Design of intelligent power controller for DC–DC converters using CMAC neural network

DC–DC converters are the devices which can convert a certain electrical voltage to another level of electrical voltage. They are very popularly used because of the high efficiency and small size. This paper proposes an intelligent power controller for the DC–DC converters via cerebella model articulation controller (CMAC) neural network approach. The proposed intelligent power controller is composed of a CMAC neural controller and a robust controller. The CMAC neural controller uses a CMAC neural network to online mimic an ideal controller, and the robust controller is designed to achieve L2 tracking performance with desired attenuation level. Finally, a comparison among a PI control, adaptive neural control and the proposed intelligent power control is made. The experimental results are provided to demonstrate the proposed intelligent power controller can cope with the input voltage and load resistance variations to ensure the stability while providing fast transient response and simple computation.

[1]  D. He,et al.  Fuzzy logic average current-mode control for DC/DC converters using an inexpensive 8-bit microcontroller , 2004, Conference Record of the 2004 IEEE Industry Applications Conference, 2004. 39th IAS Annual Meeting..

[2]  Chun-Fei Hsu,et al.  Supervisory intelligent control system design for forward DC-DC converters , 2006 .

[3]  Chih-Min Lin,et al.  Fuzzy–Neural Sliding-Mode Control for DC–DC Converters Using Asymmetric Gaussian Membership Functions , 2007, IEEE Transactions on Industrial Electronics.

[4]  Luis García de Vicuña,et al.  Current distribution control design for paralleled DC/DC converters using sliding-mode control , 2004, IEEE Transactions on Industrial Electronics.

[5]  F. Chang,et al.  Adaptive fuzzy CMAC control for a class of nonlinear systems with smooth compensation , 2006 .

[6]  Chih-Min Lin,et al.  Wavelet Adaptive Backstepping Control for a Class of Nonlinear Systems , 2006, IEEE Transactions on Neural Networks.

[7]  Tsu-Tian Lee,et al.  Hinfin tracking-based sliding mode control for uncertain nonlinear systems via an adaptive fuzzy-neural approach , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[8]  Yih-Guang Leu,et al.  Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems , 2005, IEEE Trans. Neural Networks.

[9]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[10]  Chih-Min Lin,et al.  Adaptive CMAC-based supervisory control for uncertain nonlinear systems , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  Chun-Fei Hsu,et al.  Self-Organizing Adaptive Fuzzy Neural Control for a Class of Nonlinear Systems , 2007, IEEE Transactions on Neural Networks.

[12]  C.-M. Lin,et al.  Robust cerebellar model articulation controller design for unknown nonlinear systems , 2004, IEEE Transactions on Circuits and Systems II: Express Briefs.

[13]  Chih-Min Lin,et al.  Neural-network-identification-based adaptive control of wing rock motions , 2005 .

[14]  A. Rubaai,et al.  Hardware implementation of an adaptive network-based fuzzy controller for DC-DC converters , 2004, IEEE Transactions on Industry Applications.

[15]  Chih-Hong Lin,et al.  An adaptive H∞ controller design for permanent magnet synchronous motor drives , 2005 .

[16]  Ali H. Nayfeh,et al.  Robust control of parallel DC-DC buck converters by combining integral-variable-structure and multiple-sliding-surface control schemes , 2002 .

[17]  J. Álvarez-Ramírez,et al.  A stable design of PI control for DC-DC converters with an RHS zero , 2001 .

[18]  Rong-Jong Wai,et al.  Implementation of LLCC-resonant driving circuit and adaptive CMAC neural network control for linear piezoelectric ceramic motor , 2004, IEEE Transactions on Industrial Electronics.

[19]  Dipti Srinivasan,et al.  Nonlinear function controller: a simple alternative to fuzzy logic controller for a power electronic converter , 2005, IEEE Transactions on Industrial Electronics.

[20]  Manuel A. Duarte-Mermoud,et al.  Multivariable predictive control of a pressurized tank using neural networks , 2005, Neural Computing & Applications.

[21]  J.-Y. Chen,et al.  Adaptive design of a fuzzy cerebellar model arithmetic controller neural network , 2005 .

[22]  Abraham Pressman,et al.  Switching Power Supply Design , 1997 .

[23]  D.A. Handelman,et al.  Theory and development of higher-order CMAC neural networks , 1992, IEEE Control Systems.

[24]  Chih-Min Lin,et al.  Adaptive RCMAC sliding mode control for uncertain nonlinear systems , 2006, Neural Computing & Applications.

[25]  Zne-Jung Lee,et al.  Robust and fast learning for fuzzy cerebellar model articulation controllers , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).