The Iterative Learning Gain That Optimizes Real-Time Torque Tracking for Ankle Exoskeletons in Human Walking Under Gait Variations - Zhang, Juanjuan; Collins, Steven H. (Frontiers in Neurorobotics, 2021)
Lower-limb exoskeletons often use torque control to achieve easy manipulation of energy flow and ensure human safety. Accuracy of applied torque greatly affects system performance and therefore it is always of interest to be improved. Feed-forward type iterative learning as a compensation term for feedback control was proved effective in torque tracking of these devices with complicated dynamics during human walking, since it is effectively stride-wise integral control. Although it was added merely to benefit average tracking performance over multiple strides, we found that iterative learning after proper gain tuning can help improving real-time torque tracking of lower-limb exoskeletons during human walking. We used theoretical analysis and predicted an optimal gain as the inverse of the passive actuator stiffness. Walking experiments resulted in an optimum 0.9929 $±$ 0.3846 times the predicted one, which agreed with the hypothesis. Results of this study provides guidance for the design of torque controller in robotic legged locomotion systems and will help improving the performance of gait assistive robots.