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Establishment and experimental verification of a Prandtl-Ishlinskii hysteresis model for tri-layer conducting polymer actuators
In this paper, a Prandtl-Ishlinskii hysteresis model (PI) is used to build a rate-independent hysteresis model for a class of conducting polymer actuators typified by tri-layer conjugated polymer actuators. Firstly, an off-line method is proposed to identify a discretization density function for the...
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creator | Xiangjiang Wang Alici, Gursel Chuc Huu Nguyen |
description | In this paper, a Prandtl-Ishlinskii hysteresis model (PI) is used to build a rate-independent hysteresis model for a class of conducting polymer actuators typified by tri-layer conjugated polymer actuators. Firstly, an off-line method is proposed to identify a discretization density function for the hysteresis model, and then a linear transfer function for the actuator is identified using the PI inverse model. Secondly, a neural network approach is proposed to realize an adaptive on-line identification method for the density function of the PI hysteresis model. In the back propagation (BP) algorithm for the neural network, the discretization PI operator is considered as an operational function of the neural network and the density function is considered as the power value. Finally, the simulation and experimental results are presented to demonstrate the validity of the model identification method and the actuator model. |
doi_str_mv | 10.1109/AIM.2013.6584260 |
format | conference_proceeding |
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Firstly, an off-line method is proposed to identify a discretization density function for the hysteresis model, and then a linear transfer function for the actuator is identified using the PI inverse model. Secondly, a neural network approach is proposed to realize an adaptive on-line identification method for the density function of the PI hysteresis model. In the back propagation (BP) algorithm for the neural network, the discretization PI operator is considered as an operational function of the neural network and the density function is considered as the power value. Finally, the simulation and experimental results are presented to demonstrate the validity of the model identification method and the actuator model.</description><subject>Actuators</subject><subject>Adaptation models</subject><subject>Conducting polymer actuators</subject><subject>Density functional theory</subject><subject>Hysteresis</subject><subject>inverse hysteresis model</subject><subject>Mathematical model</subject><subject>Neural networks</subject><subject>Polymers</subject><subject>Prandtl-Ishlinskii model</subject><subject>system identification</subject><issn>2159-6247</issn><issn>2159-6255</issn><isbn>1467353191</isbn><isbn>9781467353199</isbn><isbn>9781467353205</isbn><isbn>1467353205</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo9kEtPwzAQhM1LopTekbj4D6T4tbFzrKoWKhXBAc6V49jU4CaV7SJy4q8TRMVpduZbrbSD0A0lU0pJdTdbPU4ZoXxaghKsJCdoUklFRSk5cEbgFI0YhaooGcAZujoCWtHzfyDkJZqk9E4IoUQBY3yEvhcp6zr4tN3ZNmPdNth-7W30v1YH_DmMzhudfdfizmGNn-OwlEOxStvg2_ThPd72Kdtok0941zU2YNdFnKMvgu5txKZrm4PJvn3D-y70uyHSJh907mK6RhdOh2QnRx2j1-XiZf5QrJ_uV_PZuvBUQi4o0JI5RoytpQJCLCulYsQ5MKJxSjcViMrURBrmAMAoB7WxwghVa26t5WN0-3fXD26zH_7Tsd8cu-Q_Np5nUA</recordid><startdate>201307</startdate><enddate>201307</enddate><creator>Xiangjiang Wang</creator><creator>Alici, Gursel</creator><creator>Chuc Huu Nguyen</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201307</creationdate><title>Establishment and experimental verification of a Prandtl-Ishlinskii hysteresis model for tri-layer conducting polymer actuators</title><author>Xiangjiang Wang ; Alici, Gursel ; Chuc Huu Nguyen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-15162f20ceb78500e267820ff5c4df8ad9549cb07c2f555c8f5bce4c48ba3eee3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Actuators</topic><topic>Adaptation models</topic><topic>Conducting polymer actuators</topic><topic>Density functional theory</topic><topic>Hysteresis</topic><topic>inverse hysteresis model</topic><topic>Mathematical model</topic><topic>Neural networks</topic><topic>Polymers</topic><topic>Prandtl-Ishlinskii model</topic><topic>system identification</topic><toplevel>online_resources</toplevel><creatorcontrib>Xiangjiang Wang</creatorcontrib><creatorcontrib>Alici, Gursel</creatorcontrib><creatorcontrib>Chuc Huu Nguyen</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xiangjiang Wang</au><au>Alici, Gursel</au><au>Chuc Huu Nguyen</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Establishment and experimental verification of a Prandtl-Ishlinskii hysteresis model for tri-layer conducting polymer actuators</atitle><btitle>2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics</btitle><stitle>AIM</stitle><date>2013-07</date><risdate>2013</risdate><spage>1217</spage><epage>1221</epage><pages>1217-1221</pages><issn>2159-6247</issn><eissn>2159-6255</eissn><isbn>1467353191</isbn><isbn>9781467353199</isbn><eisbn>9781467353205</eisbn><eisbn>1467353205</eisbn><abstract>In this paper, a Prandtl-Ishlinskii hysteresis model (PI) is used to build a rate-independent hysteresis model for a class of conducting polymer actuators typified by tri-layer conjugated polymer actuators. Firstly, an off-line method is proposed to identify a discretization density function for the hysteresis model, and then a linear transfer function for the actuator is identified using the PI inverse model. Secondly, a neural network approach is proposed to realize an adaptive on-line identification method for the density function of the PI hysteresis model. In the back propagation (BP) algorithm for the neural network, the discretization PI operator is considered as an operational function of the neural network and the density function is considered as the power value. Finally, the simulation and experimental results are presented to demonstrate the validity of the model identification method and the actuator model.</abstract><pub>IEEE</pub><doi>10.1109/AIM.2013.6584260</doi><tpages>5</tpages></addata></record> |
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subjects | Actuators Adaptation models Conducting polymer actuators Density functional theory Hysteresis inverse hysteresis model Mathematical model Neural networks Polymers Prandtl-Ishlinskii model system identification |
title | Establishment and experimental verification of a Prandtl-Ishlinskii hysteresis model for tri-layer conducting polymer actuators |
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