Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors
Date
2015
Authors
Yu, J.
Shi, P.
Dong, W.
Chen, B.
Lin, C.
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Journal article
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IEEE Transactions on Neural Networks and Learning Systems, 2015; 26(3):640-645
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Jinpeng Yu, Peng Shi, Wenjie Dong, Bing Chen, and Chong Lin
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Abstract
This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping design. Next, under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced to only one, and the designed neural controllers structure is much simpler than some existing results in literature, which can guarantee that the tracking error converges to a small neighborhood of the origin. Then, simulations are given to illustrate the effectiveness and potential of the new design technique.
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© 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.