Sliding Mode Control of SPMSM Drivers: An Online Gain Tuning Approach with Unknown System Parameters

This paper proposes an online gain tuning algorithm for a robust sliding mode speed controller of surface-mounted permanentmagnet synchronous motor (SPMSM) drives. The proposed controller is constructed by a fuzzy neural network control (FNNC)term and a sliding mode control (SMC) term. Based on a fuzzy neural network, the first term is designed to approximate thenonlinear factors while the second term is used to stabilize the system dynamics by employing an online tuning rule. Therefore,unlike conventional speed controllers, the proposed control scheme does not require any knowledge of the system parameters. Asa result, it is very robust to system parameter variations. The stability evaluation of the proposed control system is fully describedbased on the Lyapunov theory and related lemmas. For comparison purposes, a conventional sliding mode control (SMC) schemeis also tested under the same conditions as the proposed control method. It can be seen from the experimental results that theproposed SMC scheme exhibits better control performance (i.e., faster and more robust dynamic behavior, and a smallersteady-state error) than the conventional SMC method.

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Cite this article

[IEEE Style]

J. Jung, V. Q. Leu, D. Q. Dang, H. H. Choi, T. H. Kim, "Sliding Mode Control of SPMSM Drivers: An Online Gain Tuning Approach with Unknown System Parameters," Journal of Power Electronics, vol. 14, no. 5, pp. 980-988, 2014. DOI: 10.6113/JPE.2014.14.5.980.

[ACM Style]

Jin-Woo Jung, Viet Quoc Leu, Dong Quang Dang, Han Ho Choi, and Tae Heoung Kim. 2014. Sliding Mode Control of SPMSM Drivers: An Online Gain Tuning Approach with Unknown System Parameters. Journal of Power Electronics, 14, 5, (2014), 980-988. DOI: 10.6113/JPE.2014.14.5.980.