Iterative Learning Compensation Control for Torque Tracking of Short Pulses Throughout Gait Cycle with an Ankle Exoskeleton

Research in gait entrainment can help to understand the neuromechanics of human gait and develop new rehabilitation approaches. We use a Bowden cable-driven ankle exoskeleton with series elastic actuation to apply periodic short small torque pulses to the ankle for gait entrainment studies. The exoskeleton dynamics at pulse initiation are inconsistent because torque pulses can coincide with any point in the gait cycle, introducing difficulty to pulse torque tracking. We investigated if iterative learning compensation for torque error from similar time instants of previous strides in combination with a proportional controller with damping injection to motor velocity could improve pretension torque tracking and pulse torque tracking of pulses initiated at the same time instant during the iterative learning. We showed that iterative learning improves root-mean-square-error of pretension torque and torque pulses throughout gait cycle except in early stance. The delay to track the desired torque pulse decreased throughout the gait cycle. We conclude that a comprehensive library of compensation terms for both pretension and pulse torque tracking would facilitate consistent torque pulses throughout the gait cycle thus facilitating our future gait entrainment research.