Humanoid control of lower limb exoskeleton robot based on human gait data with sliding mode neural network  

作　　者：Jun Yu Shuaishuai Zhang Aihui Wang Wei Li Zhengxiang Ma Xuebin Yue 

机构地区：[1]Zhongyuan‐Petersburg Aviation College,Zhongyuan University of Technology,Zhengzhou,China [2]School of Electric and Information Engineering,Zhongyuan University of Technology,Zhengzhou,China [3]School of Intelligent Engineering,Zhengzhou University of Aeronautics,Zhengzhou,China [4]Department of Electronic and Computer Engineering,Ritsumeikan University,Kusatsu,Shiga,Japan

出　　处：《CAAI Transactions on Intelligence Technology》2022年第4期606-616,共11页智能技术学报（英文）

基　　金：National Natural Science Foundation of China,Grant/Award Numbers:No.62073297,No.U1813201。

摘　　要：Lower limb exoskeleton robots offer an effective treatment for patients with lower extremity dysfunction.In order to improve the rehabilitation training effect based on the human motion mechanism,this paper proposes a humanoid sliding mode neural network controller based on the human gait.A humanoid model is constructed based on the human mechanism,and the parameterised gait trajectory is used as target to design the humanoid control system for robots.Considering the imprecision of the robot dynamics model,the neural network is adopted to compensate for the uncertain part of the model and improve the model accuracy.Moreover,the sliding mode control in the system improves the response speed,tracking performance,and stability of the control system.The Lyapunov stability analysis proves the stability of the control system theoretically.Meanwhile,an evaluation method using the similarity function is improved based on joint angle,velocity,and acceleration to evaluate the comfort of humans in rehabilitation training more reasonably.Finally,to verify the effectiveness of the proposed method,simulations are carried out based on experimental data.The results show that the control system could accurately track the target trajectory,of which the robot is highly similar to the human.

关 键 词：ROBOT NEURAL network 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP242[自动化与计算机技术—控制科学与工程]