一种改进的SCARA机器人动力学参数辨识方法

摘要/Abstract

摘要： 针对SCARA机器人动力学参数辨识问题，提出了一种基于优化改进傅里叶级数的辨识方法。根据SCARA机器人完整动力学方程，推导得到动力学模型的线性形式。采用改进傅里叶级数作为机器人关节的激励轨迹，使得关节角度满足连续周期性，并且关节角速度和角加速度在轨迹起始和停止时刻为零。为进一步提高辨识精度，以SCARA机器人观测矩阵的条件数为目标函数，采用基于排挤机制的小生境遗传算法对激励轨迹的系数进行优化。考虑到测量噪声的影响，采用加权最小二乘法（WLS）作为参数估计方法。实验结果表明，采用所提方法能准确辨识出SCARA机器人的动力学参数，两关节力矩测量值和预测值的残差均方根分别减小了11.50%和26.35%。

关键词: 机器人, 动力学, 小生境遗传算法, 激励轨迹, 参数辨识

Abstract: In order to identify the dynamics parameters of SCARA robots, a new identification method was proposed based on improved Fourier series. According to the complete dynamics equations of the SCARA robots, a linear form of the dynamics model was derived. The improved Fourier series was used as the excitation trajectory of the robot joints, and the joint angles satisfied the continuous periodicity, and the angular velocities and angular accelerations were as zero at the beginning and end of the trajectory. In order to further improve the identification accuracy, the condition numbers of the observation matrix of the SCARA robots were used as the objective function, and based on crowding mechanism the niche genetic algorithm was used to optimize the coefficients of the trajectory. Considering the influences of measurement noises, the weighted least square method (WLS) was used as the parameter estimation method. The experimental results show that the proposed method may accurately identify the dynamics parameters of the SCARA robots, torque measurements and predictive values of two joints, the residual root mean square values are reduced by 11.50% and 26.35% respectively.

Key words: robot, dynamics, niche genetic algorithm, excitation trajectory, parameter identification

中图分类号:

TP242

严浩1;白瑞林1;吉峰2. 一种改进的SCARA机器人动力学参数辨识方法[J]. 中国机械工程.

YAN Hao1;BAI Ruilin1;JI Feng2. An Improved Dynamics Parameter Identification Method for SCARA Robots[J]. China Mechanical Engineering.

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