Fuzzy Adaptive Internal Model Control Schemes for PMSM Speed-Regulation System Shihua Li; Hao Gu Industrial Informatics, IEEE Transactions on Volume: 8,

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Presentation transcript:

Fuzzy Adaptive Internal Model Control Schemes for PMSM Speed-Regulation System Shihua Li; Hao Gu Industrial Informatics, IEEE Transactions on Volume: 8, Issue: 4 Digital Object Identifier: /TII Publication Year: 2012, Page(s): 767 – 779 IEEE JOURNALS & MAGAZINES Student : 鍾子涵

Abstract 2 In this paper, the speed regulation problem for permanent magnet synchronous motor (PMSM) system under vector control framework is studied. First, a speed regulation scheme based on standard internal model control (IMC) method is designed. For the speed loop, a standard internal model controller is first designed based on a first-order model of PMSM by analyzing the relationship between reference quadrature axis current and speed. For the two current loops, PI algorithms are employed respectively. Second, considering the disadvantages that the standard IMC method is sensitive to control input saturation and may lead to poor speed tracking and load disturbance rejection performances, a modified IMC scheme is developed based on a two-port IMC method, where a feedback control term is added to form a composite control structure. Third, considering the case of large variations of load inertia, two adaptive IMC schemes with two different adaptive laws are proposed.

Proposed PI-/PD-like FNN controller structure 3

4

FNN PI.PD structure 5

6

Block diagram of the hardware apparatus 7

under normal condition 8

under zero speed 9

under disturbance 10

under load 11

under constant speed 12

poor initial condition & training improvement 13

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REFERENCES [13] S.-J. Ho, L.-S. Shu, and S.-Y. Ho, “Optimizing fuzzy neural networks for tuning PID controllers using an orthogonal simulated annealing algorithm OSA,” IEEE Trans. Fuzzy Syst., vol. 14, no. 3, pp. 421–434, Jun [14] I. del Campo, J. Echanobe, G. Bosque, and J. M. Tarela, “Efficient hardware/ software implementation of an adaptive neuro–fuzzy system,” IEEE Trans. Fuzzy Syst., vol. 16, no. 3, pp. 761–778, Jun [15] M. N. Uddin and M. A. Rahman, “Development and implementation of a hybrid intelligent controller for interior permanent-magnet synchronous motor drives,” IEEE Trans. Ind. Appl., vol. 40, no. 1, pp. 68–76, Jan./Feb [16] dSPACE User’s Guide, Digital Signal Processing and Control Engineering, dSPACE, Paderborn, Germany, [17] G Technical Data Manual, Moog Aerospace, East Aurora, New York, [18] T Technical Data Manual, Moog Aerospace, East Aurora, New York,

Thank you for your attention. 16

Q & A. 17