A Sensorless Predictive Current Controlled Boost Converter by Using an EKF with Load Variation Effect Elimination...

Area of Science:

• Electrical Engineering
• Control Systems

Background:

• Accurate inductor current measurement is crucial for boost converter control.
• Traditional current sensors add cost, delay, noise, and reliability concerns.
• Sensorless control offers a cost-effective and reliable alternative.

Purpose of the Study:

• To develop a sensorless current control technique for boost converters.
• To improve inductor current estimation and output voltage filtering accuracy.
• To address challenges posed by load variations in sensorless control systems.

Main Methods:

• Developed an accurate nonlinear model of the boost converter, including parasitics.
• Proposed an Extended Kalman Filter (EKF) for inductor current estimation and output voltage filtering.
• Integrated a load variation elimination (LVEE) module to handle dynamic load changes.
• Implemented a predictive average current controller for precise current regulation.

Main Results:

• The EKF effectively estimates inductor current and filters output voltage, mimicking sensored control performance.
• The LVEE module mitigates errors caused by load variations.
• The predictive average current controller significantly improves transient response, reaching reference current in two switching cycles.
• Experimental results confirm stable operation and robust large-signal transient tracking.

Conclusions:

• The proposed sensorless control strategy provides an accurate, cost-effective, and reliable solution for boost converters.
• The integration of EKF and LVEE module enhances system performance under dynamic load conditions.
• This method offers superior transient response compared to conventional voltage-controlled systems.