Closing the control loop by providing somatosensory feedback to the user of a prosthesis is a well-known, long standing challenge in the field of prosthetics. Various approaches have been investigated for feedback restoration, ranging from direct neural stimulation to noninvasive sensory substitution methods. Although there are many studies presenting closed-loop systems, only a few of them objectively evaluated the closed-loop performance, mostly using vibrotactile stimulation. Importantly, the conclusions about the utility of the feedback were partly contradictory. The goal of the current study was to systematically investigate the capability of human subjects to control grasping force in closed loop using electrotactile feedback. We have developed a realistic experimental setup for virtual grasping, which operated in real time, included a set of real life objects, as well as a graphical and dynamical model of the prosthesis. We have used the setup to test 10 healthy, able bodied subjects to investigate the role of training, feedback and feedforward control, robustness of the closed loop, and the ability of the human subjects to generalize the control to previously “unseen” objects. Overall, the outcomes of this study are very optimistic with regard to the benefits of feedback and reveal various, practically relevant, aspects of closed-loop control.

No abstract

Abstract-In this paper design of a closed loop FES system for torque control is presented. Snap power worker s used for measuring muscle torque. Using this system torque is proportional to angle of a flexion so by controlling angle of a flexion torque is controlled too. During functional electrical stimulation 3 parameters can be changed: pulse width, pulse amplitude and time between two impulses. In this paper pulse amplitude and frequency are constant and system is controlled by changing pulse width. PI regulator is used to minimize difference between desired trajectory and system output. Parameters of a PI controller are obtained by minimizing mean square error on simulated system. In this paper NARX Radial based neural network model of system is used for simulation. Result on a real system show that this type of control system can be used for torque control.