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

Model predictive position control (MPPC) is a promising control method for planar switched reluctance motors (PSRMs) to achieve high-precision trajectory tracking. However, the control performance of MPPC may deteriorate due to a mismatch between the system model and the actual system. To address this issue, online parameter identification can be used as an alternative method to model refinement. In this paper, an MPPC method based on recursive extended least squares (RELS) is proposed to improve the trajectory tracking performance of a laboratory-developed PSRM system. The conventional dynamic model and RELS-based parameter identification dynamic model for the PSRM are established. The predictive model is developed using the parameter identification dynamic model, with its parameters updated online. The control law is derived from the developed predictive model and the defined cost function, and the system stability is subsequently analyzed. The simulation and experimental results show improvement in the trajectory tracking performance of the PSRM system. The effectiveness of the proposed method is verified.