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Proceedings Paper

Sliding mode control using neural network for IPM machine
Author(s): Min Chan Kim; Jae Hoon Kim; Seung Kyu Park; Tae Sung Yoon
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Paper Abstract

In this paper, a novel sliding mode controller is proposed by using neural network for IPM machine. The current control for interior permanent magnet machines is more complicate than surface permanent magnet machine because of its torque characteristic depending on the reluctance. For high performance torque control, it requires state decoupling between the dcurrent and q-current dynamics. However the variation of the inductances, which couples the state dynamics of the currents, makes the state decoupling difficult. This paper presents a novel approach for fully decoupling the states cross-coupling using sliding mode control with neural network. The sliding mode control method is based on the error between reference currents and the currents with state decoupling which have to follow the references. In the conventional sliding mode control, the dynamic of sliding surface is not as same as nominal dynamic of original system. To overcome this problem, this paper proposes a new design method of a sliding surface without defining any additional dynamic state by using neural network. Finally, the proposed sliding surface can have the dynamics of nominal system controlled by PI controller.

Paper Details

Date Published: 7 January 2008
PDF: 6 pages
Proc. SPIE 6794, ICMIT 2007: Mechatronics, MEMS, and Smart Materials, 67940E (7 January 2008); doi: 10.1117/12.784178
Show Author Affiliations
Min Chan Kim, Changwon National Univ. (South Korea)
Jae Hoon Kim, Changwon National Univ. (South Korea)
Seung Kyu Park, Changwon National Univ. (South Korea)
Tae Sung Yoon, Changwon National Univ. (South Korea)

Published in SPIE Proceedings Vol. 6794:
ICMIT 2007: Mechatronics, MEMS, and Smart Materials
Minoru Sasaki; Gisang Choi Sang; Zushu Li; Ryojun Ikeura; Hyungki Kim; Fangzheng Xue, Editor(s)

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