A finite-set model predictive position controller for the permanent magnet synchronous motor

In this work a position controller for the permanent magnet synchronous motor is developed. The design is based on ideas of time-optimal control and predictive control and considers constraints for both torque and speed. The proposed controller takes the form of a state feedback. Its inputs are the position reference and the system state, and its output is the switching state to be applied by the power converter in the next sampling instant. The design procedure starts by solving a time-optimal control problem for a simple system: the triple integrator. The solution is then used to devise a state feedback rule for the motor. Although the control scheme does not have a completely cascaded structure, the approach taken establishes a hierarchy, with the error in the position having the highest priority. This separates the controller in two main building blocks, which work at different abstraction levels. The purpose of the first block is to generate references for the relevant states of the motor (speed and torque), based on long term predictions, which are realized using the triple integrator model. The second block is a receding horizon controller with a prediction horizon of one time step and a specially devised cost function, for the simultaneous control of speed, torque and stator currents. The performance of the controller is verified with experimental results.