DocumentCode
1947474
Title
A Self-tuning Controller for Real-time Voltage Regulation
Author
Li, Weiming ; Yu, Xiao-Hua
Author_Institution
St. Jude Med., Sylmar
fYear
2007
fDate
12-17 Aug. 2007
Firstpage
2010
Lastpage
2014
Abstract
In this research, a self-tuning controller based on multi-layer feed-forward neural network is developed for realtime output voltage regulation of a class of DC power supplies. The neural network based controller has the advantage of adaptive learning ability, and can work under the situations when the input voltage and load current fluctuate. Levenberg-Marquardt back-propagation training algorithm is used in computer simulation. The neural network controller is implemented and tested on hardware using a DSP (digital signal processor). Experimental results show that this neural network based approach outperforms the conventional analog controller, in terms of both line regulation and load regulation.
Keywords
DC-DC power convertors; backpropagation; feedforward neural nets; neurocontrollers; power engineering computing; power supply circuits; tuning; voltage control; DC-DC converter; DSP; Levenberg-Marquardt back-propagation training algorithm; adaptive learning; computer simulation; conventional analog controller; input voltage; load current fluctuate; multilayer feed-forward neural network; neural network based controller; real-time voltage regulation; realtime output voltage regulation; self-tuning controller; Adaptive control; Computer simulation; Feedforward neural networks; Feedforward systems; Multi-layer neural network; Neural networks; Power supplies; Programmable control; Signal processing algorithms; Voltage control;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location
Orlando, FL
ISSN
1098-7576
Print_ISBN
978-1-4244-1379-9
Electronic_ISBN
1098-7576
Type
conf
DOI
10.1109/IJCNN.2007.4371267
Filename
4371267
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