Model Predictive Current Control of Switched Reluctance Motors With Inductance Auto-Calibration
IEEE transactions on industrial electronics (1982. Print)
The thesis is composed of three papers, which investigate the application of Model Predictive Controller (MPC) for current control of Switched Reluctance Motor (SRM). Since the conventional hysteresis current control method is not suitable for high power SRM drive system with low inductance and limited switching frequency, MPC is a promising alternative approach for this application. The proposed MPC can cope with the measurement noise as well as uncertainties within the machine inductance
... ine inductance profile. In the first paper, a MPC current control method for Double-Stator Switched Reluctance Motor (DSSRM) drives is presented. A direct adaptive estimator is incorporated to follow the inductance variations in a DSSRM. In the second paper, the Linear Quadratic (LQ) form and dynamic programming recursion for MPC are analyzed, afterwards the unconstrained MPC solution for stochastic SRM model is derived. The Kalman filter is employed to reduce the variance of measurement noises. Based on Recursive Linear-Square (RLS) estimation, the inductance profile is calibrated dynamically. In the third paper, a simplified recursive MPC current control algorithm for SRM is applied for embedded implementation. A novel auto-calibration method for inductance surface estimation is developed to improve current control performance of SRM drive in statistic terms. v ACKNOWLEDGMENTS First of all, I would like to express my deep gratitude to my advisor Dr. Pourya Shamsi. Without his patience, guidance, and support, my Master program won't move so smoothly. In addition, his solid theoretical foundation and rich practical experiences help me overcome difficulties in research. My sincere thanks also go to other respectable committee members, Dr. Ferdowsi and Dr. Kimball. I benefit a lot from their insightful teaching, academic seminars, and colloquiums. Moreover, I want to thank all teachers who had taught me in my two year Master program in Missouri University of Science and Technology. Their contribution of time, hard working, and advice to my academic improvement are highly appreciated.