Cogging effect minimization in PMSM position servo system using dual high-order periodic adaptive learning...

Area of Science:

• Electrical Engineering
• Control Systems
• Robotics

Background:

• Cogging effect in permanent magnetic synchronous motors (PMSMs) presents a significant challenge, causing position-dependent periodic disturbances.
• Existing control methods struggle to fully mitigate the impact of cogging force, viscous friction, and external loads in PMSM systems.

Purpose of the Study:

• To propose and validate a novel Dual High-Order Periodic Adaptive Learning Compensation (DHO-PALC) method for PMSM servo control.
• To minimize the detrimental effects of cogging on PMSM position and velocity servo system performance.

Main Methods:

• Development of a simulation system model for PMSM position servo control, incorporating cogging force, viscous friction, and applied load.
• Implementation of the DHO-PALC scheme, utilizing stored information from previous periods of tracking error and cogging force estimates for control law updates.
• Asymptotic stability analysis of the proposed DHO-PALC scheme.

Main Results:

• Simulations demonstrate the effectiveness of the DHO-PALC method in a PMSM servo system model.
• DHO-PALC exhibits faster learning convergence compared to first-order periodic adaptive learning compensation (FO-PALC) under constant speed reference.
• For periodic reference signals, DHO-PALC achieves superior convergence speed and a significantly smaller final error bound than FO-PALC.

Conclusions:

• The proposed DHO-PALC method offers a robust solution for mitigating cogging effects in PMSM servo systems.
• DHO-PALC demonstrates enhanced performance in terms of convergence speed and steady-state error reduction compared to first-order methods.
• This approach holds promise for improving the precision and efficiency of PMSM-based applications.