压电陶瓷驱动器迟滞非线性建模及逆补偿控制
TP29%TH703; 自适应光学系统中的倾斜镜、变形镜通常是应用压电陶瓷驱动器来进行精密位移控制,但压电陶瓷驱动器都有较大的非线性迟滞效应,对系统定位性能造成了一定的影响.为了补偿迟滞现象,需要对迟滞效应进行建模.本文通过引入迟滞算子,使用贝叶斯正则化训练算法训练BP神经网络来构建压电陶瓷驱动器迟滞模型,以中国科学院光电技术研究所自主研制的压电陶瓷驱动器为对象开展了实验研究.实验结果表明,通过BP神经网络构建的压电陶瓷驱动器迟滞模型具有较准确的辨识能力,其中正模型的相对误差为0.0127,逆模型的相对误差为0.014.利用所建立的模型,压电陶瓷驱动器的非线性度从14.6％降低到了1.43％....
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| Published in | Guang Dian Gong Cheng = Opto-Electronic Engineering Vol. 46; no. 8; pp. 180328 - 30 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | Chinese English |
| Published |
Chengdu
Editorial Office of Opto-Electronic Advances
01.08.2019
中国科学院自适应光学重点实验室,四川成都 610209 中国科学院光电技术研究所,四川成都 610209 中国科学院大学,北京 100049%中国科学院自适应光学重点实验室,四川成都 610209 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1003-501X |
| DOI | 10.12086/oee.2019.180328 |
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| Abstract | TP29%TH703; 自适应光学系统中的倾斜镜、变形镜通常是应用压电陶瓷驱动器来进行精密位移控制,但压电陶瓷驱动器都有较大的非线性迟滞效应,对系统定位性能造成了一定的影响.为了补偿迟滞现象,需要对迟滞效应进行建模.本文通过引入迟滞算子,使用贝叶斯正则化训练算法训练BP神经网络来构建压电陶瓷驱动器迟滞模型,以中国科学院光电技术研究所自主研制的压电陶瓷驱动器为对象开展了实验研究.实验结果表明,通过BP神经网络构建的压电陶瓷驱动器迟滞模型具有较准确的辨识能力,其中正模型的相对误差为0.0127,逆模型的相对误差为0.014.利用所建立的模型,压电陶瓷驱动器的非线性度从14.6％降低到了1.43％. |
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| AbstractList | TP29%TH703; 自适应光学系统中的倾斜镜、变形镜通常是应用压电陶瓷驱动器来进行精密位移控制,但压电陶瓷驱动器都有较大的非线性迟滞效应,对系统定位性能造成了一定的影响.为了补偿迟滞现象,需要对迟滞效应进行建模.本文通过引入迟滞算子,使用贝叶斯正则化训练算法训练BP神经网络来构建压电陶瓷驱动器迟滞模型,以中国科学院光电技术研究所自主研制的压电陶瓷驱动器为对象开展了实验研究.实验结果表明,通过BP神经网络构建的压电陶瓷驱动器迟滞模型具有较准确的辨识能力,其中正模型的相对误差为0.0127,逆模型的相对误差为0.014.利用所建立的模型,压电陶瓷驱动器的非线性度从14.6％降低到了1.43％. |
| Author | 李新阳 刘鑫 Li, Xinyang Du, Rui 杜睿 / Liu Xin |
| AuthorAffiliation | 中国科学院自适应光学重点实验室,四川成都 610209;中国科学院光电技术研究所,四川成都 610209;中国科学院大学,北京 100049%中国科学院自适应光学重点实验室,四川成都 610209;中国科学院光电技术研究所,四川成都 610209 |
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| Author_FL | Du Rui Liu Xin Li Xinyang |
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| SubjectTerms | Adaptive optics Adaptive systems Algorithms Ceramics Computer simulation Deformable mirrors Engineering Formability Hysteresis models Identification Laboratories Neural networks Nonlinearity Optics Piezoelectric actuators Piezoelectricity Regularization |
| Title | 压电陶瓷驱动器迟滞非线性建模及逆补偿控制 |
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