An iterative learning control with learnable band extension for the nanopositioning stage

Huang, C; Yang, Z; Li, J

An iterative learning control with learnable band extension for the nanopositioning stage

Huang, C Yang, Z Li, J

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Publisher:: Emerald
Publication Type:: Journal Article
Citation:: Assembly Automation, 2022, 42, (5), pp. 677-685
Issue Date:: 2022-10-17

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Field	Value	Language
dc.contributor.author	Huang, C
dc.contributor.author	Yang, Z
dc.contributor.author	Li, J
dc.date.accessioned	2023-06-28T05:19:01Z
dc.date.available	2023-06-28T05:19:01Z
dc.date.issued	2022-10-17
dc.identifier.citation	Assembly Automation, 2022, 42, (5), pp. 677-685
dc.identifier.issn	0144-5154
dc.identifier.issn	0144-5154
dc.identifier.uri	http://hdl.handle.net/10453/170944
dc.description.abstract	Purpose: Due to the advantages of fast response, high positioning precision and large stiffness, the piezoelectric-actuated nanopositioning stage is widely used in the micro/nanomachining fields. However, due to the inherent nonlinear hysteresis of the piezoelectric-actuator, the positioning accuracy of nanopositioning stage is greatly degraded. Besides, the nanopositioning stage is always performed with repetitive trajectories as the reference signals in applications, which makes the hysteresis behavior periodic. To this end, an adaptive resonance suppression iterative learning control (ARS-ILC) is proposed to address the hysteresis effect. With this effort, the positioning accuracy of the nanopositioning stage is improved. Design/methodology/approach: The hysteresis behavior is identified by the Prandtl–Ishlinskii model. By establishing a convergence function, it is demonstrated that the learnable band of ILC is restricted by the lightly damping resonance of nanopositioning stage. Then, an adaptive notch filter (ANF) with constrained poles and zeros is adopted to suppress the resonant peak. Finally, online stability supervision (OSS) is used to ensure that the estimated frequency converges to the resonant frequency. Findings: A series of experiments were carried out in the nanopositioning stage, and the results validated that the OSS is available to ensure the convergence of the ANF. Furthermore, the learnable band was extended via ARS-ILC; thus, the hysteresis behavior of nanopositioning stage has been canceled. Originality/value: Due to high accuracy and easy implementation, the ARS-ILC can be used in not only nanopositioning stage control but other fabrication process control with repetitive motion.
dc.language	en
dc.publisher	Emerald
dc.relation.ispartof	Assembly Automation
dc.relation.isbasedon	10.1108/AA-03-2022-0070
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0902 Automotive Engineering, 0906 Electrical and Electronic Engineering, 0910 Manufacturing Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.title	An iterative learning control with learnable band extension for the nanopositioning stage
dc.type	Journal Article
utslib.citation.volume	42
utslib.for	0902 Automotive Engineering
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0910 Manufacturing Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access	*
dc.date.updated	2023-06-28T05:19:00Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	42
utslib.citation.issue	5

Abstract:

Purpose: Due to the advantages of fast response, high positioning precision and large stiffness, the piezoelectric-actuated nanopositioning stage is widely used in the micro/nanomachining fields. However, due to the inherent nonlinear hysteresis of the piezoelectric-actuator, the positioning accuracy of nanopositioning stage is greatly degraded. Besides, the nanopositioning stage is always performed with repetitive trajectories as the reference signals in applications, which makes the hysteresis behavior periodic. To this end, an adaptive resonance suppression iterative learning control (ARS-ILC) is proposed to address the hysteresis effect. With this effort, the positioning accuracy of the nanopositioning stage is improved. Design/methodology/approach: The hysteresis behavior is identified by the Prandtl–Ishlinskii model. By establishing a convergence function, it is demonstrated that the learnable band of ILC is restricted by the lightly damping resonance of nanopositioning stage. Then, an adaptive notch filter (ANF) with constrained poles and zeros is adopted to suppress the resonant peak. Finally, online stability supervision (OSS) is used to ensure that the estimated frequency converges to the resonant frequency. Findings: A series of experiments were carried out in the nanopositioning stage, and the results validated that the OSS is available to ensure the convergence of the ANF. Furthermore, the learnable band was extended via ARS-ILC; thus, the hysteresis behavior of nanopositioning stage has been canceled. Originality/value: Due to high accuracy and easy implementation, the ARS-ILC can be used in not only nanopositioning stage control but other fabrication process control with repetitive motion.

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http://hdl.handle.net/10453/170944