  Loss of mobility or balance resulting from neural trauma is a critical
consideration in public health. Robotic exoskeletons hold great potential for
rehabilitation and assisted movement, yet optimal assist-as-needed (AAN)
control remains unresolved given pathological variance among patients. We
introduce a model predictive control (MPC) architecture for lower limb
exoskeletons centred around a fuzzy logic algorithm (FLA) identifying modes of
assistance based on human involvement. Assistance modes are: 1) passive for
human relaxed and robot dominant, 2) active-assist for human cooperation with
the task, and 3) safety in the case of human resistance to the robot. Human
torque is estimated from electromyography (EMG) signals prior to joint motions,
enabling advanced prediction of torque by the MPC and selection of assistance
mode by the FLA. The controller is demonstrated in hardware with three subjects
on a 1-DOF knee exoskeleton tracking a sinusoidal trajectory with human relaxed
assistive, and resistive. Experimental results show quick and appropriate
transfers among the assistance modes and satisfied assistive performance in
each mode. Results illustrate an objective approach to lower limb robotic
assistance through on-the-fly transition between modes of movement, providing a
new level of human-robot synergy for mobility assist and rehabilitation.
