NDLI: Decoupling control based on PID neural network for deaerator and condenser water level control system

Content Provider	IEEE Xplore Digital Library
Author	Wang Peng Meng Hao Dong Peng Dai Ri-hui
Copyright Year	2015
Description	Author affiliation: Representative Office, No.703 Res. Inst., Harbin, China (Dai Ri-hui) \|\| Coll. of Autom., Harbin Eng. Univ., Harbin, China (Wang Peng; Meng Hao) \|\| Harbin Marine Boiler & Turbine Res. Inst., Harbin, China (Dong Peng)
Abstract	There is strong intercoupling relation between condenser water level and deaerator water level in marine steam power plant. In order to get satisfactory control effect, it is essential to take corresponding decoupling measures. PID neural network not only has the advantages of PID, but also has the abilities of learning, remembering and nonlinear approximation. In this paper, we propose a deaerator water level and condenser water level decoupling control strategy based on PID neural network, which integrates PID and neural network by establishing proportional neuron, integral neuron and derivative neuron corresponding to proportional, integral and derivative, respectively. We also propose a method of choosing initial weights and learning coefficient from experience of PID to enhance the convergence performance of PID neural network. The simulation results show that the PID neural network decoupling control strategy can meet the requirements of multivariable system decoupling control. It is more effective in condenser water level and deaerator water level decoupling than PID control strategy.
Starting Page	3441
Ending Page	3446
File Size	322502
Page Count	6
File Format	PDF
ISBN	9789881563897
ISSN	19341768
DOI	10.1109/ChiCC.2015.7260169
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Technical Committee on Control Theory, Chinese Association of Automation
Subject Keyword	BP neural network steam power Neurons condenser water level Artificial neural networks Valves Mathematical model Feeds deaerator water level Biological neural networks Chemicals multivariable system decouple
Content Type	Text
Resource Type	Article

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