Improvement of Model Predictive Current Control Sensing Strategy for a Developed Small Flux-Switching Permanent...

Area of Science:

• Electrical Engineering
• Electromechanical Systems
• Control Theory

Background:

• Flux-switching permanent magnet motors (FSPM) require advanced control for optimal performance.
• Existing control strategies like PI controllers may not fully address nonlinearities and computational burdens.
• Model predictive current control (MPCC) offers high performance but can be complex.

Purpose of the Study:

• To develop an improved control system for small FSPM to enhance performance and torque sensing.
• To optimize FSPM design and control strategy for system integrity.
• To propose a modified MPCC strategy that reduces current waveform distortion and computational load.

Main Methods:

• Analytical magnetic circuit design and ANSYS Maxwell for motor parameter optimization.
• Uniform design experimentation (UDE) for performance investigation.
• Development of a modified MPCC strategy including EMF estimation, current prediction, and optimal vector selection, utilizing space vector modulation (SVM).

Main Results:

• The modified MPCC strategy demonstrated superior performance compared to traditional PI controllers.
• Improved dynamic response at low speeds (350 rpm) and effective resolution of nonlinear startup problems.
• Reduced computational burden and current waveform distortion achieved by the modified MPCC.

Conclusions:

• The proposed modified MPCC strategy is effective for FSPM control, offering better dynamic response and reduced complexity.
• The integrated approach, optimizing both motor design and control, enhances overall FSPM system integrity.
• The study validates improved torque sensing performance through simulation and experimental comparison.