Dynamic Stiffness Identification of Adjustable Stiffness Joint Based on Decoupling and Linearization
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摘要: 为了能够利用变刚度关节实现对机器人动态特性的调整，需要对关节的动态刚度进行有效的辨识和控制.本文首先根据机器人变刚度关节的结构特点建立了简化模型，并对其刚度输出特性表达做出假设；然后对模型中的力矩相关参数进行解耦，消除了关节刚度调节参数对力矩的影响，获取与刚度辨识相关的归一化力矩；利用泰勒展开对归一化力矩进行线性化处理，采用卡尔曼滤波器进行了系数优化，并进一步实现了对关节动态刚度的辨识.仿真中该刚度在线辨识方法可以将辨识误差控制在 ±2% 以内，在实现动态刚度辨识的基础上研究了基于前馈的刚度闭环控制方法，通过仿真实验验证了该方法对于机器人关节刚度闭环控制是有效的.Abstract: In order to take advantage of adjustable stiffness joint to adjust robot's dynamic feature, it is necessary to effectively identify and control the dynamic stiffness of the joint.Firstly, a simplified model is derived based on the structure features of robotic adjustable stiffness joint, and the assumption of stiffness output form is made.Then the torque related parameters in the model are decoupled, to eliminate the effect of adjusting parameter of joint stiffness on the torque, and thus the unified torque expression for stiffness identification is acquired.Linearization of the unified torque expression is then carried out by utilizing Tailor expansion, and Kalman filter is applied to optimizing the factors of the expansion.Based on this, the joint dynamic stiffness identification is achieved.It is proved the identification error is controlled within ± 2% by the dynamic stiffness online identification method in simulation.Based on the result of dynamic stiffness identification, feedforward based joint stiffness closed-loop control method is then studied.Simulation experiments show that the method is effective for robotic joint stiffness closed-loop control.
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