Nonlinear Modeling of the Inverse Force Function for the Planar Switched Reluctance Motor Using Sparse Least Squares Support Vector Machines

In the 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 first 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.