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dc.contributor.advisorÇakmakcı, Melih
dc.contributor.authorLeblebicioğlu, Damla
dc.date.accessioned2021-10-08T13:59:58Z
dc.date.available2021-10-08T13:59:58Z
dc.date.copyright2021-09
dc.date.issued2021-09
dc.date.submitted2021-10-06
dc.identifier.urihttp://hdl.handle.net/11693/76608
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Master's): Bilkent University, Mechanical Engineering, İhsan Doğramacı Bilkent University, 2021.en_US
dc.descriptionIncludes bibliographical references (leaves 121-125).en_US
dc.description.abstractUnmanned aerospace vehicles (such as rockets, drones, and satellites) usually carry sensors as their primary payload. These sensors (i.e., electro-optical and/or infrared imaging cameras) are used for image processing, target tracking, surveillance, mapping, and providing high-resolution imagery for environmental surveys. It is crucial to obtain a steady image in all of those applications. This is typically accomplished by using multi-axis gimbal systems. This study concentrates on the modeling and control of a multi-axis gimbal system that will be mounted on a surface-to-surface tactical missile. A novel and fully detailed procedure is proposed to derive the nonlinear and highly coupled EOMs (Equations of Motion) of the two-axis gimbal system. Different from the existing works, Forward Dynamics of the two-axis gimbal system is modeled using multi-body dynamics modeling techniques. In addition to Forward Dynamics model, Inverse Dynamics model is generated to estimate the complementary torques associated with the state and mechanism-dependent, complex disturbances acting on the system. Forward and Inverse Dynamics models are used in Monte Carlo Simulations (MCSs) for Sensitivity Analysis. A multilayer perceptron (MLP) structure based disturbance compensator is implemented to cope with external and internal disturbances and parameter uncertainities through torque input channel. Comparisons with well known controllers such as cascaded PID, ADRC (Active Disturbance Rejection Control), Inverse Dynamics based controllers show that the NN (neural network)-based controller is more succesful in the full operational range without requiring any tuning or adjustment. Implementation of MLP assisted closed-loop control with simulations using Simulink® are performed. Finally, proposed control algorithms are tested on the physical system by using Simulink® Real-Time (xPC Target). Comparative results are presented in figures and tables in the thesis.en_US
dc.description.statementofresponsibilityby Damla Leblebicioğluen_US
dc.format.extentxxiii, 139 leaves : charts, graphics, tables ; 30 cm.en_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMmulti-axis gimbal systemen_US
dc.subjectReal-time controlen_US
dc.subjectNeural networken_US
dc.subjectMulti-body dynamics modelingen_US
dc.subjectADRCen_US
dc.subjectInverse dynamicsen_US
dc.subjectDisturbance torque compensationen_US
dc.subjectMonte Carlo simulationsen_US
dc.titleLearning based control compensation for multi-axis gimbal systems using inverse and forward dynamicsen_US
dc.title.alternativeÇok eksenli gimbal sistemleri için ileri ve geri dinamikleri kullanan öğrenme tabanlı kontrol dengeleyicisien_US
dc.typeThesisen_US
dc.departmentDepartment of Mechanical Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB132835
dc.embargo.release2022-04-06


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