Dynamic Parameter Identification for a Manipulator with Joint Torque Sensors Based on an Improved Experimental Design
As the foundation of model control, robot dynamics is crucial. However, a robot is a complex multi-input–multi-output system. System noise seriously affects parameter identification results, thereby inevitably requiring us to conduct signal processing to extract useful signals from chaotic...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/10/2248 |
| _version_ | 1828740834273525760 |
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| author | Jidong Jia Minglu Zhang Xizhe Zang He Zhang Jie Zhao |
| author_facet | Jidong Jia Minglu Zhang Xizhe Zang He Zhang Jie Zhao |
| author_sort | Jidong Jia |
| collection | DOAJ |
| description | As the foundation of model control, robot dynamics is crucial. However, a robot is a complex multi-input–multi-output system. System noise seriously affects parameter identification results, thereby inevitably requiring us to conduct signal processing to extract useful signals from chaotic noise. In this research, the dynamic parameters were identified on the basis of the proposed multi-criteria embedded optimization design method, to obtain the optimal excitation signal and then use maximum likelihood estimation for parameter identification. Considering the movement coupling characteristics of the multi-axis, experiments were based on a two degrees-of-freedom manipulator with joint torque sensors. Simulation and experimental results showed that the proposed method can reasonably resolve the problem of mutual opposition within a single criterion and improve the identification robustness in comparison with other optimization criteria. The mean relative standard deviation was 0.04 and 0.3 lower in the identified parameters than in <i>F</i><sub>1</sub> and <i>F</i><sub>3</sub>, respectively, thus signifying that noise is effectively alleviated. In addition, validation experimental curves were close to the estimation model, and the average of root mean square (RMS) is 0.038, thereby confirming the accuracy of the proposed method. |
| first_indexed | 2024-04-13T00:48:25Z |
| format | Article |
| id | doaj.art-4063dbc7313941febb4333b05047c46f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T00:48:25Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4063dbc7313941febb4333b05047c46f2022-12-22T03:09:56ZengMDPI AGSensors1424-82202019-05-011910224810.3390/s19102248s19102248Dynamic Parameter Identification for a Manipulator with Joint Torque Sensors Based on an Improved Experimental DesignJidong Jia0Minglu Zhang1Xizhe Zang2He Zhang3Jie Zhao4School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, ChinaState Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150006, ChinaState Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150006, ChinaState Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150006, ChinaAs the foundation of model control, robot dynamics is crucial. However, a robot is a complex multi-input–multi-output system. System noise seriously affects parameter identification results, thereby inevitably requiring us to conduct signal processing to extract useful signals from chaotic noise. In this research, the dynamic parameters were identified on the basis of the proposed multi-criteria embedded optimization design method, to obtain the optimal excitation signal and then use maximum likelihood estimation for parameter identification. Considering the movement coupling characteristics of the multi-axis, experiments were based on a two degrees-of-freedom manipulator with joint torque sensors. Simulation and experimental results showed that the proposed method can reasonably resolve the problem of mutual opposition within a single criterion and improve the identification robustness in comparison with other optimization criteria. The mean relative standard deviation was 0.04 and 0.3 lower in the identified parameters than in <i>F</i><sub>1</sub> and <i>F</i><sub>3</sub>, respectively, thus signifying that noise is effectively alleviated. In addition, validation experimental curves were close to the estimation model, and the average of root mean square (RMS) is 0.038, thereby confirming the accuracy of the proposed method.https://www.mdpi.com/1424-8220/19/10/2248dynamic parameter identificationexcitation optimizationmaximum likelihood estimationroboticsmotion controlexperiment designsignal processing |
| spellingShingle | Jidong Jia Minglu Zhang Xizhe Zang He Zhang Jie Zhao Dynamic Parameter Identification for a Manipulator with Joint Torque Sensors Based on an Improved Experimental Design Sensors dynamic parameter identification excitation optimization maximum likelihood estimation robotics motion control experiment design signal processing |
| title | Dynamic Parameter Identification for a Manipulator with Joint Torque Sensors Based on an Improved Experimental Design |
| title_full | Dynamic Parameter Identification for a Manipulator with Joint Torque Sensors Based on an Improved Experimental Design |
| title_fullStr | Dynamic Parameter Identification for a Manipulator with Joint Torque Sensors Based on an Improved Experimental Design |
| title_full_unstemmed | Dynamic Parameter Identification for a Manipulator with Joint Torque Sensors Based on an Improved Experimental Design |
| title_short | Dynamic Parameter Identification for a Manipulator with Joint Torque Sensors Based on an Improved Experimental Design |
| title_sort | dynamic parameter identification for a manipulator with joint torque sensors based on an improved experimental design |
| topic | dynamic parameter identification excitation optimization maximum likelihood estimation robotics motion control experiment design signal processing |
| url | https://www.mdpi.com/1424-8220/19/10/2248 |
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