A Dynamic Self-Adjusting System for Permanent Magnet Synchronous Motors Using an Improved Super-Twisting Sliding Mode...

Area of Science:

• Electrical Engineering
• Control Systems
• Robotics

Background:

• Sensorless control of permanent magnet synchronous motors (PMSMs) is crucial for applications requiring precise motion control.
• Parameter mismatches in PMSMs can significantly degrade the performance and robustness of existing sensorless control strategies.
• Traditional sliding mode observers (SMOs) often require low-pass filters (LPFs) to mitigate chattering, adding complexity and potentially affecting dynamic response.

Purpose of the Study:

• To develop a novel sliding mode observer (SMO) for robust sensorless control of PMSMs that addresses parameter mismatches.
• To enhance the precision of rotor position estimation while simultaneously suppressing observer chattering.
• To simplify the design and implementation of sensorless control systems for PMSMs.

Main Methods:

• Design of an SMO incorporating an adjustable error factor to reduce chattering and eliminate the need for a low-pass filter (LPF).
• Analysis of the error factor's impact on current, speed, and position estimation within the SMO framework.
• Development of a neural network algorithm to determine the optimal error factor, balancing chattering suppression and control accuracy.
• Proposal of a neural network-based self-adjusting SMO model for automatic error factor adaptation to varying motor operating conditions.

Main Results:

• The proposed adjustable error factor SMO effectively reduces chattering without requiring an LPF.
• The neural network-based method successfully balances chattering suppression with high estimation accuracy for rotor position, speed, and current.
• Simulation and experimental results validate the feasibility and effectiveness of the self-adjusting SMO approach.

Conclusions:

• The novel self-adjusting SMO offers a robust and simplified solution for sensorless control of PMSMs under parameter variations.
• The adaptive error factor mechanism significantly improves observer performance and system reliability.
• This approach holds promise for enhancing the performance of PMSM drives in various industrial applications.