Design of an adaptive model predictive field oriented control for a six-phase permanent magnet synchronous motor

Among the various types of e-motors the Permanent Magnet Synchronous Motor (PMSM) is widely used in automotive and industry due to its high efficiency and dynamic performances. For their reliability, Six phases (Instead of classical three phase ones) PMSMs are becoming more and more important in automotive technology. These kind of motors can be controlled with some modified version of the three phase Field Oriented Control (FOC). It is also possible to run only some of the six phases of the motors this leed us to the possibility of to implement some fault tolerant strategies. In this work, one of those modified version of the FOC algorithm was been used for to control a six phase motor. In addiction the two PI current controllers inside the algorithm has been substituted with an adaptive model predictive controller for improve efficiency, robustness and for provide hard costraints on voltage and current values instead of classical saturations with antiwindup technic, therefore improving reliability. The model and the control algorithm will be validated by means of on target rapid prototyping aproach using a dSPACE platform. Which allows us to do real time simulations and hardware in the loop tests. The topics of electrical drives and Model Predictive control allows the possibility of to work on a specific problem without compromise the generality. Which is the characteristic of the control theory that fascinated me the most when I started this travel. In this work, efficiency and robustness were taken into account From a practical but still rigorous point of view for the designing of a powertrain system for electric cars. At the beginning a theorycal approach for to describe the system was used for to build a state space model of the motor. Different simulink models of different degree of complessity were implemented. Several simulation were carried out for to validate the designed controllers: classical Field oriented controller and Field oriented controller with Model Predictive controller instead of classical PI current loops. Performance and Disturb rejection were evaluated also in presence of parametric errors. From simulations it can be seen that Model predictive controller bring to a more efficient behaviour. An experimental setup was developed for to validate the control algorithms in real world. The technical problems were solved at the beginning with a pragmatic approach but several problems in different conditions bring us to go back to theory for to design an estimation algorithm. The measures are still not good enough for a good closed loop experiment. The solution could be the use of an encoder or the tuning of the Kalman filter. A real time simulation with dSPACE was done for to see if the Model Predictive Control could run in realtime. Was experimented that it can but not faster than 10 KHz