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

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

Abstract

© 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
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 65
• Issue: 3
• Pages: 2598-2607
• DOI: https://doi.org/10.1109/TIE.2017.2740826
• Public URL: https://uwe-repository.worktribe.com/output/3597004