Bio-Inspired Adaptive Control for Active Knee Exoprosthetics

On the quest to bring function of prosthetic legs closer to their biological counterparts, the intuitive interplay of their control with the user's impedance modulation is key. We present two control features to enable more physiological and more user-adaptive control of prosthetic legs: a neuromusculoskeletal impedance model (NeurImp) including a reflexive component, and a human model reference adaptive controller (HuMRAC), which can be combined with the former. In stance-phase simulations, the NeurImp allowed to control a prosthetic leg with physiological knee joint angle and moment. When perturbations were applied, the HuMRAC reduced the resulting root mean square error (RMSE) between simulated and physiological reference angle by 96%. In a pilot experiment with two unimpaired and one amputee subject, gait with the NeurImp deviated more from a physiological reference than with a conventional visco-elastic impedance controller. Subjects, however, preferred the NeurImp. When adding the HuMRAC to either of the two impedance controllers, the RMSE between the actual and the physiological reference angle was reduced by up to 54%. Subjects confirmed this finding and reported an easier stance-to-swing transition. Simulation and pilot experiment suggest that a reflex-based impedance controller combined with an adaptive controller may improve user-cooperative behavior of active knee exoprostheses.