Improving intersample performance with linearly parameterized feedforward using sampled-data differentiator

Masahiro Mae, M.J. van Haren, Wataru Ohnishi, Tom A.E. Oomen, Hiroshi Fujimoto

Research output: Contribution to conferenceAbstractAcademic

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Abstract

Increasing performance requirements result in demanding intersample performance improvement in industrial motion systems while there is a limitation of sampling time. The aim of this study is to improve intersample performance by using a discrete-time linearly parameterized feedforward while maintaining on-sample performance in high-precision mechatronic systems. The developed approach is parameterized with a sampled-data differentiator using single-rate and multirate inversion. The resulting framework improves both on-sample and intersample behavior compared to using a conventional backward differentiator. The performance improvement is demonstrated in a benchmark motion system.
Original languageEnglish
Number of pages1
Publication statusPublished - 2022
EventJoint 9th IFAC Symposium on Mechatronic Systems (Mechatronics 2022) and 16th International Conference on Motion and Vibration Control (MoViC 2022) - University of California, Los Angeles, United States
Duration: 7 Sept 20229 Sept 2022

Conference

ConferenceJoint 9th IFAC Symposium on Mechatronic Systems (Mechatronics 2022) and 16th International Conference on Motion and Vibration Control (MoViC 2022)
Country/TerritoryUnited States
CityLos Angeles
Period7/09/229/09/22

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