Abstract
The paper designs an appropriate iterative learning control (ILC) algorithm based on the trajectory characteristics of upper exosk el eton robotic system. The procedure of mathematical modelling of an exoskeleton system for rehabilitation is given and synthesis of a control law with two loops. First (inner) loop represents exact linearization of a given system, and the second (outer) loop is synthesis of a iterative learning control law which consists of two loops, open and closed loop. In open loop ILC sgnPDD2 is applied, while in feedback classical PD control law is used. Finally, a simulation example is presented to illustrate the feasibility and effectiveness of the proposed advanced open-closed iterative learning control scheme.
Highlights
Stroke is second cause of mortality and disability in the world, [1]
It is noted in [4] and [5] that robot-aided sensorimotor training, especially in upper limbs, shows that more activity leads to better recovery and that recovered functions are sustained over long period
Body state of patients will improve with an increase in the number of training while the auxiliary level of robot and electrical stimulation will be reduced.In iterative learning control (ILC) the control input is directly updated between trials and it is this feature that makes it suitable for exoskeleton robots (i.e robotic assisted stroke rehabilitation),[12]
Summary
Stroke is second cause of mortality and disability in the world, [1]. Patients who survive stroke are faced with some degree of limb impairment, depending on the place in brain structure and size of caused damage. In order to enhance therapy deliveredby therapists, use of robotics emerged as aid in rehabilitation process,[3] It is noted in [4] and [5] that robot-aided sensorimotor training, especially in upper limbs, shows that more activity leads to better recovery and that recovered functions are sustained over long period. ILC uses knowledge obtained from the previous trial to adjust the control input for the current trial so that a better performance can be achieved. Body state of patients will improve with an increase in the number of training while the auxiliary level of robot and electrical stimulation will be reduced.In ILC the control input is directly updated between trials and it is this feature that makes it suitable for exoskeleton robots (i.e robotic assisted stroke rehabilitation),[12]. A simulation example is presented to illustrate the feasibility and effectiveness of the proposed advanced open-closed ILC scheme
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