Online model predictive torque control for Permanent Magnet Synchronous Motors

In this paper a Model Predictive Control (MPC) method for torque control of a Permanent Magnet Synchronous Motor (PMSM) is presented. The proposed approach takes into account constraints on voltages and currents, and allows the use of modulation techniques that eliminate the side effects caused by the direct transistor actuation performed by Model Predictive Direct Torque Control (MP-DTC) approaches. The optimization problem resulting from the proposed MPC formulation is solved online, in contrast with what is done in explicit MPC, where the optimal control law is obtained offline by multiparametric optimization. The performance of the proposed control strategy is evaluated in Processor-In-the-Loop (PIL) experiments, carried out on a low cost Digital Signal Processor (DSP) commonly used in motion control. Results show that the proposed approach is able to improve the torque dynamics with respect to a conventional controller, and that an embedded implementation is feasible in terms of required computational power and memory.