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
Link To Document :
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