A Method for Parameter Identification of Mechanical System Based on Differential Motion Equations

Abstract:

Correct identification of system parameters contributes to construct more precise model of the system and therefore improves the performance of controller. The current identification algorithms have some disadvantages, such as the complication of algorithms and the high requirement on hardware. In this paper, we propose a new system identification method which only utilizes the data from serve motor encoder. Based on the differential equations of motion and the theory of undetermined coefficients, mechanical parameters of the system can be solved using the least square method with the equations constructed based on kinematic parameters obtained by experimental tests. The identified mechanical parameters are then used to construct the system transfer function and a SIMULINK model. The method is validated by comparing the results between simulation and experiment. This method has advantages of low requirement on hardware and simple algorithm. It is proper to be applied in mechanical parameter identification of servo system.

• Periodical: Applied Mechanics and Materials (Volumes 278-280)
• Pages: 1664-1669
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.278-280.1664
• Online since: January 2013
• Authors: Jian Wen Zhao, Sheng Rong Fan
• Keywords: Motion Differential Equations, Servo System, System Identification

References

[1] LIU Zhengshi CHEN Enwei, GAN Fangjian. Review of some methods for identifying inertial parameters of robots and their development. JOURNAL OF HEFEI UNIVERSITY OF TECHNOLOGY. 2005, 28(9): 998-1007.

[2] Hu P S. Determination of rigid body characteristics from time domain modal analysis [J]. J Sound and Vibration, 1994, 177(1): 31-41.

DOI: 10.1006/jsvi.1994.1414

[3] LI Yangmin，LIU Youwu，ZHANG Dajun，DENG Zhiyun. Identification of Dynamic Parameters on Robot. Modular Machine Tool & Automatic Manufacturing Technique. 1994, 5: 14-17.

[4] Olsen M M, Peterson H G. A new method for estimating of a dynamic robot model [J]. J Robotics and Automation, 2001, 17(1): 95-100.

[5] YANG Weibin, WU Guangqiang, QIN Datong, etc. Influence and Neural Network，s Parameters on System Identification Accuracy and Determination Method of Parameters. Chinese Journal of Mechanical Engineering. 2006, 42(7): 217-226.

DOI: 10.3901/jme.2006.07.217

[6] C.Y. Huang and T.C. Chen. Robust of Induction Motor with A Neural-Network Load Torque Estimator and A Neural-Network Identification. IEEE Trans. On Industrial Electronics, 1999, 46(5): 990-998.

DOI: 10.1109/41.793348

[7] S. Wade, a New Method of Rotor Resistance for Vector-Controlled Induction Machines. IEEE Trans. On Industrial Electronics, 1997, 44(2): 247-257.

DOI: 10.1109/41.564164

[8] LIU Shuxing, HE Xiaorong, LEI Shaolan, LI Zhao. Parameters Identification of Induction Motors Based on DSP. Micromotors. 2004, 44(1): 65-69.

[9] YANG Yibo, YIN Wensheng, WANG Jinsong, ZHU Yu. CAI Tian. Optimal Excitation Signal Design for Identification of a Coarse-fine Motion Platform. Journal of Mechanical Engineering. 2012, 46(9): 167-170.

DOI: 10.3901/jme.2010.09.165

[10] LI Ning, LIU Qixin. Motor to Be Automatic Control System. Mechanical Industry Press. 2005: 21-28.

[11] ZHANG Hu, LI Zhengxi, TONG Chaonan. Off-line Parameter Identification of Induction Motor Based on Recursive Least-squares Algorithm. Proceedings of the CSEE. 2011, 31(18): 79-86.