[1]王洪如,刘强.利用支持向量机的摩擦模型参数辨识[J].华侨大学学报(自然科学版),2010,31(2):132-135.[doi:10.11830/ISSN.1000-5013.2010.02.0132]
 WANG Hong-ru,LIU Qiang.Research on the Parameter Identification of Friction Model Based on Support Vector Machine[J].Journal of Huaqiao University(Natural Science),2010,31(2):132-135.[doi:10.11830/ISSN.1000-5013.2010.02.0132]
点击复制

利用支持向量机的摩擦模型参数辨识()
分享到:

《华侨大学学报(自然科学版)》[ISSN:1000-5013/CN:35-1079/N]

卷:
第31卷
期数:
2010年第2期
页码:
132-135
栏目:
出版日期:
2010-03-20

文章信息/Info

Title:
Research on the Parameter Identification of Friction Model Based on Support Vector Machine
文章编号:
1000-5013(2010)02-0132-04
作者:
王洪如刘强
华侨大学机电及自动化学院
Author(s):
WANG Hong-ru LIU Qiang
College of Mechanical Engineering and Automation, Huaqiao University, Quanzhou 362021, China
关键词:
摩擦模型 参数辨识 支持向量机 伺服系统
Keywords:
friction model parameter identification support vector machine servo system
分类号:
TP18
DOI:
10.11830/ISSN.1000-5013.2010.02.0132
文献标志码:
A
摘要:
以Tustin摩擦模型为参数辨识对象,提出一种基于支持向量机算法的摩擦模型参数辨识的方法.构建训练样本并选取适当的支持向量机模型,选择具有较好泛化能力的径向基核函数和具有稀疏性特点的ε不敏感损失函数,以求解最优化问题,得到最优解.以某直流电机高精度位置伺服系统为对象,用辨识得到的参数估计值设计摩擦力矩的补偿环节,对系统进行补偿,仿真结果表明,算法的辨识精度比较高.
Abstract:
A method for the parameter identification of the friction model based on support vector machine is proposed with Tustion friction models as the object for parameter identification.The optimum solutions are obtained by solving the optimization problem where training samples are constructed,the appropriate model of support vector machine(SVM for short) is selected,and the radial kernal function with better generalization ability and ε-insensitive loss function with the sparse characteristics are selected as well.With a DC motor high-precision position servo system as the research object,the system is compensated by using the estimated value of parameters to design the compensation aspect of friction torque.The simulation results show that the algorithm has high recognition accuracy.

参考文献/References:

[1] ARMSTRONG B, DUPONT P, CANUDAS C. A survey of models, analysis tools and compensation methods for the control of machines with friction [J]. Automatica, 1994(7):1083-1138.
[2] FRIEDLAND B, PARK Y J. On adaptive friction compensation [J]. IEEE Transactions on Automatic Control, 1992, (10):1609-1612.doi:10.1109/9.256395.
[3] PHILLIPS S M, BALLOU K R. Friction modeling and compensation for an industrial robot [J]. Journal of ROBOTIC SYSTEMS, 1993(7):947-971.
[4] WIT C C, OISSON H, ASTROM K J. A new model for control of systems with friction [J]. IEEE Transactions on Automatic Control, 1995(3):419-425.doi:10.1109/9.376053.
[5] FEEMSTER M, VEDAGARBHA P, DAWSPM D M. Adaptive control techniques for friction compensation [J]. Mechatronics, 1998, (21/26):1488-1492.
[6] LEE S W, KIM J H. Friction identification using evolution strategies and robust control of positioning tables [J]. ASME Journal of Dynamic Systems, Measurement, and Control, 1999(4):619-624.doi:10.1115/1.2802525.
[7] LIAO T L, CHIEN T I. An exponentially stable adaptive friction compensator [J]. IEEE Transactions on Automatic Control, 2000(5):977-980.doi:10.1109/9.855565.
[8] YANG S, TOMIZUKA M. Adaptive pulse width control for precise positioning under influence of sticktion and coulomb friciton [J]. ASME Journal of Dynamic Systems, Measurement, and Control, 1988, (43):221-227.
[9] PARK E C, LIM H, CHOI C H. Position control of X-Y table at velocity reversal using presliding friction characteristics [J]. IEEE Transactions on Control Systems Technology, 2003(1):24-30.
[10] MOREL G, IAGMEMMA K, DUBOWSKY S. The precise control of manipulators with high joint-friction using base force/torque sensing [J]. Automatica, 2000(7):931-941.
[11] LEE H S, TOMIZUKA M. Robust motion controller design for high-accuracy positioning systems [J]. IEEE Transactions on Control Systems Technology, 1996(1):48-55.doi:10.1109/41.481407.
[12] OLSON H, ASTROM K J, DE WIT C C. Friction models and friction compensation [J]. European Journal of Control, 1998(3):176-195.
[13] 李秀英, 韩志刚. 非线性系统辨识方法的新进展 [J]. 自动化技术与应用, 2004, (10):5-7.doi:10.3969/j.issn.1003-7241.2004.10.002.
[14] WANG G L, LI Y F, BI D X. Support vector machine network for friction modeling [J]. IEEE/ASME Transactions on Mechatronics, 2003, (4/6):2833-2838.
[15] VAPNIK V N. Statistical learning theory [M]. New York:wiley, 1998.

相似文献/References:

[1]陈勇,刘雄伟,俞铁岳.立铣再生颤振闭环控制系统的设计[J].华侨大学学报(自然科学版),2006,27(3):288.[doi:10.3969/j.issn.1000-5013.2006.03.018]
 Chen Yong,Liu Xiongwei,Yu Tieyue.Design of Closed-Loop Control Systems with Regenerative Chatter in Peripheral Milling Process[J].Journal of Huaqiao University(Natural Science),2006,27(2):288.[doi:10.3969/j.issn.1000-5013.2006.03.018]
[2]陈勇,黄国钦.立铣动力学系统模态参数辨识及实验[J].华侨大学学报(自然科学版),2014,35(1):1.[doi:10.11830/ISSN.1000-5013.2014.01.0001]
 CHEN Yong,HUANG Guo-qin.Modal Parameters Identification and Experiments ofDynamic System on Vertical Milling Process[J].Journal of Huaqiao University(Natural Science),2014,35(2):1.[doi:10.11830/ISSN.1000-5013.2014.01.0001]
[3]聂卓赟,李兆洋,詹瑜坤,等.尺度变换下的直流电机参数辨识方法与实验验证[J].华侨大学学报(自然科学版),2019,40(5):674.[doi:10.11830/ISSN.1000-5013.201902041]
 NIE Zhuoyun,LI Zhaoyang,ZHAN Yukun,et al.Identification Method for DC Motor Based on Scaling Transformation and and Experimental Verification[J].Journal of Huaqiao University(Natural Science),2019,40(2):674.[doi:10.11830/ISSN.1000-5013.201902041]

备注/Memo

备注/Memo:
福建省自然科学基金资助项目(E0510023); 福建省高校新世纪优秀人才计划项目(E0510023)
更新日期/Last Update: 2014-03-23