Force Control of a Hydraulic Actuator With a Neural Network Inverse Model

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In this study, a learning-based force controller for a hydraulic actuator is presented. We propose a control method with an inverse model composed of a deep neural network, which accurately tracks a force trajectory. This learning-based controller can be trained offline using force and position data sets from the hydraulic actuator. The methodology for training the controller network and the experimental setup for data collection are proposed. The learning-based controller was implemented on a hydraulic actuator hardware platform. The proposed learning-based controller demonstrates improved tracking performance compared to that of conventional model-based adaptive control methods.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2021-04
Language
English
Article Type
Article
Citation

IEEE ROBOTICS AND AUTOMATION LETTERS, v.6, no.2, pp.2814 - 2821

ISSN
2377-3766
DOI
10.1109/LRA.2021.3062353
URI
http://hdl.handle.net/10203/282467
Appears in Collection
ME-Journal Papers(저널논문)
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