Model Parameter Identification for Feed Drive Dynamics using Kernel-Based Methods

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dc.contributor.authorMcPherson, J.D.
dc.contributor.supervisorAhmadi, Keivan
dc.date.accessioned2024-05-31T20:34:14Z
dc.date.available2024-05-31T20:34:14Z
dc.date.issued2024
dc.degree.departmentDepartment of Mechanical Engineering
dc.degree.levelMaster of Applied Science MASc
dc.description.abstractThis thesis presents an application of kernel-based methods for identification of the feed drive's dynamics model parameters and prediction of the disturbances affecting feed drives during operation from the cutting forces. To this purpose, the Partially Linear - Least Squares Support Vector Machine (PL-LSSVM) and Kernel Recursive Least Squares - Tracker (KRLS-T) algorithms were utilised for batch identification and online prediction of disturbances. Experimental case studies were performed with two ball screw feed drives under simulated and real cutting conditions to verify the identification and in-operation prediction of cutting forces.
dc.description.scholarlevelGraduate
dc.identifier.bibliographicCitationAhmadi, K., Mehrabi, M., & McPherson, J.D. (2023). A kernel-based approach for identifying the dynamics of feed drives and robot joints. Control Engineering Practice. Preprint.
dc.identifier.urihttps://hdl.handle.net/1828/16588
dc.languageEnglisheng
dc.language.isoen
dc.rightsAvailable to the World Wide Web
dc.subjectFeed Drive
dc.subjectKernel
dc.titleModel Parameter Identification for Feed Drive Dynamics using Kernel-Based Methods
dc.typeThesis

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