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In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

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Flexible joint parameters identification method based on improved tracking differentiator and adaptive differential

Chuanming Song¹, Qinjun Du¹, Shuxin Yang¹

¹School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, Shandong 255049, China.

The Review of Scientific Instruments

|September 1, 2022

Summary

This summary is machine-generated.

This study introduces a novel method for identifying flexible joint parameters, improving control accuracy by combining offline and online identification techniques. The approach enhances tracking speed and accuracy, leading to more precise robotic control.

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Area of Science:

Robotics
Control Systems Engineering
Mechanical Engineering

Background:

Flexible joints exhibit parameter variations (e.g., stiffness) due to assembly errors and dynamic changes during operation.
These inconsistencies between actual and nominal joint parameters significantly degrade the accuracy of joint control systems.

Purpose of the Study:

To develop a robust parameter identification method for flexible joints that mitigates the impact of parameter errors.
To enhance the overall control accuracy of systems incorporating flexible joints.

Main Methods:

A hybrid approach combining offline identification (inertia, mass, damping) and online identification (joint stiffness) was developed.
A novel hybrid tracking differentiator utilizing an improved Sigmoid function was designed for accurate differential signal tracking, with convergence proven via Lyapunov function.
Adaptive differential evolution, featuring enhanced crossover, mutation, and Metropolis acceptance criteria, was employed for rapid parameter identification.
A feedforward control structure was implemented to compensate for model deviations identified through the proposed method.

Main Results:

The improved tracking differentiator demonstrated enhanced signal tracking speed and derivation accuracy.
The proposed parameter identification method exhibited superior convergence speed and higher accuracy compared to existing algorithms.
The feedforward compensation control effectively corrected model parameter deviations, leading to a significant improvement in overall control accuracy.

Conclusions:

The developed parameter identification and compensation strategy effectively addresses the challenges posed by parameter uncertainties in flexible joint control.
This method offers a promising solution for enhancing the precision and reliability of robotic systems and other applications involving flexible joints.