DocumentCode :
423914
Title :
The research of PID neural network decoupling controller and its application in unit load system
Author :
Liu, Hong-Jun ; Han, Pu ; Yao, Wan-ye ; Li, Yu-Hong
Author_Institution :
Dept. of Autom., North China Electr. Power Univ., Baoding, China
Volume :
1
fYear :
2004
fDate :
26-29 Aug. 2004
Firstpage :
505
Abstract :
Based on back-propagation (BP) arithmetic, a neural network PID decoupling control strategy is presented for multi-input and multi-output (MIMO) system, which is applied to thermal power unit load system. The idea is the parameter such as proportion, integration and differentiation of PID controller are tuned on-line through self-learning of a network based on a certain control law. And the corresponding control law is taken across classical PID control algorithm. Then decoupling control is carried out in the process. Simulation results show that the almost dynamic decoupling and completely static decoupling are obtained, the closed loop system has zero static error, and the advantages of higher speed response and stronger robustness are developed.
Keywords :
MIMO systems; backpropagation; closed loop systems; load regulation; neurocontrollers; power station control; thermal power stations; three-term control; unsupervised learning; MIMO system; PID neural network decoupling controller; back-propagation arithmetic; closed loop system; multiinput multioutput; self-learning network; static decoupling; thermal power unit load system; unit load system; zero static error; Automatic control; Control systems; Intelligent networks; Load flow control; MIMO; Machine learning; Neural networks; Power generation; Proportional control; Three-term control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
Type :
conf
DOI :
10.1109/ICMLC.2004.1380743
Filename :
1380743
Link To Document :
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