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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Software Sensor to Enhance Online Parametric Identification for Nonlinear Closed-Loop Systems for Robotic
Lilia Sidhom1,2, Ines Chihi1,2,3, Ernest Nlandu Kamavuako4
1Laboratory of Energy Applications and Renewable Energy Efficiency (LAPER), El Manar University, Tunis 1068, Tunisia.
Sensors (Basel, Switzerland)
|June 2, 2021
Summary
This study introduces a new dynamic sliding mode technique for robotic systems, improving online parameter identification. The method enhances torque prediction accuracy in robotic manipulators.
Area of Science:
- Robotics
- Control Systems Engineering
- System Identification
Background:
- Accurate dynamic parameter identification is crucial for robotic control.
- Existing methods often struggle with real-time performance and noise sensitivity.
- Online identification in closed-loop systems presents unique challenges.
Purpose of the Study:
- To develop an online direct closed-loop identification method for robotic applications.
- To improve the accuracy and reliability of dynamic parameter estimation.
- To enhance the prediction of system input variables, such as torque.
Main Methods:
- A novel dynamic sliding mode technique is proposed for direct closed-loop identification.
- A robust differentiator based on higher-order sliding modes with dynamic gain is utilized.
- Recursive least squares algorithm is employed for dynamic parameter estimation using sampled trajectory data.
Main Results:
- The proposed method achieves accurate online estimation of dynamic parameters.
- Experimental validation on a 2-DOF robot manipulator demonstrates effectiveness and reliability.
- The new differentiator design significantly improves online parametric identification and torque prediction quality.
Conclusions:
- The dynamic sliding mode technique offers a robust solution for online closed-loop system identification in robotics.
- The choice of differentiator design critically impacts identification accuracy and predictive capabilities.
- This approach provides a reliable method for enhancing robotic system modeling and control.
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