面向人机交互的下肢外骨骼导纳控制策略

Admittance Control Strategy of Lower Limb Exoskeleton Oriented to Human-machine Interaction

针对下肢外骨骼运动中的人机共融问题,提出了由导纳控制外环和位置控制内环组成的双环控制策略。外环采用导纳控制策略,并设计了基于Sigmoid函数的变导纳模型,将人体主动力矩转化为关节角度以修正期望轨迹,使人的意图得到表达。内环采用基于径向基函数(RBF)神经网络的位置控制器,利用RBF神经网络能逼近任意非线性函数的特性,消除建模所带来的误差和干扰;采用改进的莱文贝格-马夸特(L-M)算法,提高RBF神经网络的紧凑性和收敛性,并设置合理的参数初始值,提高收敛速度。搭建MATLAB仿真平台,仿真结果表明,所设计的控制策略使下肢外骨骼的人机交互效果良好,对人体有良好的助行效果。

In order to achieve human-machine integration in lower limb exoskeleton exercise,a dual-loop control strategy consisting of outer loop of admittance control and inner loop of position control is proposed in this paper.The admittance control strategy is adopted in the outer loop,and a variable admittance model based on Sigmoid function is designed,which converts the active moment of the human body into the joint angle to correct the desired trajectory,so that the human intention can be expressed.A position controller based on radial basis function(RBF)neural network is adopted in the inner loop.The RBF neural network can approximate any nonlinear function so that the errors and interference caused by modeling are eliminated.The improved Levenberg-Marquardt(L-M)algorithm is used to improve the compactness and convergence of RBF neural network,and reasonable initial parameters are set to improve the convergence speed.The MATLAB simulation platform is established,and the simulation results show that the proposed control strategy makes the lower limb exoskeleton have a good human-machine interaction effect,and a good walking effect on the human body.