Title :
A Self-tuning Controller for Real-time Voltage Regulation
Author :
Li, Weiming ; Yu, Xiao-Hua
Author_Institution :
St. Jude Med., Sylmar
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;
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2007.4371267