Physics-guided neural networks for inversion-based feedforward control applied to linear motors

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Abstract

Ever-increasing throughput specifications in semiconductor manufacturing require operating high-precision mechatronics, such as linear motors, at higher accelerations. In turn this creates higher nonlinear parasitic forces that cannot be handled by industrial feedforward controllers. Motivated by this problem, in this paper we develop a general framework for inversion-based feedforward controller design using physics-guided neural networks (PGNNs). In contrast with black-box neural networks, the developed PGNNs embed prior physical knowledge in the input and hidden layers, which results in improved training convergence and learning of underlying physical laws. The PGNN inversion-based feedforward control framework is validated in simulation on an industrial linear motor, for which it achieves a mean average tracking error twenty times smaller than mass-acceleration feedforward in simulation
Original languageEnglish
Title of host publicationCCTA 2021 - 5th IEEE Conference on Control Technology and Applications
PublisherInstitute of Electrical and Electronics Engineers
Pages1115-1120
Number of pages6
ISBN (Electronic)978-1-6654-3643-4
DOIs
Publication statusPublished - 3 Jan 2022
Event5th IEEE Conference on Control Technology and Applications, CCTA 2021 - Online, San Diego, United States
Duration: 8 Aug 202111 Aug 2021
Conference number: 5
https://ccta2021.ieeecss.org/

Conference

Conference5th IEEE Conference on Control Technology and Applications, CCTA 2021
Abbreviated titleCCTA 2021
Country/TerritoryUnited States
CitySan Diego
Period8/08/2111/08/21
Internet address

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