Neural Network Based Sliding Mode Control for Robotic Manipulator

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dc.contributor.advisorShan, Jinjun
dc.contributor.authorGomes Carmo, Ingredy Gabriela
dc.date.accessioned2024-07-18T21:29:21Z
dc.date.available2024-07-18T21:29:21Z
dc.date.copyright2024-05-07
dc.date.issued2024-07-18
dc.date.updated2024-07-18T21:29:20Z
dc.degree.disciplineEarth & Space Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractRobotic manipulators are used in many applications. However, robotic arms are complex systems due to external disturbances, perturbations, and their coupled non-linear dynamics. This thesis aims to propose a robust control strategy for autonomous robotic manipulation. First, the trajectory tracking problem was introduced and an approach to overcome this issue using a sliding mode controller combined with a neural network is proposed. Then, the proposed approach is compared to classical and modern control methods including controllers from the literature to demonstrate the performance of the proposed controller. The proposed controller was then integrated with a grasp detection algorithm for an autonomous manipulation application. Simulations and hardware experiments were conducted to validate the performance of the proposed method.
dc.identifier.urihttps://hdl.handle.net/10315/42205
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectRobotics
dc.subjectEngineering
dc.subject.keywordsRobot manipulator
dc.subject.keywordsRobust control
dc.subject.keywordsSliding mode control
dc.subject.keywordsNeural network
dc.subject.keywordsRBFNN
dc.subject.keywordsTrajectory tracking
dc.subject.keywordsAutonomous robot
dc.subject.keywordsAutonomous manipulation
dc.titleNeural Network Based Sliding Mode Control for Robotic Manipulator
dc.typeElectronic Thesis or Dissertation

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