基于改进LuGre摩擦模型的机器人关节摩擦力辨识研究

Robot Joint Friction Identification Based on Improved LuGre Friction Model

机器人在精准装配时,摩擦力影响着控制精度。利用LuGre摩擦模型进行关节力矩计算时,机器人关节摩擦力具有周期性纹波误差。针对此问题提出一种改进的LuGre摩擦模型,包括LuGre摩擦模型表示的稳态摩擦力,以及与速度相关的位置依赖项。对摩擦模型进行分步辨识,利用LuGre摩擦模型的特征,对稳态摩擦力参数进行辨识,通过SVM多类分类算法、支持向量回归(SVR)和最小二乘法求解方程组,对模型中的位置依赖项进行参数辨识。实验结果表明,机器人在不同负载下运行,使用改进模型及辨识方法计算关节摩擦力矩时,误差可以降低50%以上。

Friction affects control accuracy of robot during precision assembly.When LuGre friction model is used for joint torque calculation,the robot joint friction force has periodic ripple errors.An improved LuGre friction model was proposed to address this problem,including a steady-state friction force represented by the LuGre friction model,and a velocity-dependent position-dependent term.The friction model was identified in steps,and the steady-state friction parameters were identified using the features of the LuGre friction model,and the position-dependent terms in the model were parametrically identified by the SVM multi-class classification algorithm,support vector regression(SVR) and least squares method to solve the system equations.The experimental results show that the error can be reduced by more than 50% when the robot is operated under different loads using the improved model and the identification method to calculate the joint friction torque.