Paper Info
Title
A New Control Method for dc-dc Converter by Neural Network Predictor with Repetitive Training
Abstract
This paper proposes a novel prediction based digital control dc-dc converter. In this method, a neural network control is adopted to improve the transient response in coordination with a conventional P-I-D control. The prediction based control term is consists of predicted data which are obtained from repetitive training of the neural network. This works to improve the transient response very effectively when the load is changed quickly. As a result, the undershoot of the output voltage and the overshoot of the reactor current are suppressed effectively as compared with the conventional one in the step change of load resistance. The proposed method is based on the neural network learning, it is expected that the proposed approach has high availability in providing the easy way for the design of circuit system since there is no need to change the algorithm. The adequate availability of the proposed method is also confirmed by the experiment in which P-I-D control parameters of the circuit are set to non-optimal ones and the proposed method is used in the same manner.
Year
DOI
Venue
2011
10.1109/ICMLA.2011.17
ICMLA (2)
Keywords
Field
DocType
p-i-d control parameter,neural network,control term,digital control dc-dc converter,neural network control,new control method,neural network learning,neural network predictor,dc-dc converter,transient response,repetitive training,conventional p-i-d control,digital control,high availability,predictive control
Transient response,Control theory,Computer science,Voltage,Model predictive control,Overshoot (signal),Artificial neural network,Dc dc converter,Digital control,High availability
Conference
Citations 
PageRank 
References 
1
0.87
0
Authors
4
Name
Order
Citations
PageRank
fujio kurokawa1149.80
Kimitoshi Ueno211.21
Hidenori Maruta3167.11
Hiroyuki Osuga422.37