A Method Toward Comprehensive Identification for Piezoelectric Dynamic System With Multimodal Hysteresis and Uncertainty Compensation
An accurate model of the piezoelectric actuator (PEA) is important for the controller design to realize high-performance closed-loop control. However, the PEA exhibits inherent fast, slow modes (SMs), hysteresis nonlinearity, and system uncertainty at the same time. In this article, a two-stage mode...
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| Veröffentlicht in: | IEEE transactions on instrumentation and measurement 2024, Vol.73, p.1-10 |
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| creator | Lin, Jianfeng Qi, Chenkun Xue, Yuxuan Wang, Yichen Liu, Xinyu Gao, Feng |
| description | An accurate model of the piezoelectric actuator (PEA) is important for the controller design to realize high-performance closed-loop control. However, the PEA exhibits inherent fast, slow modes (SMs), hysteresis nonlinearity, and system uncertainty at the same time. In this article, a two-stage modeling approach called slow fast hysteresis with uncertainty compensation (SFHUC) for the piezoelectric dynamic system is proposed to achieve a high-accuracy model between input voltage and output displacement. The fast, SMs and hysteresis nonlinearity of the PEA are estimated first based on a linear-linear-nonlinear cascade model. Then the system uncertainty of the PEA is compensated by a nonlinear artificial neural network, where the input is the voltage signal and the output is the residual error of this linear-linear-nonlinear cascade model. The corresponding comprehensive identification algorithm including fast, SMs, hysteresis nonlinearity, and system uncertainty is developed for accurate displacement prediction. Experimental results on a typical PEA demonstrate the effectiveness of the proposed comprehensive identification approach. |
| doi_str_mv | 10.1109/TIM.2024.3376010 |
| format | Article |
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However, the PEA exhibits inherent fast, slow modes (SMs), hysteresis nonlinearity, and system uncertainty at the same time. In this article, a two-stage modeling approach called slow fast hysteresis with uncertainty compensation (SFHUC) for the piezoelectric dynamic system is proposed to achieve a high-accuracy model between input voltage and output displacement. The fast, SMs and hysteresis nonlinearity of the PEA are estimated first based on a linear-linear-nonlinear cascade model. Then the system uncertainty of the PEA is compensated by a nonlinear artificial neural network, where the input is the voltage signal and the output is the residual error of this linear-linear-nonlinear cascade model. The corresponding comprehensive identification algorithm including fast, SMs, hysteresis nonlinearity, and system uncertainty is developed for accurate displacement prediction. Experimental results on a typical PEA demonstrate the effectiveness of the proposed comprehensive identification approach.</description><identifier>ISSN: 0018-9456</identifier><identifier>EISSN: 1557-9662</identifier><identifier>DOI: 10.1109/TIM.2024.3376010</identifier><identifier>CODEN: IEIMAO</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Artificial neural networks ; Autoregressive processes ; Closed loops ; Compensation ; Control systems design ; Creep ; Dynamical systems ; Electric potential ; Feedback control ; Hysteresis ; Model accuracy ; Modeling ; Nonlinearity ; parameter identification ; piezoelectric actuator (PEA) ; Piezoelectric actuators ; system uncertainty ; Uncertainty ; Vibrations ; Voltage</subject><ispartof>IEEE transactions on instrumentation and measurement, 2024, Vol.73, p.1-10</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c245t-ca4d34ca8a95937ffba6d410084931be367d819bdd5e4d1f492eba3fbd98c6f63</cites><orcidid>0009-0009-0772-3811 ; 0000-0002-7545-1338 ; 0000-0001-5358-719X ; 0000-0002-7467-5540 ; 0009-0003-5747-3810 ; 0000-0002-9501-9083</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10466632$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,4024,27923,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10466632$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Lin, Jianfeng</creatorcontrib><creatorcontrib>Qi, Chenkun</creatorcontrib><creatorcontrib>Xue, Yuxuan</creatorcontrib><creatorcontrib>Wang, Yichen</creatorcontrib><creatorcontrib>Liu, Xinyu</creatorcontrib><creatorcontrib>Gao, Feng</creatorcontrib><title>A Method Toward Comprehensive Identification for Piezoelectric Dynamic System With Multimodal Hysteresis and Uncertainty Compensation</title><title>IEEE transactions on instrumentation and measurement</title><addtitle>TIM</addtitle><description>An accurate model of the piezoelectric actuator (PEA) is important for the controller design to realize high-performance closed-loop control. However, the PEA exhibits inherent fast, slow modes (SMs), hysteresis nonlinearity, and system uncertainty at the same time. In this article, a two-stage modeling approach called slow fast hysteresis with uncertainty compensation (SFHUC) for the piezoelectric dynamic system is proposed to achieve a high-accuracy model between input voltage and output displacement. The fast, SMs and hysteresis nonlinearity of the PEA are estimated first based on a linear-linear-nonlinear cascade model. Then the system uncertainty of the PEA is compensated by a nonlinear artificial neural network, where the input is the voltage signal and the output is the residual error of this linear-linear-nonlinear cascade model. The corresponding comprehensive identification algorithm including fast, SMs, hysteresis nonlinearity, and system uncertainty is developed for accurate displacement prediction. Experimental results on a typical PEA demonstrate the effectiveness of the proposed comprehensive identification approach.</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Autoregressive processes</subject><subject>Closed loops</subject><subject>Compensation</subject><subject>Control systems design</subject><subject>Creep</subject><subject>Dynamical systems</subject><subject>Electric potential</subject><subject>Feedback control</subject><subject>Hysteresis</subject><subject>Model accuracy</subject><subject>Modeling</subject><subject>Nonlinearity</subject><subject>parameter identification</subject><subject>piezoelectric actuator (PEA)</subject><subject>Piezoelectric actuators</subject><subject>system uncertainty</subject><subject>Uncertainty</subject><subject>Vibrations</subject><subject>Voltage</subject><issn>0018-9456</issn><issn>1557-9662</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkM1LwzAYxoMoOD_uHjwEPHcmTZo2xzG_BhsKTjyWNHnDMtZmJpky7_7fdm4HTw88PB_wQ-iKkiGlRN7OJ7NhTnI-ZKwUhJIjNKBFUWZSiPwYDQihVSZ5IU7RWYxLQkgpeDlAPyM8g7TwBs_9lwoGj327DrCALrpPwBMDXXLWaZWc77D1Ab84-PawAp2C0_hu26m219dtTNDid5cWeLZZJdd6o1b4aWcHiC5i1Rn81mkISbkubf-O-pe_4Qt0YtUqwuVBz9Hbw_18_JRNnx8n49E00zkvUqYVN4xrVSlZSFZa2yhhOCWk4pLRBpgoTUVlY0wB3FDLZQ6NYrYxstLCCnaObva76-A_NhBTvfSb0PWXdS4lqySROelTZJ_SwccYwNbr4FoVtjUl9Q523cOud7DrA-y-cr2vOAD4F-dCCJazXy3cfos</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Lin, Jianfeng</creator><creator>Qi, Chenkun</creator><creator>Xue, Yuxuan</creator><creator>Wang, Yichen</creator><creator>Liu, Xinyu</creator><creator>Gao, Feng</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Algorithms Artificial neural networks Autoregressive processes Closed loops Compensation Control systems design Creep Dynamical systems Electric potential Feedback control Hysteresis Model accuracy Modeling Nonlinearity parameter identification piezoelectric actuator (PEA) Piezoelectric actuators system uncertainty Uncertainty Vibrations Voltage |
| title | A Method Toward Comprehensive Identification for Piezoelectric Dynamic System With Multimodal Hysteresis and Uncertainty Compensation |
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