Feed System Friction Compensation Using Position-Based Motor Prediction
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Solution Overview
Problem
Feed systems face discontinuous speed issues due to insufficient motor power to overcome static friction during reversals, leading to manufacturing errors and reduced quality.
Innovation Solution
A method that predicts motor friction by analyzing current and angle-position signals, creating a friction model, and providing compensation current to the motor to ensure sufficient power at each rotation position, thereby preventing commutation sharp angles and improving manufacturing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the motor operates without friction compensation, then the device complexity is low, but the manufacturing precision deteriorates due to commutation sharp angles
Solution Approach 1:
The friction model is established in advance through offline learning by collecting current signals and angle-position signals during motor rotation, estimating friction at each position, and creating a friction model that stores compensation values. During actual operation, the system only needs to query and apply pre-calculated compensation currents, avoiding real-time calculation complexity while maintaining high manufacturing precision
Solution Approach 2:
The system uses its own operational data (current signals and angle-position signals) to automatically build and update its friction model without external intervention. The motor driver continuously learns from the motor's actual performance and self-adjusts the friction compensation, eliminating the need for external calibration equipment or manual adjustment
2Manufacturing precision
If the motor power is increased to overcome static friction, then the manufacturing precision improves, but the energy consumption increases
Solution Approach 1:
Instead of uniformly increasing motor power across all operating conditions, the system applies localized friction compensation by adding compensation current specifically at positions where friction is detected to be high. The friction model provides position-specific compensation values, ensuring the motor receives exactly the additional power needed at each angle position without wasting energy during low-friction phases
Solution Approach 2:
The system dynamically adjusts the motor's current parameter based on the predicted friction at each position. By changing the current parameter according to the friction model's prediction, the system optimizes energy consumption by applying additional current only when and where friction requires it, rather than maintaining high power continuously
3Reliability
If real-time friction calculation is performed during motor operation, then the reliability improves, but the loss of time increases due to computation overhead
Solution Approach 1:
The friction model is built in advance during an offline learning phase where the system collects operational data and performs comprehensive friction analysis. This pre-calculated model stores friction characteristics for various positions, allowing the system to quickly query and apply compensation values during actual operation without performing heavy real-time calculations, thus maintaining high reliability while minimizing computation time
Solution Approach 2:
The system performs friction estimation at discrete angle-position intervals rather than continuously at every instant. By sampling friction characteristics at key positions and interpolating between them using the pre-built model, the system achieves sufficient prediction accuracy without the computational overhead of continuous real-time calculation
Data Source
AI summary
A method for predicting and compensating frictions of a feed system includes following steps: constantly obtaining current signals and angle-position signals of a motor by a motor driver of a feed system after being activated; calculating frictions of the motor upon each rotating position according to the obtained current signals and angle-position signals and generating multiple records of friction data; creating a friction model according to the multiple records of friction data and the angle-position signals each respectively corresponding to each record of friction data with respect to each rotating position; importing current angle-position signal of the motor to the friction model for predicting a predicted friction; calculating a compensation current based on the predicted friction; and, controlling the motor driver to additionally provide the compensation current to the motor for conquering an upcoming friction of the feed system approximate to the predicted friction.