一种改进的SCARA机器人动力学参数辨识方法

发布时间：2018-12-20 07:09

【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%。
[Abstract]:Aiming at the problem of dynamic parameter identification of SCARA robot, an identification method based on optimized improved Fourier series is proposed. According to the complete dynamic equation of SCARA robot, the linear form of dynamic model is derived. The improved Fourier series is used as the excitation trajectory of the robot joint, which makes the joint angle satisfy the continuous periodicity, and the angular velocity and acceleration of the joint are zero at the start and stop time of the trajectory. In order to further improve the identification accuracy, taking the condition number of SCARA robot observation matrix as objective function, niche genetic algorithm based on crowding mechanism is used to optimize the coefficient of excitation trajectory. Considering the influence of measurement noise, the weighted least square method (WLS) is used as the parameter estimation method. The experimental results show that the dynamic parameters of the SCARA robot can be accurately identified by the proposed method, and the root mean square of the residual errors of the measured and predicted joint torque values are reduced by 11.50% and 26.35%, respectively.
【作者单位】：江南大学轻工过程先进控制教育部重点实验室;无锡信捷电气股份有限公司;
【基金】：江苏省产学研前瞻性联合研究项目(BY2015019-38) 江苏高校优势学科建设工程项目(PAPD)
【分类号】：TP242

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