Non-periodic Transition-Tracking Switching via Learning-Based Decomposition

High-Speed Nano-Positioning Experiment Example

Jiangbo Liu, Qingze Zou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, the tracking problem involving nonperiodic tracking-transition switching is considered from a learning-based decomposition viewpoint. Non-periodic tracking-transition occurs in applications where precision tracking of a given desired trajectory in sections is intermediated to rapid transition of the output with given boundary conditions. The control challenges arise as the tracking and the transition sections are coupled together, post-switching oscillations of the output can be induced due to the mismatch of the boundary states, and the tracking performance can be limited by the nonminimum-phase zeros. Although these challenges have been tackled recently by combining the system-inversion with optimization technique, the solution obtained involves heavy online computation, and can be sensitive to model uncertainties. This work aims to overcome these limitations through a learning-based decomposition approach, where a library of input-output elements is constructed offline via iterative learning a priori, and then used online to both decompose the desired output (in tracking sections), design and optimize the desired transition output (in transition sections), and synthesize the control input based on the superposition principle. The proposed approach is demonstrated through experiment implementation on a piezoelectric actuator.

Original languageEnglish (US)
Title of host publication2018 Annual American Control Conference, ACC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6372-6377
Number of pages6
ISBN (Print)9781538654286
DOIs
StatePublished - Aug 9 2018
Event2018 Annual American Control Conference, ACC 2018 - Milwauke, United States
Duration: Jun 27 2018Jun 29 2018

Publication series

NameProceedings of the American Control Conference
Volume2018-June
ISSN (Print)0743-1619

Other

Other2018 Annual American Control Conference, ACC 2018
CountryUnited States
CityMilwauke
Period6/27/186/29/18

Fingerprint

Decomposition
Piezoelectric actuators
Experiments
Trajectories
Boundary conditions
Uncertainty

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Liu, J., & Zou, Q. (2018). Non-periodic Transition-Tracking Switching via Learning-Based Decomposition: High-Speed Nano-Positioning Experiment Example. In 2018 Annual American Control Conference, ACC 2018 (pp. 6372-6377). [8431505] (Proceedings of the American Control Conference; Vol. 2018-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACC.2018.8431505
Liu, Jiangbo ; Zou, Qingze. / Non-periodic Transition-Tracking Switching via Learning-Based Decomposition : High-Speed Nano-Positioning Experiment Example. 2018 Annual American Control Conference, ACC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 6372-6377 (Proceedings of the American Control Conference).
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Liu, J & Zou, Q 2018, Non-periodic Transition-Tracking Switching via Learning-Based Decomposition: High-Speed Nano-Positioning Experiment Example. in 2018 Annual American Control Conference, ACC 2018., 8431505, Proceedings of the American Control Conference, vol. 2018-June, Institute of Electrical and Electronics Engineers Inc., pp. 6372-6377, 2018 Annual American Control Conference, ACC 2018, Milwauke, United States, 6/27/18. https://doi.org/10.23919/ACC.2018.8431505

Non-periodic Transition-Tracking Switching via Learning-Based Decomposition : High-Speed Nano-Positioning Experiment Example. / Liu, Jiangbo; Zou, Qingze.

2018 Annual American Control Conference, ACC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 6372-6377 8431505 (Proceedings of the American Control Conference; Vol. 2018-June).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Liu J, Zou Q. Non-periodic Transition-Tracking Switching via Learning-Based Decomposition: High-Speed Nano-Positioning Experiment Example. In 2018 Annual American Control Conference, ACC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 6372-6377. 8431505. (Proceedings of the American Control Conference). https://doi.org/10.23919/ACC.2018.8431505