Direct Inverse Control for ECRH Voltage Based on ICA-CMAC

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Abstract:

The negative high-voltage power supply of Electron Cyclotron Resonance Heating (ECRH) is a nonlinear system with serve sensitivity and it is not well for traditional controller to meet restrict demand on stability and quick response. Based on the concept of credit a novel CMAC is designed to accelerate the convergence of traditional CMAC and also is used as an intelligent controller for the power of ECRH based on the idea on direct inverse control. Experiment results show that ICA-CMAC can control the power of ECRH well with shorter settling time and less CPU consumption thus the validity of ICA-CMAC is determined.

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Periodical:

Advanced Materials Research (Volumes 268-270)

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1755-1758

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Online since:

July 2011

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] S.G.E. Pronko, S. Delaware, et al. The Performance of 8. 4MW Modulator/Regulator Power System for the Electron Cyclotron Heating Upgrade at DIII-D. In: The 14th Topical Meeting on the Technology of Fusion Energy, Park City, Utah:2000. 10. (GA-A23551).

DOI: 10.13182/fst01-a11963393

Google Scholar

[2] Nerem A, Kellman D H, Pronko S C E, et al. Circuit Modeling and Feedback Controller Development of the 8. 4MW Modulator/Regular Power System for the Electron Cyclotron Heating Facility Upgrade at D III-D. In: The 14th Topical Meeting on the Technology of Fusion Energy, . Park City, Utah:2000. 10. (GA-A23524).

DOI: 10.13182/fst01-a11963394

Google Scholar

[3] Shaowu DU, Research on Negative High high-voltage pulse power supply for ECRH . Hefei University of Technology, (2004).

Google Scholar

[4] XU Li, JIANG Jiang-ping. CMAC Neural Network Based Learning Control of the CSTR System, Control and Decision, Vol. 7, No. 2, 131~136, (1992).

DOI: 10.1109/sicici.1992.637733

Google Scholar

[5] Ge Yingqi, Luo Xiaoping, Du Pengying. A New Improved CMAC Neural Network, 2010 Chinese Control and Decision Conference, CCDC 2010: 3271-3274.

DOI: 10.1109/ccdc.2010.5498618

Google Scholar

[6] Nie J, Linkens D A. FCMAC: A fuzzified cerebellar model articulation controller with self-organizing capacity, Automatica, 1994, 30(4): 655-664.

DOI: 10.1016/0005-1098(94)90154-6

Google Scholar

[7] Geng Z J, Mccullough C L. Missile control using fuzzy cerebellar model arithmetic computer neural networks. J Guid, Control, Dynamic, 1997, 20(3): 557-565.

DOI: 10.2514/2.4077

Google Scholar

[8] Shun F S, Ted T, Hung T H. Credit assigned CMAC and its application to online learning robust controllers. IEEE Trans on Systems, Man, and Cybernetics-part B: Cybernetics, 2003, 33 (2) : 202-2l3.

DOI: 10.1109/tsmcb.2003.810447

Google Scholar

[9] Zhu Da-qi, Zhang Wei. Nonlinear identification algorithm of the improved CMAC based on balanced learning . Control and Decision, 2004, 19 (12) : 1425-1428.

Google Scholar