MTPA control of mechanical sensorless IPMSM based on adaptive nonlinear control

Area of Science:

• Electrical Engineering
• Control Systems
• Power Electronics

Background:

• Interior Permanent Magnet Synchronous Motors (IPMSMs) are widely used in electric drives due to their high efficiency and power density.
• Achieving Maximum Torque per Ampere (MTPA) is crucial for optimizing the performance of IPMSM drives.
• Traditional control methods often struggle with parameter variations and require accurate motor models.

Purpose of the Study:

• To propose an adaptive nonlinear control scheme for MTPA control in IPMSM drives.
• To develop a robust control strategy that can handle online parameter variations.
• To improve the efficiency and dynamic performance of IPMSM drives.

Main Methods:

• Adaptive Input-Output State Feedback Linearization (AIOFL) method in the rotor d-q axis reference frame.
• Lyapunov theory for ensuring drive system control stability.
• Nonlinear optimization technique for MTPA strategy based on minimizing current amplitude.
• Online estimation of motor inductances and stator resistance using estimation laws and PI controllers.
• Rotor position detection through online estimation of stator flux and q-axis inductance.

Main Results:

• The proposed AIOFL-based control scheme effectively implements MTPA control for IPMSM drives.
• Online estimation laws ensure asymptotic convergence of motor inductance estimation errors to zero.
• Stator resistance and rotor position are accurately estimated online.
• Simulation and experimental results validate the effectiveness and capability of the proposed control method.

Conclusions:

• The developed adaptive nonlinear control scheme provides a robust and effective solution for MTPA control in IPMSM drives.
• The online parameter estimation capabilities enhance the drive system's adaptability and performance.
• The proposed method demonstrates significant potential for improving the efficiency and control accuracy of electric motor drives.