Parallel Computing Utilization in Nonlinear Model Predictive Control of Permanent Magnet Synchronous Motor

Abstract

Permanent Magnet Synchronous Motor (PMSM) drives are widely used for motion control industrial applications and electrical vehicle powertrains, where they provide a good torque-to-weight ratio and a high dynamical performance. With the increasing usage of these machines, the demands on exploiting their abilities are also growing. Usual control techniques, such as field-oriented control (FOC), need some workaround to achieve the requested behavior, e.g., field-weakening, while keeping the constraints on the stator currents. Similarly, when applying the linear model predictive control, the linearization of the torque function and defined constraints lead to a loss of essential information and sub-optimal performance. That is the reason why the application of nonlinear theory is necessary. Nonlinear Model Predictive Control (NMPC) is a promising alternative to linear control methods. However, this approach has a major drawback in its computational demands. This paper presents a novel approach to the implementation of PMSMs’ NMPC. The proposed controller utilizes the native parallelism of population-based optimization methods and the supreme performance of field-programmable gate arrays to solve the nonlinear optimization problem in the time necessary for proper motor control. The paper presents the verification of the algorithm’s behavior both in simulation and laboratory experiments. The proposed controller’s behavior is compared to the standard control technique of FOC and linear MPC. The achieved results prove the superior quality of control performed by NMPC in comparison with FOC and LMPC. The controller was able to follow the Maximal Torque Per Ampere strategy without any supplementary algorithm, altogether with constraint handling.

Keywords

• Evolutionary algorithms
• motor control
• nonlinear control
• parallel computing
• predictive control