111-117 ◽  
Author(s):  
Li Jun Chen ◽  
Bo Sun ◽  
Jian Chao Diao ◽  
Li Li Zhao
Keyword(s):  
Neural Network ◽  
Control System ◽  
Temperature Control ◽  
Superheated Steam ◽  
Network Control ◽  
Temperature Control System ◽  
Neural Network Control ◽  
Steam Temperature ◽  
Cmac Neural Network ◽  
Superheated Steam Temperature

Aiming at that superheated steam temperature system exists the large inertia and large time delay of the dynamic characteristics,and the converge speed of the conventional CMAC neural network is not fast enough to the real-time system, a credit assignment CMAC (CA-CMAC) neural network control is adopted in superheated steam temperature control system, which is proposed to speed up the learning process in CMAC. The simulation of the superheated steam temperature control system shows that CA-CMAC converges faster than the conventional CMAC. This result illustrates the effectiveness of this method.

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