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Implementation of MPC on embedded system for control of induction motors


X. Li

Master Thesis, FS16

Electrical drives in industrial applications are usually not optimized in terms of energy consumption. In this thesis, we present a nonlinear trajectory planner for the sensorless control of the induction motor (IM). Three different state-of-the-art observers, namely model reference adaptive system (MRAS), extended Kalman filter (EKF) and unscented Kalman filter (UKF), are utilized to estimate system states of the IM. They are distinguished by various working conditions of the IM. Furthermore, the nonlinear model of the mutual inductance can be incorporated into the observers to improve the accuracy.
The trajectory planner is designed to minimize the losses in the IM. Two approaches, the maximum torque per ampere (MTPA) approach and the full loss model based approach, are designed as model predictive controllers and implemented in the IM to reach the goal. The losses of the IM and the solve time of the two trajectory planners are compared in simulations. The use of trajectory planner can reduce the losses significantly. Further reduction of the losses can be achieved by implementing nonlinear model of the mutual inductance in trajectory planners.

Supervisors: Damian Frick, Giampaolo Torrisi


Type of Publication:

(12)Diploma/Master Thesis

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2017:IFA_5631
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