Continuous-Set Model Predictive Control for Interior Permanent Magnet Motors: Maximum Torque per Ampere and Flux Weakening Operation

The objective of the thesis is to establish robust and efficient torque control for the Internal Permanent Magnet Motors by using Model Predictive Control (MPC). The control complexity is high due to the nonlinearities and coupling of the system. In addition, voltage and current limitations of the motor must also be accounted for to enable a wider speed operating range. These challenges pose an ideal opportunity for the application of predictive control methods, with the potential to improve performance and efficiency. The proposed solution represented in the thesis is a Continuous-set Model Predictive Torque Controller. The MPC-based approach enables the motor to operate across Maximum Torque Per Ampere (MTPA) and Flux Weakening (FW) operating regions and ensure smooth transitions between them. Furthermore, this control strategy is able to handle the nonlinearities and the mutual coupling between controlled variables, and voltage and current constraints. On top of that, the developed controller is designed with the minimum possible number of tunable parameters, which improves practicality and reduces the likelihood of errors.