Adjustable output voltage Zeta converter using neural network adaptive model reference control
- 1 December 2011
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in The 2nd International Conference on Control, Instrumentation and Automation
Abstract
Zeta converters are the fourth-order DC-DC converters capable of operating in both step-up and step-down modes and do not suffer from the polarity reversal problem. There are many applications which require a variable output voltage commanded by an external reference signal. So, the Zeta converters can be particularly useful for such applications. To achieve a Zeta converter with adjustable output voltage capable of following an external reference signal smoothly and accurately, there will be a need for a suitable control system. Since the Zeta converter model that is used in this paper is nonlinear, we propose a combination scheme of model reference adaptive control (MRAC) with neural networks (NN). In this paper, we propose and design a neural network adaptive model reference controller to control the output voltage of Zeta converter. Simulation results show the effectiveness of the proposed scheme for the Zeta converters with adjustable output voltage.Keywords
This publication has 7 references indexed in Scilit:
- Modeling and control of a Zeta converterPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010
- MRAC Combined Neural Networks for Ultra-Sonic MotorJSME International Journal Series C, 2006
- Application of Neural Networks to MRAC for the Nonlinear Magnetic Levitation SystemJSME International Journal Series C, 2006
- Fundamentals of Power ElectronicsPublished by Springer Science and Business Media LLC ,2001
- Neural Networks for Modelling and Control of Dynamic SystemsPublished by Springer Science and Business Media LLC ,2000
- Intelligent Control Based on Flexible Neural NetworksPublished by Springer Science and Business Media LLC ,1999
- Identification and control of dynamical systems using neural networksIEEE Transactions on Neural Networks, 1990