Data-driven Reference Trajectory Optimization for Precision Motion Systems
We propose a data-driven optimization-based pre-compensation method to improve the contour tracking performance of precision motion stages by modifying the reference trajectory and without modifying any built-in low-level controllers. The position of the precision motion stage is predicted with data...
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| creator | Balula, Samuel Liao-McPherson, Dominic Rupenyan, Alisa Lygeros, John |
| description | We propose a data-driven optimization-based pre-compensation method to
improve the contour tracking performance of precision motion stages by
modifying the reference trajectory and without modifying any built-in low-level
controllers. The position of the precision motion stage is predicted with
data-driven models, a linear low-fidelity model is used to optimize traversal
time, by changing the path velocity and acceleration profiles then a non-linear
high-fidelity model is used to refine the previously found time-optimal
solution. We experimentally demonstrate that the proposed method is capable of
simultaneously improving the productivity and accuracy of a high precision
motion stage. Given the data-based nature of the models, the proposed method
can easily be adapted to a wide family of precision motion systems. |
| doi_str_mv | 10.48550/arxiv.2205.15694 |
| format | Article |
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improve the contour tracking performance of precision motion stages by
modifying the reference trajectory and without modifying any built-in low-level
controllers. The position of the precision motion stage is predicted with
data-driven models, a linear low-fidelity model is used to optimize traversal
time, by changing the path velocity and acceleration profiles then a non-linear
high-fidelity model is used to refine the previously found time-optimal
solution. We experimentally demonstrate that the proposed method is capable of
simultaneously improving the productivity and accuracy of a high precision
motion stage. Given the data-based nature of the models, the proposed method
can easily be adapted to a wide family of precision motion systems.</description><identifier>DOI: 10.48550/arxiv.2205.15694</identifier><language>eng</language><subject>Computer Science - Robotics ; Computer Science - Systems and Control</subject><creationdate>2022-05</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2205.15694$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2205.15694$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Balula, Samuel</creatorcontrib><creatorcontrib>Liao-McPherson, Dominic</creatorcontrib><creatorcontrib>Rupenyan, Alisa</creatorcontrib><creatorcontrib>Lygeros, John</creatorcontrib><title>Data-driven Reference Trajectory Optimization for Precision Motion Systems</title><description>We propose a data-driven optimization-based pre-compensation method to
improve the contour tracking performance of precision motion stages by
modifying the reference trajectory and without modifying any built-in low-level
controllers. The position of the precision motion stage is predicted with
data-driven models, a linear low-fidelity model is used to optimize traversal
time, by changing the path velocity and acceleration profiles then a non-linear
high-fidelity model is used to refine the previously found time-optimal
solution. We experimentally demonstrate that the proposed method is capable of
simultaneously improving the productivity and accuracy of a high precision
motion stage. Given the data-based nature of the models, the proposed method
can easily be adapted to a wide family of precision motion systems.</description><subject>Computer Science - Robotics</subject><subject>Computer Science - Systems and Control</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj8tqwzAURLXJoqT9gK6qH7B7JVmOsyzpm4SExntzJV2BQmwHWYS6X9_GzWIY5iwGDmP3AvKi0hoeMX6Hcy4l6FzoclncsM9nTJi5GM7U8S_yFKmzxOuIB7KpjyPfnlJoww-m0Hfc95HvItkwXNamn-B-HBK1wy2beTwOdHftOatfX-rVe7bevn2sntYZlosiK8B4bY2CSgopnZGCyNhKGC_IWwDhvFGVBpDaIBpnHeqlX_xFKaNKp-bs4f92kmlOMbQYx-Yi1UxS6hfm5Uj6</recordid><startdate>20220531</startdate><enddate>20220531</enddate><creator>Balula, Samuel</creator><creator>Liao-McPherson, Dominic</creator><creator>Rupenyan, Alisa</creator><creator>Lygeros, John</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20220531</creationdate><title>Data-driven Reference Trajectory Optimization for Precision Motion Systems</title><author>Balula, Samuel ; Liao-McPherson, Dominic ; Rupenyan, Alisa ; Lygeros, John</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a674-40bf5cb3082122db21eebc81bf1efc001dfb3850025baabdcda59f759f33b36d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Robotics</topic><topic>Computer Science - Systems and Control</topic><toplevel>online_resources</toplevel><creatorcontrib>Balula, Samuel</creatorcontrib><creatorcontrib>Liao-McPherson, Dominic</creatorcontrib><creatorcontrib>Rupenyan, Alisa</creatorcontrib><creatorcontrib>Lygeros, John</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Balula, Samuel</au><au>Liao-McPherson, Dominic</au><au>Rupenyan, Alisa</au><au>Lygeros, John</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Data-driven Reference Trajectory Optimization for Precision Motion Systems</atitle><date>2022-05-31</date><risdate>2022</risdate><abstract>We propose a data-driven optimization-based pre-compensation method to
improve the contour tracking performance of precision motion stages by
modifying the reference trajectory and without modifying any built-in low-level
controllers. The position of the precision motion stage is predicted with
data-driven models, a linear low-fidelity model is used to optimize traversal
time, by changing the path velocity and acceleration profiles then a non-linear
high-fidelity model is used to refine the previously found time-optimal
solution. We experimentally demonstrate that the proposed method is capable of
simultaneously improving the productivity and accuracy of a high precision
motion stage. Given the data-based nature of the models, the proposed method
can easily be adapted to a wide family of precision motion systems.</abstract><doi>10.48550/arxiv.2205.15694</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Robotics Computer Science - Systems and Control |
| title | Data-driven Reference Trajectory Optimization for Precision Motion Systems |
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