Intelligent Control of Cable-Driven Parallel Robot for Rehabilitation
A Master of Science thesis in Mechatronics Engineering by Omar Khattab entitled, “Intelligent Control of Cable-Driven Parallel Robot for Rehabilitation”, submitted in August 2025. Thesis advisor is Dr. Lotfi Romdhane and thesis co-advisor is Dr. Mohammad Jaradat. Soft copy is available (Thesis, Comp...
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| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | doctoralThesis |
| منشور في: |
2025
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/11073/33106 |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513438903959552 |
|---|---|
| author | Khattab, Omar |
| author_facet | Khattab, Omar |
| author_role | author |
| dc.contributor.none.fl_str_mv | Romdhane, Lotfi Jaradat, Mohammad |
| dc.creator.none.fl_str_mv | Khattab, Omar |
| dc.date.none.fl_str_mv | 2025-08 2026-01-26T06:37:46Z 2026-01-26T06:37:46Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 35.232-2025.52 https://hdl.handle.net/11073/33106 |
| dc.language.none.fl_str_mv | en_US |
| dc.relation.none.fl_str_mv | Master of Science in Mechatronics Engineering (MSMTR) |
| dc.subject.none.fl_str_mv | Cable Driven Parallel Robot Rehabilitation Soft Robotics Suspended Cable Robots Artificial Neural Networks |
| dc.title.none.fl_str_mv | Intelligent Control of Cable-Driven Parallel Robot for Rehabilitation |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/doctoralThesis |
| description | A Master of Science thesis in Mechatronics Engineering by Omar Khattab entitled, “Intelligent Control of Cable-Driven Parallel Robot for Rehabilitation”, submitted in August 2025. Thesis advisor is Dr. Lotfi Romdhane and thesis co-advisor is Dr. Mohammad Jaradat. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form). |
| format | doctoralThesis |
| id | aus_2e6da5f527367e87a05acdf70681346f |
| identifier_str_mv | 35.232-2025.52 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/33106 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Intelligent Control of Cable-Driven Parallel Robot for RehabilitationKhattab, OmarCable Driven Parallel RobotRehabilitationSoft RoboticsSuspended Cable RobotsArtificial Neural NetworksA Master of Science thesis in Mechatronics Engineering by Omar Khattab entitled, “Intelligent Control of Cable-Driven Parallel Robot for Rehabilitation”, submitted in August 2025. Thesis advisor is Dr. Lotfi Romdhane and thesis co-advisor is Dr. Mohammad Jaradat. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).This thesis aims to improve the accuracy and effectiveness of cable-driven parallel robots (CDPR) in medical rehabilitation by employing advanced kinematic solutions and neural network modelling. The research primarily focuses on three key areas: the geometric solution of inverse kinematics, the use of intelligent methods to solve forward kinematics, and the enhancement of the CDPR design to incorporate rotational movement capabilities. The first step involves a comprehensive analysis of the inverse kinematics, allowing for precise determination of cable lengths to achieve specific poses and orientations of the robot's end-effector. This is crucial for ensuring accurate and controlled movements, which are essential in the context of rehabilitation exercises. Following that an integration of artificial neural networks (ANN) to solve the forward kinematics problem, significantly improving the computational efficiency and accuracy of the robot's movement predictions. The research also explores modifications to the hardware design of the CDPR, enabling rotational movements that increase the range of possible rehabilitation exercises. These enhancements are expected to significantly improve the versatility and efficacy of the robot in targeting different muscle groups in the upper limb and the neck region. Preliminary results demonstrate that the proposed neural network model performs well in predicting the robot's position with high accuracy, particularly in the x and y coordinates. However, there are observed challenges in accurately predicting the θ (rotational) component, suggesting areas for further refinement. Future work will involve additional experimentation to validate these findings and further enhance the CDPR's performance, particularly in the context of complex, multi-dimensional trajectories. This thesis outlines the necessary components, including literature review, design methodology, and future research directions, required for the successful advancement of CDPR technology in rehabilitation applications.College of EngineeringMultidisciplinary ProgramMaster of Science in Mechatronics Engineering (MSMTR)Romdhane, LotfiJaradat, Mohammad2026-01-26T06:37:46Z2026-01-26T06:37:46Z2025-08info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2025.52https://hdl.handle.net/11073/33106en_USMaster of Science in Mechatronics Engineering (MSMTR)oai:repository.aus.edu:11073/331062026-01-26T08:07:06Z |
| spellingShingle | Intelligent Control of Cable-Driven Parallel Robot for Rehabilitation Khattab, Omar Cable Driven Parallel Robot Rehabilitation Soft Robotics Suspended Cable Robots Artificial Neural Networks |
| status_str | publishedVersion |
| title | Intelligent Control of Cable-Driven Parallel Robot for Rehabilitation |
| title_full | Intelligent Control of Cable-Driven Parallel Robot for Rehabilitation |
| title_fullStr | Intelligent Control of Cable-Driven Parallel Robot for Rehabilitation |
| title_full_unstemmed | Intelligent Control of Cable-Driven Parallel Robot for Rehabilitation |
| title_short | Intelligent Control of Cable-Driven Parallel Robot for Rehabilitation |
| title_sort | Intelligent Control of Cable-Driven Parallel Robot for Rehabilitation |
| topic | Cable Driven Parallel Robot Rehabilitation Soft Robotics Suspended Cable Robots Artificial Neural Networks |
| url | https://hdl.handle.net/11073/33106 |