FPGA-BASE ADAPTIVE WAVELET NEURCONTROLLER DESIGN FOR DC-DC CONVERTER
DC-DC converters are the devices which can convert a certain electrical voltage to another level of electrical voltage. They are very popular used because of the high efficiency and small size. This paper proposes an adaptive wavelet neurcontrollcr system for the DC-DC converters. The proposed adaptive wavelet neurcontrollcr system is composed of a neural controller and a robust controller. The neural controller uses a wavelet neural network (WNN) to online mimic an ideal controller, and the robust controller is designed to achieve L2 tracking performance with desiredattenuation level. Finally, a field-programmable gate array chip is adopted to implement the proposed adaptive wavelet neurcontroller scheme for possible low-cost and high-performance industrial applications. The experimental results are provided to demonstrate the proposed adaptive wavelet neurcontroller system can cope with the input voltage and load resistance variations to ensure the stability while providing fast transient response.
Adaptive control Robust control Wavelet neural network DC-DC converter
CHUN-FEI HSU TSU-TIAN LEE SHUEN-LIANG WANG
Department of Electrical Engineering, Chung Hua University, Hsinchu 300, Taiwan, Republic of China Department of Electrical Engineering, National Taipei University of Technology, Taipei 106, Taiwan,
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
3833-3838
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）