A New Prediction Based Digital Control for DC-DC Converter

Hidenori Maruta, Junya Sakemi, Akihiro Nakamura, Fujio Kurokawa, Hiroyuki Osuga

Abstract


As energy saving becomes important for sustainable society, one of the important social problems
is to decrease the power consumption of the power converters such as those in renewable energy
systems and information systems. In these application fields, the superior digital control method of power
converter is needed. This paper presents a novel prediction based digitally controlled dc-dc converter. In
the presented method, the prediction based control is adopted as the feedforward control added to the
conventional P-I-D control as the feedback control. This prediction based control performs to control
dynamical properties of the system and it is realized by a neural network approach. This neural network
based control improves the transient response very effectively when the load is changed quickly. As a
result, the undershoot of output voltage and the overshoot of reactor current are suppressed effectively
as compared with the conventional one in the step change of load resistance. Both in simulations and
experiments, it is confirmed that the presented prediction based control technique is useful to realize
the superior digitally controlled method for the dc-dc converter.

Keywords


dc-dc converter, digital control, neural network, feedforward control

Full Text:

PDF

References


“Energy Management And Conservation Program (Fiscal Year 2009)â€, U.S. Environmental Protection Agency, Dec. 2009.

“FY 2008 Annual Energy Reportâ€, Agency for Natural Resources and Energy, Ministry of Economy, Trade and Industry, Japan, Aug. 2009.

K. Kant, â€Toward a Science of Power Managementâ€, IEEE Computer, vol. 42, no. 9, pp. 99-101, Sep. 2009.

J. Xiao, A. V. Peterchev and S. R. Sanders, â€Architecture and IC implementation of a digital VRM controllerâ€, IEEE PESC Record, pp. 38-47, June 2001.

L. Guo, J. Y. Hung and R. M. Nelms, â€PID controller modifications to improve steady-state– performance of digital controllers for buck and boost convertersâ€, Proc. of Annual IEEE Applied Power Electronics Conference, no. 9.3, pp. 381-388, March 2002.

Q. M. Li, â€A low-cost configurable PWM controller using programmable system-on-chipâ€, IEEE PESC Record, pp. 1169-1174, June 2003.

F. Kurokawa and W. Okamoto, â€Improvement of dynamic characteristics of digitally controlled switching power converterâ€, Proc. IEEE & IEEJ Power Conversion Conference, vol. 4, no.1, pp. 1147-1153, April 2007.

H. Fujimoto and Y. Hori, â€Advanced digital motion control based on multirate sampling controlâ€, Proc. IFAC Triennial World Congress, pp. 1603-1608, July 2002.

F. Kurokawa and S. Sukita, â€A new model control dc-dc converter to improve dynamic characteristicsâ€, Proc. IEEE International Conference on Power Electronics and Drive Systems, pp. 763-767, Nov. 2007.

T. Hastie, R. Tibshirani and J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction, Springer, 2001.

O. A. Kossov, â€Comparative analysis of chopper voltage regulators with LC filter,†IEEE Trans. Magnetics, Vol. MAG-4, no.4, pp. 712-715, 1968.

H. Matsuo and K. Harada, â€Characteristics of dc-dc converter in the discontinuous mode of the reactor currentâ€, Trans. IEICE of Japan, vol. J61-C, no.1, pp. 33-40, 1978.




DOI (PDF): https://doi.org/10.20508/ijrer.v1i2.36.g38

Refbacks

  • There are currently no refbacks.


Online ISSN: 1309-0127

Publisher: Gazi University

IJRER is cited in SCOPUS, EBSCO, WEB of SCIENCE (Clarivate Analytics);

IJRER has been cited in Emerging Sources Citation Index from 2016 in web of science.

WEB of SCIENCE in 2025; 

h=35,

Average citation per item=6.59

Last three Years Impact Factor=(1947+1753+1586)/(146+201+78)=5286/425=12.43

Category Quartile:Q4