A New Closed-Loop Input Error Approach for Industrial Robot Manipulator Identification Based on Evolutionary Algorithms

By exploiting the concept of closed-loop input error (CLIE), this article proposes an identification approach for the dynamic parameters of industrial robot manipulator based on evolutionary algorithms (EAs). The proposed algorithm estimates the dynamic parameters by using joint torque residuals bet...

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Bibliographic Details
Published in:IEEE transactions on control systems technology Vol. 32; no. 4; pp. 1196 - 1211
Main Authors: Huang, Hao-Lun, Cheng, Ming-Yang
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Automation
Closed loop systems
Closed loops
Closed-loop system identification
estimation error
Evolutionary algorithms
evolutionary algorithms (EAs)
Filtration
Friction
Genetic algorithms
Heuristic algorithms
industrial robot manipulator
Industrial robots
Manipulator dynamics
Manipulators
Mathematical models
Measurement uncertainty
Parameter identification
Robot arms
Robots
Solution space
System identification
Torque
Trajectory control
ISSN:1063-6536, 1558-0865
Online Access:Get full text
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Summary:By exploiting the concept of closed-loop input error (CLIE), this article proposes an identification approach for the dynamic parameters of industrial robot manipulator based on evolutionary algorithms (EAs). The proposed algorithm estimates the dynamic parameters by using joint torque residuals between the actual robot and a parallel estimated model. Both the actual robot and the estimated model use the same reference trajectories and the same control law structure tuned with the same gain. The state of the estimated model is generated by filtering the measured signal through a model state generator, so by adjusting the cutoff frequency of the filter, different estimated models can be generated. By using EA, one can search for an estimated model in the solution space corresponding to the model state generator so that the joint torque difference between the actual robot and the estimated model is minimized. In addition, EA will automatically adjust the filtering strength of the measured signals to provide a noise-free signal for the estimated model so that the identification result can be more accurate. The dynamic parameters of the estimated model obtained through the optimization process are the optimal identification results of the actual robot. Moreover, to reduce the computation cost of the estimated model, this article also provides an approximation algorithm for the observation matrix so as to enhance the search efficiency of EA. Several experiments have been conducted on a 6-DOF industrial robot manipulator to verify the effectiveness of the proposed approach.
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ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2024.3356391