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Dynamic Parameter Identification for a Manipulator with Joint Torque Sensors Based on an Improved Experimental
Jidong Jia1,2, Minglu Zhang3, Xizhe Zang4
1School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China. jiajidong.0724@163.com.
Sensors (Basel, Switzerland)
|May 18, 2019
Summary
This study introduces a new method for robot dynamics parameter identification, improving accuracy by optimizing excitation signals and reducing noise. The approach enhances robustness in complex robotic systems.
Area of Science:
- Robotics
- Control Systems
- Signal Processing
Background:
- Robot dynamics are fundamental for model control but are complicated by multi-input-multi-output systems.
- System noise significantly impacts parameter identification accuracy, necessitating effective signal processing techniques.
Purpose of the Study:
- To develop and validate a robust method for identifying robot dynamic parameters.
- To improve the accuracy and reliability of parameter identification in the presence of system noise.
Main Methods:
- Utilized a multi-criteria embedded optimization design method to determine optimal excitation signals.
- Employed maximum likelihood estimation for parameter identification.
- Conducted experiments on a two degrees-of-freedom manipulator with joint torque sensors.
Main Results:
- The proposed method effectively resolved conflicts between single optimization criteria, enhancing identification robustness.
- Achieved lower mean relative standard deviations (0.04 and 0.3) compared to F1 and F3 criteria, indicating effective noise alleviation.
- Validation experimental curves closely matched the estimation model, with an average root mean square (RMS) error of 0.038, confirming high accuracy.
Conclusions:
- The developed method offers a robust solution for robot dynamic parameter identification.
- The approach significantly improves identification accuracy and noise reduction in complex robotic systems.
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