Dynamic Parameter Identification for a Manipulator with Joint Torque Sensors Based on an Improved Experimental Design

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 10; p. 2248
• Main Authors: Jia, Jidong, Zhang, Minglu, Zang, Xizhe, Zhang, He, Zhao, Jie
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 15.05.2019; MDPI
• Subjects: Accuracy; Design; dynamic parameter identification; Economic models; excitation optimization; experiment design; Genetic algorithms; Kalman filters; maximum likelihood estimation; Methods; motion control; Neural networks; Noise; Optimization; Parameter identification; Researchers; Robotics; Robots; signal processing; United Kingdom > UK; United States > US; China; dynamic parameter identification; motion control; experiment design; signal processing; robotics; excitation optimization; maximum likelihood estimation
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19102248

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 F1 and F3, 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.