Recursive-Extended-Least-Squares-Based Model Predictive Position Control for the Planar Switched Reluctance Motor

High-precision trajectory tracking is necessary for planar switched reluctance motors (PSRMs). In this paper, parameter identification is considered, and a model predictive position control (MPPC) algorithm is proposed based on recursive extended least squares (RELS) to enhance the trajectory tracking performance of the laboratory-developed PSRM. The mathematic model and parameter identification model of the PSRM are given. The predictive model, cost function, and control law of the MPPC based on RELS are derived. The effectiveness of the proposed RELS-based MPPC algorithm is verified via simulation and experiment.