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Title: Design and implementation of a novel lightweight soft upper limb exoskeleton using pneumatic actuator muscles
Author: Irshaidat, M. M.
ISNI:       0000 0004 7654 4063
Awarding Body: University of Salford
Current Institution: University of Salford
Date of Award: 2018
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Stroke is the leading cause of disability and weakness in the UK and around the world. Thus, stroke patients require an extensive rehabilitation therapy to regain some of the weaknesses. Many rehabilitation robotic devices have been designed and developed to assist the stroke patients to perform their activities of daily living and to perform repetitive movements. However, these devices remain unmanageable to use by the patients alone not only because they are cumbersome to use but also due to their weights, rigid, fix and non-portable characteristics. Thus there is a need to invent a novel exoskeleton soft arm that has a lightweight and a high power to rehab the elbow joint with lower cost and without the need to therapists. Here for elbow joint rehabilitation, we investigate and propose a novel exoskeleton soft robotic arm, which is wearable, lightweight and portable so that it would allow patients to perform repetitive motion therapy more often with a greater intensity in their homes and relevant to their Activities of Daily Living (ADL). The proposed arm consists of various bending pneumatic muscle actuators (pMA), where traditional pMA are not suitable. Testing on various pMA (traditional and bending) revealed its behaviour and the relationship between pressure, length, force, and bending angle in different setups such as isotonic and isometric. Experiments are done to analyse its non-linear behaviour, moreover, geometrical and numerical models are compared to the experimental results to validate the results. A developed control approach to control the soft arm is implemented to validate the design. Model reference adaptive control (MRAC) to control the arm using (Proportional, Integral, and Derivative) PID controller as an input for MRAC. Neural Network (NN) is also used in MRAC to improve the performance of MRAC.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available