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Generalized Model Predictive Torque Control of Synchronous Machines
  • Kyunghwan Choi,
  • Jongseok Kim,
  • Ki-Bum Park
Kyunghwan Choi
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Jongseok Kim
Ki-Bum Park

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Abstract

This study presents a generalized model predictive torque control (GMPTC) scheme that applies to all types of synchronous machines (SMs), which are used for vehicle traction. The GMPTC problem is formulated to simultaneously minimize the torque error and the performance index (PI) while satisfying the voltage and current constraints. The PI can be defined by any function to be minimized to enhance the SM drive's performance, such as copper loss and inverter loss. When the torque command is not achievable due to either of the constraints, the problem is defined differently to maximize the torque magnitude under the two constraints. The GMPTC problem, a nonlinear optimization problem, is solved based on either a continuous control set (CCS) or finite control set (FCS) using the augmented Lagrangian method. The GMPTC scheme guarantees optimal operation under all operating regions, including the maximum torque per ampere, flux weakening, maximum current, and maximum torque per voltage, even without additional controllers. The GMPTC method is practical and easy to implement because it involves one optimization problem with a small number (four to five) of tuning parameters. This aspect differs from existing model predictive control schemes for SMs that involve an additional optimization, usually solved offline, to obtain optimal references for the stator currents or stator flux linkages. The effectiveness of the proposed GMPTC method is demonstrated numerically and experimentally by controlling interior permanent magnet synchronous machines based on both the CCS and FCS.
12 Mar 2024Submitted to TechRxiv
18 Mar 2024Published in TechRxiv