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A neural-network-based controller for piezoelectric-actuated stick-slip devices

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


Long Cheng

Weichuan Liu

Tingwen Huang

Zeng Guang Hou

Min Tan


© 1982-2012 IEEE. Piezoelectric-actuated stick-slip device (PASSD) is a highly promising equipment that is 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 macrolevel motion with the microlevel precision. Due to the following two reasons: The complicated relative motion between the end-effector and the driving object, and the inherent hysteresis nonlinearity in the PEA, the ultraprecision displacement control of the end-effector of PASSDs raises a real challenge, which is rarely reported in the literature. Toward 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 online 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 Type Article
Acceptance Date Jul 22, 2017
Online Publication Date Aug 17, 2017
Publication Date Mar 1, 2018
Deposit Date Oct 8, 2019
Journal IEEE Transactions on Industrial Electronics
Print ISSN 0278-0046
Electronic ISSN 1557-9948
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 65
Issue 3
Pages 2598-2607
Public URL