ROBUST FINITE CONTROL SET MODEL PREDICTIVE
CURRENT CONTROL FOR IM APPLIED TO ELECTRICAL TRACTION FOR TRACTORS
This thesis presents a model-based control type of finite control set, with the addition of a
robust portion capable of dealing with the uncertainties of the driven induction motor.
The ability to minimize the impacts of parametric variations is crucial for the controller’s
performance since it is model-based. These variations degrade the control’s performance.
The work will review existing control techniques for induction motors in tractor traction
applications, as a second objective of the thesis is the electrification of agricultural tractors.
After reviewing the controls, the induction motor is modeled in the stationary reference
for the design of the robust predictive controller, which uses a cost function to minimize
the error between the electromagnetic torque and the stator flux to perform the speed
control. Finally, simulation results are presented to demonstrate the proposed control’s
performance together with the thesis’s partial conclusions.