研究目的
To propose a novel inverse force function using sparse least squares support vector machines (LS-SVMs) for nonlinear modeling of the inverse force function for precise motion of a planar switched reluctance motor (PSRM).
研究成果
The proposed inverse force function using sparse LS-SVMs provides precise phase current command for precise motion of the PSRM, demonstrating feasibility, validity, and promising industrial applicability.
研究不足
The inverse force function has small training error and testing error except that the estimated phase current is less than 1 A. For the PSRM, the operating current mainly works ranging from 2 to 6 A.
1：Experimental Design and Method Selection:
The methodology involves obtaining training and testing sets from experimental measurement and developing a sparse LS-SVMs regression to model the inverse force function.
2：Sample Selection and Data Sources:
Experimental measurement of thrust force versus phase current from 0 to 10 A versus position in a pole pitch is performed to obtain the sample set.
3：List of Experimental Equipment and Materials:
The PSRM system includes the PSRM, dSPACE controller, current drivers, linear optical encoders, PC, and power supply.
4：Experimental Procedures and Operational Workflow:
The inverse force function is tested via the testing set to assess its feasibility and applied to the PSRM system for trajectory tracking.
5：Data Analysis Methods:
The root-mean-square error of the sparse LS-SVMs is defined to assess the learning and generalization performances.
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