Generalized Input Preshaping Vibration Control Approach for Multi-Link Flexible Manipulators using Machine Intelligence*

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dc.authoridİlman, Mehmet Mert/0000-0001-7664-5217
dc.authoridYavuz, Sahin/0000-0001-9007-772X
dc.authoridYILDIRIM TASER, Pelin/0000-0002-5767-2700
dc.authorwosidİlman, Mehmet Mert/O-7252-2019
dc.authorwosidYavuz, Sahin/P-2105-2019
dc.authorwosidYILDIRIM TASER, Pelin/O-6422-2019
dc.contributor.authorIlman, Mehmet Mert
dc.contributor.authorYavuz, Sahin
dc.contributor.authorTaser, Pelin Yildirim
dc.date.accessioned2024-03-09T18:48:22Z
dc.date.available2024-03-09T18:48:22Z
dc.date.issued2022
dc.departmentİzmir Bakırçay Üniversitesien_US
dc.description.abstractIn this study, an open-loop vibration control through parameter tuning method for multi-link flexible manipulators is generalized using the machine learning paradigm. The experimental studies part of the study consists of two stages. In the first stage, a decision tree model is created using the C4.5 algorithm to predict transient (during motion) and residual (stationary) vibration levels, and decision rules are derived from this model. The paper utilizes the C4.5 (decision tree) algorithm on a broad experimental dataset and compares its performance to that of other four well-known traditional machine learning algorithms (Naive Bayes (NB), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and k-Nearest Neighbor (kNN)). The reason for choosing the decision tree method is that it has a transparent decision-making mechanism that works in tandem with the Explainable Artificial Intelligence (XAI) perspective. The experiments showed that the C4.5 algorithm achieved the best classification performance for predicting both root mean square (RMS) values of residual and transient vibrations (RMSres and RMStrans) with 92.25% and 83.75% accuracy rates. In the second stage, the generalized coefficients for use in parameter tuning were found by interpreting the rules obtained from this model. Furthermore, the rules derived from the tree in this study were applied to different systems. Control applications showed that an average of 93.9% and 84.5% suppression ratios in residual and transient vibrations could be achieved with this method. In addition, vibration settling times were shortened by an average of 41.4 seconds. Following the experimental studies, the simulation was used to compare results with the aid of Matlab Simscape-Multibody. The results prove that the rules obtained from the tree in this study generate successful results in several different systems.en_US
dc.identifier.doi10.1016/j.mechatronics.2021.102735
dc.identifier.issn0957-4158
dc.identifier.scopus2-s2.0-85121970712en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.mechatronics.2021.102735
dc.identifier.urihttps://hdl.handle.net/20.500.14034/1313
dc.identifier.volume82en_US
dc.identifier.wosWOS:000795946900003en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofMechatronicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDecision Tree; Explainable Artificial Intelligence; Flexible Manipulators; Machine Learning; Vibration Control; Input Shapingen_US
dc.titleGeneralized Input Preshaping Vibration Control Approach for Multi-Link Flexible Manipulators using Machine Intelligence*en_US
dc.typeArticleen_US

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