A New Real-Time Pinch Detection Algorithm Based on Model Reference Kalman Prediction and SRMS for Electric Adjustable...

Area of Science:

• Robotics and Control Systems
• Mechatronics Engineering
• Human-Computer Interaction

Background:

• Electric height-adjustable desks require precise control for safe operation.
• Noise and signal coupling in current sampling can hinder accurate load detection.
• Existing pinch detection methods may lack reliability under varying loads.

Purpose of the Study:

• To develop a robust algorithm for accurate pinch detection in electric height-adjustable desks.
• To enhance user safety by preventing collisions and accidents.
• To improve the reliability of motor control under dynamic load conditions.

Main Methods:

• Established a system model for electric height-adjustable desks, considering DC motor characteristics and system coupling.
• Proposed a novel model reference Kalman prediction method for precise load identification.
• Utilized the sliding root mean square (SRMS) of torque as a criterion for threshold detection.

Main Results:

• The proposed algorithm accurately identifies the load torque signal for pinch state detection.
• Sliding root mean square (SRMS) effectively serves as a reliable threshold detection criterion.
• Experimental validation confirmed the algorithm's effectiveness across different load conditions.

Conclusions:

• The combined model reference Kalman prediction and SRMS algorithm significantly improves pinch detection accuracy.
• This approach enhances the safety and reliability of electric height-adjustable desks.
• The algorithm is effective for detecting pinch states under diverse operational loads.