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Optimized Admittance Control for Manipulators Interacting with Unknown Environment
This paper considers the study scenario that the end-effector of a manipulator follows a desired trajectory and interacts with external environment. To maximize the interaction performance, admittance control is combined with adaptive dynamic programming (ADP). The optimal admittance parameters can...
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| creator | Kong, Haiyi Peng, Guangzhu Li, Guang Yang, Chenguang |
| description | This paper considers the study scenario that the end-effector of a manipulator follows a desired trajectory and interacts with external environment. To maximize the interaction performance, admittance control is combined with adaptive dynamic programming (ADP). The optimal admittance parameters can be learned online without prior knowledge of the environment. A data-driven Hybrid Iteration is employed in the ADP, which can relax the initial stabilizing requirement and at the same time has a faster convergence rate compared with Value Iteration. In addition, a more accurate environment model is considered in the system control design, where a general iterative expression is proposed to describe the varying contour of the environment. At last, simulation and experimental studies are given to verify the effectiveness of the proposed method. |
| doi_str_mv | 10.1109/ICIT58233.2024.10540834 |
| format | conference_proceeding |
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At last, simulation and experimental studies are given to verify the effectiveness of the proposed method.</description><subject>Adaptation models</subject><subject>Adaptive admittance control</subject><subject>Adaptive dynamic programming</subject><subject>Control design</subject><subject>Dynamic programming</subject><subject>End effectors</subject><subject>Environment position</subject><subject>Heuristic algorithms</subject><subject>Iterative methods</subject><subject>Optimized admittance adaptation</subject><subject>Trajectory</subject><subject>Unknown environment</subject><issn>2643-2978</issn><isbn>9798350340266</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2024</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1kNFKwzAYRqMgOGbfQDAv0Jrk_5s2l6NMLUwGUq9H2iYabdOSRoc-vQP16jtw4Fx8hNxwlnHO1G1d1U1eCoBMMIEZZzmyEvCMJKpQJeQMkAkpz8lKSIRUqKK8JMmyvDHGQJwsyhV52s_Rje7b9HTTjy5G7TtDq8nHMA3UToE-au_mj0HHKSy09tEE3UXnX-jRxVf67N_9dPR06z9dmPxofLwiF1YPi0n-dk2au21TPaS7_X1dbXapQyVS3steYqtL3oOFrrPQKmlZkRuBHNrWWotG5TIvEGzRqVJZrZREI8UJGcKaXP9mnTHmMAc36vB1-H8BfgDre1LK</recordid><startdate>20240325</startdate><enddate>20240325</enddate><creator>Kong, Haiyi</creator><creator>Peng, Guangzhu</creator><creator>Li, Guang</creator><creator>Yang, Chenguang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20240325</creationdate><title>Optimized Admittance Control for Manipulators Interacting with Unknown Environment</title><author>Kong, Haiyi ; Peng, Guangzhu ; Li, Guang ; Yang, Chenguang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i492-1d6d64ba81d3f3ccf3b96f075e2413bbfff4e9565743f7c989fa9964e6289f043</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Adaptation models</topic><topic>Adaptive admittance control</topic><topic>Adaptive dynamic programming</topic><topic>Control design</topic><topic>Dynamic programming</topic><topic>End effectors</topic><topic>Environment position</topic><topic>Heuristic algorithms</topic><topic>Iterative methods</topic><topic>Optimized admittance adaptation</topic><topic>Trajectory</topic><topic>Unknown environment</topic><toplevel>online_resources</toplevel><creatorcontrib>Kong, Haiyi</creatorcontrib><creatorcontrib>Peng, Guangzhu</creatorcontrib><creatorcontrib>Li, Guang</creatorcontrib><creatorcontrib>Yang, Chenguang</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 Online</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>Kong, Haiyi</au><au>Peng, Guangzhu</au><au>Li, Guang</au><au>Yang, Chenguang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Optimized Admittance Control for Manipulators Interacting with Unknown Environment</atitle><btitle>IEEE International Conference on Industrial Technology (Online)</btitle><stitle>ICIT</stitle><date>2024-03-25</date><risdate>2024</risdate><spage>1</spage><epage>6</epage><pages>1-6</pages><eissn>2643-2978</eissn><eisbn>9798350340266</eisbn><abstract>This paper considers the study scenario that the end-effector of a manipulator follows a desired trajectory and interacts with external environment. To maximize the interaction performance, admittance control is combined with adaptive dynamic programming (ADP). The optimal admittance parameters can be learned online without prior knowledge of the environment. A data-driven Hybrid Iteration is employed in the ADP, which can relax the initial stabilizing requirement and at the same time has a faster convergence rate compared with Value Iteration. In addition, a more accurate environment model is considered in the system control design, where a general iterative expression is proposed to describe the varying contour of the environment. At last, simulation and experimental studies are given to verify the effectiveness of the proposed method.</abstract><pub>IEEE</pub><doi>10.1109/ICIT58233.2024.10540834</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | EISSN: 2643-2978 |
| ispartof | IEEE International Conference on Industrial Technology (Online), 2024, p.1-6 |
| issn | 2643-2978 |
| language | eng |
| recordid | cdi_ieee_primary_10540834 |
| source | IEEE Xplore All Conference Series |
| subjects | Adaptation models Adaptive admittance control Adaptive dynamic programming Control design Dynamic programming End effectors Environment position Heuristic algorithms Iterative methods Optimized admittance adaptation Trajectory Unknown environment |
| title | Optimized Admittance Control for Manipulators Interacting with Unknown Environment |
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