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
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