Subject Adaptive Control Paradigms for Robotic Rehabilitation

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dc.contributor.advisorO'Malley, Marcia Ken_US
dc.creatorPehlivan, Ali Utkuen_US
dc.date.accessioned2017-08-07T18:17:54Zen_US
dc.date.available2017-08-07T18:17:54Zen_US
dc.date.created2016-05en_US
dc.date.issued2016-04-26en_US
dc.date.submittedMay 2016en_US
dc.date.updated2017-08-07T18:17:54Zen_US
dc.description.abstractAs the majority of the activities of daily living involve distal upper extremity movement, eff ective rehabilitation of the upper limbs, especially the distal joints, is crucial. Due to their inherent capabilities to deliver intensive and repetitive therapy, robotic devices are increasingly being used for the rehabilitation of neurologically impaired individuals. However, not every robotic device or therapy protocol has been shown to promote plasticity-mediated recovery. It is necessary that the robotic therapy must be capable of engaging the participant. Furthermore, the mechanical design of the robotic device must exhibit specifi c properties, such as low apparent inertia and friction, isotropic dynamic characteristics, and minimal backlash, to support sophisticated interaction modes. In this thesis a subject adaptive controller, capable of adaptively estimating position-dependent subject input and providing only the required amount of assistance is presented. This controller aims to maximize the participants' engagement in their therapy. Features of the controller were validated via simulations and experiments, and clinical validation was conducted with an elbow-forearm-wrist exoskeleton, the MAHI Exo-II. Results highlighted limitations in both the hardware's workspace and in the controller's performance. To address this limitations a novel wrist-forearm exoskeleton, the RiceWrsit-S, is proposed and an improved minimally assistive (mAAN) controller is presented. The controller is capable of estimating subject input as a function of time, hence it can estimate subject input regardless of position dependency, as opposed to the subject adaptive controller proposed in the rst part of the thesis. Novel features of the controller algorithm for maintaining subject engagement via performance based challenge modulation while still satisfying ultimately bounded error performance are presented. The mAAN controller and consistency of the accompanying algorithms are demonstrated experimentally with healthy subjects and with one subject with incomplete spinal cord injury in the RiceWrist-S. The proposed controllers and the novel exoskeletal device provide a means for a more eff ective robot-aided rehabilitation of neurologically impaired individuals.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationPehlivan, Ali Utku. "Subject Adaptive Control Paradigms for Robotic Rehabilitation." (2016) Diss., Rice University. <a href="https://hdl.handle.net/1911/96617">https://hdl.handle.net/1911/96617</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/96617en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectRoboticsen_US
dc.subjectNonlinear Controlen_US
dc.subjectExoskeletal Devicesen_US
dc.subjectRobotic Rehabilitationen_US
dc.titleSubject Adaptive Control Paradigms for Robotic Rehabilitationen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentMechanical Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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