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dc.contributor.authorRahimi Nohooji, Hamed
dc.contributor.supervisorProf. Ian Howarden_US

This thesis studies safe human-robot interaction utilizing the neural adaptive control design. First, novel tangent and secant barrier Lyapunov functions are constructed to provide stable position and velocity constrained controls, respectively. Then, neural backpropagation and the concept of the inverse differential Riccati equation are utilized to achieve the impedance adaption control for assistive human-robot interaction, and the optimal robot-environment interaction control, respectively. Finally, adaptive neural assist-as-needed control is developed for assistive robotic rehabilitation.

dc.publisherCurtin Universityen_US
dc.titleAdaptive Neural Control for Safe Human-Robot Interactionen_US
curtin.departmentMechanical Engineeringen_US
curtin.accessStatusOpen accessen_US
curtin.facultyScience and Engineeringen_US

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