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A Neural-Network-Based Controller for Piezoelectric-Actuated Stick–Slip Devices

Long Cheng, Weichuan Liu, Chenguang Yang, Tingwen Huang, Zeng-Guang Hou, Min Tan

IEEE Transactions on Industrial Electronics, Volume: 65, Issue: 3, Pages: 2598 - 2607

Swansea University Author: Chenguang Yang

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DOI (Published version): 10.1109/TIE.2017.2740826

Abstract

Piezoelectric-actuated stick-slip device (PASSD) is a highly promising equipment that composed of one end-effector, one piezoelectric actuator (PEA) and one driving object adhered to the PEA. Since the end-effector can slip on the surface of the driving object, the PASSD is capable of realizing the...

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Published in: IEEE Transactions on Industrial Electronics
ISSN: 0278-0046 1557-9948
Published: 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa34959
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spelling 2018-04-13T17:40:21.8047343 v2 34959 2017-08-21 A Neural-Network-Based Controller for Piezoelectric-Actuated Stick–Slip Devices d2a5024448bfac00a9b3890a8404380b Chenguang Yang Chenguang Yang true false 2017-08-21 EEN Piezoelectric-actuated stick-slip device (PASSD) is a highly promising equipment that composed of one end-effector, one piezoelectric actuator (PEA) and one driving object adhered to the PEA. Since the end-effector can slip on the surface of the driving object, the PASSD is capable of realizing the macro-level motion with the micro-level precision. Due to the following two reasons: (1) the complicated relative motion between the end-effector and the driving object, and (2) the inherent hysteresis nonlinearity in the PEA, the ultraprecision displacement control of the end-effector of PASSDs raises a real challenge. Towards solving this challenge, a neural network based controller is proposed in this paper. First, a neural network based model is proposed to capture the relative motion between the end-effector and the driving object. Second, a neural network based inversion model is developed to on-line calculate the desired position of the PEA under the predesigned reference of the end-effector. Third, a dynamic linearized neural network based model predictive control method, which can effectively handle the hysteresis nonlinearity, is employed to implement the displacement control of the PEA, which finally results in an overall high-precision controller of the end-effector. Finally, a PASSD prototype has been implemented and tested through experimental studies to demonstrate the effectiveness of the proposed approach. Journal Article IEEE Transactions on Industrial Electronics 65 3 2598 2607 0278-0046 1557-9948 Stick-slip, piezoelectric actuator, endeffector, neural network 31 12 2018 2018-12-31 10.1109/TIE.2017.2740826 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2018-04-13T17:40:21.8047343 2017-08-21T17:20:11.7920236 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Long Cheng 1 Weichuan Liu 2 Chenguang Yang 3 Tingwen Huang 4 Zeng-Guang Hou 5 Min Tan 6 0034959-28082017161923.pdf TIE17Piezoelectric-Actuatedplain_wrapper.pdf 2017-08-28T16:19:23.8030000 Output 1246500 application/pdf Accepted Manuscript true 2017-08-28T00:00:00.0000000 true eng
title A Neural-Network-Based Controller for Piezoelectric-Actuated Stick–Slip Devices
spellingShingle A Neural-Network-Based Controller for Piezoelectric-Actuated Stick–Slip Devices
Chenguang Yang
title_short A Neural-Network-Based Controller for Piezoelectric-Actuated Stick–Slip Devices
title_full A Neural-Network-Based Controller for Piezoelectric-Actuated Stick–Slip Devices
title_fullStr A Neural-Network-Based Controller for Piezoelectric-Actuated Stick–Slip Devices
title_full_unstemmed A Neural-Network-Based Controller for Piezoelectric-Actuated Stick–Slip Devices
title_sort A Neural-Network-Based Controller for Piezoelectric-Actuated Stick–Slip Devices
author_id_str_mv d2a5024448bfac00a9b3890a8404380b
author_id_fullname_str_mv d2a5024448bfac00a9b3890a8404380b_***_Chenguang Yang
author Chenguang Yang
author2 Long Cheng
Weichuan Liu
Chenguang Yang
Tingwen Huang
Zeng-Guang Hou
Min Tan
format Journal article
container_title IEEE Transactions on Industrial Electronics
container_volume 65
container_issue 3
container_start_page 2598
publishDate 2018
institution Swansea University
issn 0278-0046
1557-9948
doi_str_mv 10.1109/TIE.2017.2740826
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description Piezoelectric-actuated stick-slip device (PASSD) is a highly promising equipment that composed of one end-effector, one piezoelectric actuator (PEA) and one driving object adhered to the PEA. Since the end-effector can slip on the surface of the driving object, the PASSD is capable of realizing the macro-level motion with the micro-level precision. Due to the following two reasons: (1) the complicated relative motion between the end-effector and the driving object, and (2) the inherent hysteresis nonlinearity in the PEA, the ultraprecision displacement control of the end-effector of PASSDs raises a real challenge. Towards solving this challenge, a neural network based controller is proposed in this paper. First, a neural network based model is proposed to capture the relative motion between the end-effector and the driving object. Second, a neural network based inversion model is developed to on-line calculate the desired position of the PEA under the predesigned reference of the end-effector. Third, a dynamic linearized neural network based model predictive control method, which can effectively handle the hysteresis nonlinearity, is employed to implement the displacement control of the PEA, which finally results in an overall high-precision controller of the end-effector. Finally, a PASSD prototype has been implemented and tested through experimental studies to demonstrate the effectiveness of the proposed approach.
published_date 2018-12-31T03:43:24Z
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score 11.036706

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