DocumentCode :
2896125
Title :
Applying Neural Networks to PID Controllers for Time-Delay Systems
Author :
Yu, Zhun ; Xie, Ying-bai ; Jing, You-Yin ; Lu, Xu-ao
Author_Institution :
Dept. of Power Eng., North China Electr. Power Univ., Baoding
fYear :
2006
fDate :
13-16 Aug. 2006
Firstpage :
3173
Lastpage :
3176
Abstract :
Generalized PID neural network (GPIDNN) has recently received more attention in industry application. To investigate the control of long time-delay systems with GPIDNN control system, both the structure and the algorithm were presented in this paper, the real-time simulation to a main steam temperature control system was also carried out. The results show that GPIDNN is less sensitive to variation in the time-delay in comparison of conventional PID control system, it has short transition time and little over-adjust as well as ideal control quality. The results obtained during the present study indicate that GPIDNN has favorable control ability with self learning and self adapting; it is suitable substitute for conventional PID controllers for long time-delay systems
Keywords :
delay systems; delays; feedforward neural nets; learning systems; neurocontrollers; self-adjusting systems; temperature control; three-term control; generalized PID neural network controller; long time-delay control system; real-time simulation; self adapting system; self learning system; steam temperature control system; Control system synthesis; Control systems; Cybernetics; Electronic mail; Machine learning; Machine learning algorithms; Neural networks; Neurons; Power engineering; Stability; Temperature control; Three-term control; PID controller; neural networks; simulation; time-delay system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
Type :
conf
DOI :
10.1109/ICMLC.2006.258413
Filename :
4028612
Link To Document :
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