平面关节型机器人关节力矩的卡尔曼估计

为了解决常用测电流获取机器人关节力矩方法中，各种噪声干扰使所得关节力矩波动较大，影响机器人控制信息可靠性的问题，提出基于机器人动力学模型的卡尔曼滤波方法对关节力矩进行估计.运用牛顿欧拉方法对平面关节型机器人（SCARA）进行动力学建模，获得非线性连续的机器人关节力矩方程.通过多元函数一阶泰勒展开将非线性连续的关节力矩方程转换为关于关节力矩的线性离散状态空间模型.利用卡尔曼滤波方法对关节力矩进行估计.实验结果表明，该关节力矩估计方法对机器人前两轴的关节力矩估计精度较好，与均值滤波方法相比均方根误差分别减少了2.9%和14.7%；且实时性较好，完成一次估计平均需时不超过1 ms.但关节力矩值的估计精度受动力学模型精度的影响.

Kalman filter-based SCARA robot joint torque estimation

A dynamic model based Kalman estimation method was designed for joint torque estimation in order to solve the problem that robot control information reliability was reduced because of the fluctuating joint torque value obtained by commonly used current measurement method while the current was affected by noise generated during robot operation. The Newton-Euler method was used for SCARA robot dynamic modeling and the nonlinear continuous joint torque equations were obtained. Then the nonlinear continuous joint torque equations were converted to linear discrete state space model of joint torque by first-order Taylor expansion of multivariate functions. The Kalman filter method was performed to estimate joint torque. Experimental results showed that the proposed method had better estimation accuracy for the first two axes of the robot and the mean square root error decreases by 2.9% and 14.7%, respectively, compared to mean filtering method. The real-time performance was good that the average time required to complete an estimation did not exceed 1 ms. The joint torque estimation accuracy was affected by the SCARA robot dynamic model accuracy.