A self-learning disturbance observer for nonlinear systems in feedback-error learning scheme

dc.contributor.author Kayacan, Erkan
dc.contributor.author Peschel, Joshua
dc.contributor.author Peschel, Joshua
dc.contributor.author Chowdhary, Girish
dc.contributor.department Agricultural and Biosystems Engineering
dc.date 2018-05-20T20:43:54.000
dc.date.accessioned 2020-06-29T22:43:51Z
dc.date.available 2020-06-29T22:43:51Z
dc.date.copyright Sun Jan 01 00:00:00 UTC 2017
dc.date.embargo 2018-06-01
dc.date.issued 2017-06-01
dc.description.abstract <p>This paper represents a novel online self-learning disturbance observer (SLDO) by benefiting from the combination of a type-2 neuro-fuzzy structure (T2NFS), feedback-error learning scheme and sliding mode control (SMC) theory. The SLDO is developed within a framework of feedback-error learning scheme in which a conventional estimation law and a T2NFS work in parallel. In this scheme, the latter learns uncertainties and becomes the leading estimator whereas the former provides the learning error to the T2NFS for learning system dynamics. A learning algorithm established on SMC theory is derived for an interval type-2 fuzzy logic system. In addition to the stability of the learning algorithm, the stability of the SLDO and the stability of the overall system are proven in the presence of time-varying disturbances. Thanks to learning process by the T2NFS, the simulation results show that the SLDO is able to estimate time-varying disturbances precisely as distinct from the basic nonlinear disturbance observer (BNDO) so that the controller based on the SLDO ensures robust control performance for systems with time-varying uncertainties, and maintains nominal performance in the absence of uncertainties.</p>
dc.description.comments <p>This is a manuscript of an article published as Kayacan, Erkan, Joshua M. Peschel, and Girish Chowdhary. "A self-learning disturbance observer for nonlinear systems in feedback-error learning scheme." <em>Engineering Applications of Artificial Intelligence</em> 62 (2017): 276-285. DOI: <a href="http://dx.doi.org/10.1016/j.engappai.2017.04.013" target="_blank">10.1016/j.engappai.2017.04.013</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/abe_eng_pubs/938/
dc.identifier.articleid 2219
dc.identifier.contextkey 12122412
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_pubs/938
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/1755
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/abe_eng_pubs/938/2017_Peschel_SelfLearning.pdf|||Sat Jan 15 02:32:07 UTC 2022
dc.source.uri 10.1016/j.engappai.2017.04.013
dc.subject.disciplines Agriculture
dc.subject.disciplines Artificial Intelligence and Robotics
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.keywords Disturbance observer
dc.subject.keywords Neural networks
dc.subject.keywords Neuro-fuzzy structure
dc.subject.keywords Online learning algorithm
dc.subject.keywords Robustness
dc.subject.keywords Sliding mode control
dc.subject.keywords Type-2 fuzzy logic systems
dc.subject.keywords Uncertainty
dc.title A self-learning disturbance observer for nonlinear systems in feedback-error learning scheme
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 3ab64f1f-e7f6-4daa-9a3a-3dbf28e8be78
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
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