Adaptive feedforward control for dynamically substructured systems based on neural network compensation

Guang Li*, J. Na, D. P. Stoten, X. Ren

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

3 Citations (Scopus)

Abstract

The potential applications of dynamically substructured systems (DSS) with both numerical and physical substructures can be found in diverse dynamics testing fields. In this paper, a feedforward adaptive controller based on a neural network (NN) is proposed to improve the DSS testing performance. To facilitate the NN compensation design, a modified DSS framework is developed so that the DSS control can be considered as a regulation problem with disturbance rejection. Then an NN feedforward compensation technique is proposed to cope with uncertainties and nonlinearities in the DSS physical substructure. The proposed NN technique generalizes the existing results in the literature. Real-time experimental results on a mechanical test rig demonstrate the improved performance by using the NN compensationstrategy.

Original languageEnglish
Title of host publicationIFAC Proceedings Volumes (IFAC-PapersOnline)
Pages944-949
Number of pages6
Volume18
EditionPART 1
DOIs
Publication statusPublished - 1 Dec 2011
Event18th IFAC World Congress - Milano, United Kingdom
Duration: 28 Aug 20112 Sep 2011

Conference

Conference18th IFAC World Congress
CountryUnited Kingdom
CityMilano
Period28/08/112/09/11

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  • Cite this

    Li, G., Na, J., Stoten, D. P., & Ren, X. (2011). Adaptive feedforward control for dynamically substructured systems based on neural network compensation. In IFAC Proceedings Volumes (IFAC-PapersOnline) (PART 1 ed., Vol. 18, pp. 944-949) https://doi.org/10.3182/20110828-6-IT-1002.00465