Current control designed with model based predictive control for six-phase motor drives

Area of Science:

• Electrical Engineering
• Control Systems

Background:

• Finite control set model-based predictive control (FS-MPC) offers advantages over traditional field-oriented control for multiphase drives.
• Existing FS-MPC methods face challenges in regulating (x-y) current components, leading to machine losses and high computational demands.

Purpose of the Study:

• To comparatively evaluate an enhanced predictive current control technique against conventional and hybrid methods for asymmetrical six-phase induction motor drives.
• To assess performance based on current tracking accuracy, stator current total harmonic distortion (THD), and computational burden.

Main Methods:

• Experimental comparison of four predictive control strategies: one enhanced, one conventional, and two hybrid.
• Evaluation metrics included mean squared error (MSE) for current tracking and THD for stator currents.
• Computational burden was quantified using the number of floating-point operations (FLOPs).

Main Results:

• The enhanced predictive current control demonstrated superior current tracking performance compared to conventional and hybrid methods.
• Lower total harmonic distortion in stator currents was observed with the enhanced technique.
• The enhanced method achieved these improvements while maintaining a comparable or slightly increased computational burden.

Conclusions:

• The enhanced predictive current control technique presents a viable solution for improving the performance of asymmetrical six-phase induction motor drives.
• It effectively mitigates drawbacks of conventional methods regarding current regulation and harmonic content.
• The trade-offs between performance enhancement and computational cost are clearly defined for transient and steady-state operation.