In collaboration with General Motors Company, we have developed a model predictive control scheme for torque control of a Permanent Magnet Synchronous Motor (PMSM). 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 moves are obtained offline by multiparametric optimization. The performance of the proposed control strategy is evaluated in Processor-in-the-Loop experiments, carried out on a low-cost Digital Signal Processor commonly used in motion control. Results show that the proposed approach is able to improve the torque dynamics with respect to conventional controllers and that an embedded implementation is feasible in terms of required computational power and memory.
Results have been published in the paper Online Model Predictive Torque Control for Permanent Magnet Synchronous Motors, presented at the 16th IEEE International Conference on Industrial Technology (ICIT 2015), held in Sevilla, Spain, in March 2015. The paper is available in IEEE Xplore.
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March 2020 update:
New results, including solver certification, have been published in the paper Embedded Model Predictive Control with Certified Real-Time Optimization for Synchronous Motors, appeared in IEEE Transactions on Control Systems Technology.