基于RBF神经网络的闭链下肢康复机器人自适应补偿控制  

Adaptive Compensation Control of Closed-chain Lower Limb Rehabilitation Robots Based on the RBF Neural Network

作　　者：李东琦 秦建军 孙茂琳 郑皓冉 李伟[3] Li Dongqi;Qin Jianjun;Sun Maolin;Zheng Haoran;Li Wei(School of Mechanical-electronic and Vehicle Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China;Beijing Engineering Research Center of Monitoring for Construction Safety,Beijing 100044,China;Institute of Sports Medicine,General Administration of Sport of China,Beijing 100061,China)

机构地区：[1]北京建筑大学机电与车辆工程学院,北京100044 [2]北京市建筑安全监测工程技术研究中心,北京100044 [3]国家体育总局运动医学研究所,北京100061

出　　处：《机械传动》2024年第4期60-68,共9页Journal of Mechanical Transmission

基　　金：国家重点研发计划主动健康和老龄化科技应对重点专项(2020YFC2006705);北京市建筑安全监测工程技术研究中心研究基金项目(BJC2020K012);北京建筑大学研究生创新项目(PG2023141)。

摘　　要：在下肢康复机器人的康复训练过程中,模型参数、环境干扰等不确定性因素会影响机器人轨迹跟踪的精度。针对这一问题,提出了一种基于径向基函数(Radial Basis Function,RBF)神经网络的自适应补偿控制,该控制方法能够提高机械系统轨迹跟踪的精确性。首先,设计一款具有4种工作模式、运动稳定的闭链卧式下肢康复机器人结构;然后,利用拉格朗日方法求解动力学名义模型,将康复装置的模型参数以及外界干扰等不确定性因素分离出来,并设计基于RBF神经网络的自适应补偿算法对其进行逼近控制;最后,通过Matlab/Simulink环境对其进行仿真验证,证明了该控制策略的有效性。结果显示,在人体步态曲线轨迹跟踪中,提出的基于RBF神经网络的自适应补偿算法相比传统的模糊比例-积分-微分(Proportional Integral Derivative,PID)控制的方法响应速度快、跟踪效果好,且髋关节和膝关节轨迹跟踪的角度误差峰值分别为0.08°和0.13°,远小于患者下肢在康复运动中的转动角度。设计了单腿样机试验,试验结果表明,采用的RBF补偿自适应控制器能够实现高精度的跟踪结果,也能够满足患者在康复训练中安全性的要求。In the rehabilitation training process of lower limb rehabilitation robots,the existence of uncer-tain factors such as model parameters and environmental interference will affect the accuracy of trajectory track-ing of the robot.To solve this problem,an adaptive compensation control based on the radial basis function(RBF)neural network is proposed.This control method can improve the accuracy of mechanical system trajecto-ry tracking.Firstly,a closed chain horizontal lower limb rehabilitation robot structure with four working modes and stable movement is designed.Secondly,the Lagrange method is used to solve the kinetic nominal model,the uncertainty factors such as model parameters and external interference of the rehabilitation device are separated,and the adaptive compensation algorithm based on the RBF neural network is designed for the approximate con-trol.Finally,the Matlab/Simulink environment is used to verify the effectiveness of the control strategy.The re-sults show that,compared with the traditional fuzzy proportional integral derivative(PID)control method,the adaptive compensation algorithm based on the RBF neural network has a faster response speed and better track-ing effect in human gait curve trajectory tracking.Moreover,the peak angle errors of the hip joint and the knee joint trajectory tracking are 0.08°and 0.13°respectively,which are much less than the rotation angle of pa-tients'lower limbs in rehabilitation exercise.A single-leg prototype experiment is designed to show that the RBF compensation adaptive controller used in the study can achieve high precision tracking results and meet the safety requirements of patients in rehabilitation training.

关 键 词：下肢康复机器人 闭链结构 RBF神经网络 不确定性 自适应补偿控制 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP183[自动化与计算机技术—控制科学与工程] TH789[机械工程—仪器科学与技术]