Review of Recent Progress in Robotic Knee Prosthesis Related Techniques:Structure,Actuation and Control

As the essential technology of human-robotics interactive wearable devices,the robotic knee prosthesis can provide above-knee amputations with functional knee compensations to realize their physical and psychological social regression.With the development of mechanical and mechatronic science and technology,the fully active knee prosthesis that can provide subjects with actuating torques has demonstrated a better wearing performance in slope walking and stair ascent when compared with the passive and the semi-active ones.Additionally,with intelligent human-robotics control strategies and algorithms,the wearing effect of the knee prosthesis has been greatly enhanced in terms of stance stability and swing mobility.Therefore,to help readers to obtain an overview of recent progress in robotic knee prosthesis,this paper systematically categorized knee prostheses according to their integrated functions and introduced related research in the past ten years(2010−2020)regarding(1)mechanical design,including uniaxial,four-bar,and multi-bar knee structures,(2)actuating technology,including rigid and elastic actuation,and(3)control method,including mode identification,motion prediction,and automatic control.Quantitative and qualitative analysis and comparison of robotic knee prosthesis-related techniques are conducted.The development trends are concluded as follows:(1)bionic and lightweight structures with better mechanical performance,(2)bionic elastic actuation with energy-saving effect,(3)artificial intelligence-based bionic prosthetic control.Besides,challenges and innovative insights of customized lightweight bionic knee joint structure,highly efficient compact bionic actuation,and personalized daily multi-mode gait adaptation are also discussed in-depth to facilitate the future development of the robotic knee prosthesis.

Robotic Knee Tracking Control to Mimic the Intact Human Knee Profile Based on Actor-Critic Reinforcement Learning

We address a state-of-the-art reinforcement learning(RL)control approach to automatically configure robotic pros-thesis impedance parameters to enable end-to-end,continuous locomotion intended for transfemoral amputee subjects.Specifically,our actor-critic based RL provides tracking control of a robotic knee prosthesis to mimic the intact knee profile.This is a significant advance from our previous RL based automatic tuning of prosthesis control parameters which have centered on regulation control with a designer prescribed robotic knee profile as the target.In addition to presenting the tracking control algorithm based on direct heuristic dynamic programming(dHDP),we provide a control performance guarantee including the case of constrained inputs.We show that our proposed tracking control possesses several important properties,such as weight convergence of the learning networks,Bellman(sub)optimality of the cost-to-go value function and control input,and practical stability of the human-robot system.We further provide a systematic simulation of the proposed tracking control using a realistic human-robot system simulator,the OpenSim,to emulate how the dHDP enables level ground walking,walking on different terrains and at different paces.These results show that our proposed dHDP based tracking control is not only theoretically suitable,but also practically useful.