Adaptive structural control using dynamic hyperspace

dc.contributor.author Laflamme, Simon
dc.contributor.author Laflamme, Simon
dc.contributor.department Civil, Construction and Environmental Engineering
dc.contributor.department Electrical and Computer Engineering
dc.contributor.department Center for Nondestructive Evaluation (CNDE)
dc.date 2018-02-22T21:13:39.000
dc.date.accessioned 2020-06-30T01:12:22Z
dc.date.available 2020-06-30T01:12:22Z
dc.date.copyright Thu Jan 01 00:00:00 UTC 2015
dc.date.issued 2015-01-01
dc.description.abstract <p>The design of closed-loop structural control systems necessitates a certain level of robustness to cope with system uncertainties. Neurocontrollers, a type of adaptive control system, have been proposed to cope with those uncertainties. However, the performance of neural networks can be substantially influenced by the choice of the input space, or the hyperspace in which the representation lies. For instance, input selection may influence computation time, adaptation speed, effects of the curse of dimensionality, understanding of the representation, and model complexity. Input space selection is often overlooked in literature, and inputs are traditionally determined offline for an optimized performance of the neurocontroller. Such offline input selection is often unrealistic to conduct in the case of civil structures. In this paper, a novel method for automating the input selection process for neural networks is presented. The method is purposefully designed for online input selection during adaptive identification and control of nonlinear systems. Input selection is conducted online and sequentially, while the excitation is occurring. The algorithm designed for the adaptive input space assumes local quasi-stationarity of the time series, and embeds local maps sequentially in a delay vector using the embedding theorem. The input space of the representation is subsequently updated. The performance of the proposed dynamic input selection method is demonstrated through simulating semi-active control of an existing structure located in Boston, MA, U.S.A. Simulation results show the substantial performance of the proposed algorithm over traditional fixed-inputs strategies.</p>
dc.description.comments <p>This article is published as Laflamme, S. "Adaptive Structural Control Using Dynamic Hyperspace." International Journal of Computational Methods and Experimental Measurements 3, no. 1 (2015): 49-64. doi: <a href="http://dx.doi.org/10.1021/ic101356n" target="_blank">10.2495/CMEM-V3-N1-49-64</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/ccee_pubs/155/
dc.identifier.articleid 1155
dc.identifier.contextkey 11623508
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ccee_pubs/155
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/13798
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/ccee_pubs/155/2015Laflamme_AdaptiveStructural.pdf|||Fri Jan 14 20:42:02 UTC 2022
dc.source.uri 10.2495/CMEM-V3-N1-49-64
dc.subject.disciplines Civil Engineering
dc.subject.disciplines Controls and Control Theory
dc.subject.disciplines Dynamics and Dynamical Systems
dc.subject.disciplines Structural Engineering
dc.subject.disciplines VLSI and Circuits, Embedded and Hardware Systems
dc.subject.keywords Adaptive control
dc.subject.keywords adaptive hyperspace
dc.subject.keywords adaptive input
dc.subject.keywords online sequential network
dc.subject.keywords self- organizing input
dc.subject.keywords sequential neural network
dc.subject.keywords structural control
dc.title Adaptive structural control using dynamic hyperspace
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 84547f08-8710-4934-b91e-ba5f46ab9abe
relation.isOrgUnitOfPublication 933e9c94-323c-4da9-9e8e-861692825f91
relation.isOrgUnitOfPublication a75a044c-d11e-44cd-af4f-dab1d83339ff
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