吴洪涛
Professor Supervisor of Doctorate Candidates
Alma Mater:天津大学
Education Level:天津大学
Degree:Doctoral Degree in Engineering
School/Department:College of Mechanical and Electrical Engineering
Discipline:Mechatronic Engineering
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Affiliation of Author(s):机电学院
Journal:Yi Qi Yi Biao Xue Bao
Abstract:Piezoelectric actuator has been widely adopted in the field of high-precision positioning applications. However, its inherent hysteresis nonlinearity seriously deteriorates the positioning accuracy of PEAs. In order to accurately describe the hysteresis characteristic of PEAs, a support vector machine (SVM) hysteresis model is proposed in this paper based on nonlinear autoregressive-moving-average with exogenous inputs (NARMAX). To establish the SVM hysteresis model, first of all, it is necessary to convert the input output relationship of the PEA from a multivalued mapping problem into single-valued one. In this paper, the effects of different input variable single-valued mapping methods on the accuracy and generalization ability of the SVM hysteresis model are compared, a single-valued mapping method based on NARMAX is proposed, and a globally higher accuracy SVM hysteresis model for PEAs is established. By reducing the interval of the input signal frequency contained in the training set, the accuracy of the model on the test set is improved. The cross verification method is adopted to determine the parameters of the SVM model, which improves the global accuracy and generalization ability of the hysteresis model. The results show that compared with traditional B-W model, the model proposed in this paper more accurately describes the rate-dependent hysteresis of piezoelectric actuators, the accuracy is improved by 8 times at 1 Hz, by 60 times at 50 Hz and more at higher frequency. Through displacement tracking experiment, it is proved that the feedforward plus feedback (FF+FB) control based on SVM hysteresis inverse model can effectively improve the tracking accuracy. Compared with PID feedback control, the tracking error can be reduced by 73.9% at most. © 2018, Science Press. All right reserved.
ISSN No.:0254-3087
Translation or Not:no
Date of Publication:2018-09-01
Co-author:Yan, Xiuquan,ly,Xiaolong Yang,Kang, Shengzheng
Correspondence Author:wht
吴洪涛，博士，教授，博士生导师。1982年毕业于东北重型机械学院机械制造专业，1985年获得天津大学机械制造专业硕士学位，1992年7月获天津大学机械制造学科工学博士学位，1992年8月至94年5月在哈尔滨工业大学大学机器人研究所作第一期博士后研究，1994年6月至96年6月在南京航空航天大学机电工程学院作第二期博士后研究。现在南京航空航天大学机电工程学院工作，曾任南航大机电学院副院长。2000年赴香港理工大学合作研究，2001-2002赴美国亚里桑那州立大学合作研究。中国机械工程学会高级会员，中国航空学会会员，中国宇航学会会员。中国科学，机械工程学报，机器人，MMT，J.ROBOTIC SYSTEM，哈工大学报，浙江大学学报，天津大学学报等国内外重要期刊审稿人，常州先进制造国家示范基地首批专家组成员，江苏省机器人标准化委员会委员，全国压力机械标准化委员会成员，清华大学启迪科技园创业导师。江苏大学、燕山大学、淮阴工学院兼职教授。主持和参加了包括国防科工委和航天工业总公司八五重大预研项目、国家高技术领域863智能机器人主题办项目、国家自然科学基金、航空自然科学基金、中国博士后科学基金和国家教委博士点基金等20多项研究课题。1996年获得航空工业总公司“中国有突出贡献的博士学位获得者”荣誉称号。曾获得中国航空工业总公司科技进步二等奖一项，国家高技术领域八六三智能机器人机构主题办公室一等奖一项，江苏省国防工业办公室科技进步一等奖，江苏省科技进步三等奖，四等奖各一项，国家教委科学技术进步三等奖一项，学校科技进步二等奖一项。
