Robotic gait trainers are used all over the world for the rehabilitation of stroke patients, despite relatively little is known about how the robots should be controlled to achieve the optimal improvement. Most devices control complete joint trajectories and assume symmetry between both legs by either a position or an impedance control. However we believe that the control should not be on a joint level but on a subtask level (i.e. foot clearance, balance control). To this end we have chosen for virtual model control (VMC) to define a set of controllers that can assist in each of these tasks. Thus enabling the exoskeleton to offer selective support and evaluation of each substask during rehabilitation training. The bottleneck of the VMC performance is the ability to offer an end point impedance at the ankle as the arm between the joints is largest here. This endpoint impedance is evaluated in this paper to show the ability of our exoskeleton to offer the required moments to support all the gait functions defined in this paper. We have shown that it is possible to implement the VMCs necessary for selective support of gait functions using series elastic actuators with a non-linear transmission. For the vertical direction we measured an stiffness of 5 kN/m for all ranges at frequencies of up to 1 Hz as a near ideal spring. In the horizontal we measured op to 0.5 kN/m in the same frequency range. The crosstalk between the vertical and the horizontal directions has been shown to be small. This means that it is possible to selectively offer forces in either vertical or horizontal directions.

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