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In advanced manufacturing industry, planar switched reluctance motors (PSRMs) have proved to be a promising candidate due to their advantages of high precision, low cost, low heat loss, and ease of manufacture. However, their inverse force function, which provides vital phase current command for precise motion, is highly nonlinear and hard to be accurately modeled. This paper proposes a novel inverse force function using sparse least squares support vector machines (LS-SVMs) to achieve nonlinear modeling for precise motion of a PSRM. The required training and testing sets of sparse LS-SVMs are firstly obtained from experimental measurement. A sparse LS-SVMs regression is further developed using training set to accurately model the inverse force function. Accordingly, the function is tested via the testing set to assess its feasibility. Finally, the proposed approach is applied to the PSRM system with dSPACE controller for trajectory tracking, and its effectiveness and superior performance are verified through experimental results. Her research interests include design and control of planar switched reluctance motors, and control theory and its application.Guang-Zhong Cao (M'15) received the B.Sc., M.Sc., and Ph.D. degrees in electrical engineering and automation from Xi'an Jiaotong University, He has published more than 60 articles in refereed journals and conferences. His research interests include motor control, and control theory and its application.Zheng-You He (M'10-SM'13) received the B.Sc. and M.Sc. degrees in computational mechanics from Chongqing University, Chongqing, China, in 1992 and 1995, respectively, and the Ph.D. degree in electrical engineering from Southwest . His research interests include signal processing and information theory applied to power system, and application of wavelet transforms in power system. J. F. Pan received the B.Sc. degree in electrical engineering from ChangChun

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