Robot Joint Friction Compensation for Adaptive Arm Control
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Solution Overview
Problem
Existing robot arm control systems struggle to accurately compensate for varying friction in robot joints, particularly due to temperature changes and wear, which affects the precision and accuracy of robot movements.
Innovation Solution
A method of controlling a robot arm where the joint motors are controlled based on signals generated from the friction torque and transmission torque of the robot joint, determined using angular positions and motor torque data, allowing for adaptive friction compensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If friction compensation is implemented using traditional methods, then control accuracy is improved, but the system cannot adapt to changing friction conditions caused by temperature and wear
Solution Approach 1:
The patent implements dynamic friction compensation by continuously estimating friction torque during robot operation and updating compensation values in real-time. The friction model adapts to changing conditions such as temperature variations and wear by recalculating friction parameters based on current operating states, transforming the static compensation approach into a dynamic one that maintains accuracy throughout the robot's operational lifecycle.
Solution Approach 2:
The system employs feedback mechanisms by monitoring actual robot motion and comparing it with predicted motion based on the friction model. Discrepancies between actual and predicted behavior are used to update friction estimates and refine compensation parameters, creating a closed-loop system that continuously improves accuracy in response to changing friction conditions.
2Measurement precision
If adaptive friction compensation is implemented, then precision under varying conditions is improved, but computational complexity and control system demands increase
Solution Approach 1:
The patent manages computational complexity by focusing on changing friction parameters (such as viscosity coefficients and Coulomb friction values) rather than recalculating the entire dynamic model. By identifying and adapting only the critical friction parameters that change with temperature and wear, the system achieves adaptive compensation with reduced computational burden compared to full model re-identification.
Solution Approach 2:
The system implements partial friction compensation by focusing on the most significant friction sources and parameters rather than attempting to compensate for all friction effects equally. This selective approach targets the dominant friction mechanisms that most impact precision, achieving effective adaptation without the full computational overhead of a complete friction model.
Data Source
AI summary
A method of controlling a robot arm with robot joints, where the joint motors of the joints are controlled based on a signal generated based on the friction torque (formula I) of at least one of the input/outside of the robot joint transmission and the robot joint transmission torque (formula II) between the input side and the output side of the transmission. The friction torque is determined based on: at least two of the angular position of the motor axle; the angular position of the output axle and/or the motor torque provided to the motor axle by the joint motor. The robot joint transmission torque is determined based on: at least one of the angular position of the output axle; the angular position of the output axle and/or the angular position of the motor axle; the angular position of the motor axle and the motor torque provided to the motor axle by the joint motor.