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7 January 2008 Sliding mode control using neural network for IPM machine
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Proceedings Volume 6794, ICMIT 2007: Mechatronics, MEMS, and Smart Materials; 67940E (2008)
Event: ICMIT 2007: Mechatronics, MEMS, and Smart Materials, 2007, Gifu, Japan
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.
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Min Chan Kim, Jae Hoon Kim, Seung Kyu Park, and Tae Sung Yoon "Sliding mode control using neural network for IPM machine", Proc. SPIE 6794, ICMIT 2007: Mechatronics, MEMS, and Smart Materials, 67940E (7 January 2008);

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