通过拉格朗日法建立了斗杆、铲斗的二自由度动力学模型,并将系统动力学模型整理为未知组合参数的线性化表示.通过对拟合的关节角-液压缸位移函数进行求导得到关节力矩与液压缸驱动力间的函数关系,并在液压缸驱动力模型中引入摩擦力.分别采用递推最小二乘法与递推随机牛顿法对系统动力学模型的未知参数进行辨识.将辨识所得模型用于预测驱动力矩,与实测数据对比分析表明,随机牛顿法比最小二乘法的预测误差在斗杆与铲斗关节分别减少约65％和63％.结论表明随机牛顿法对系统噪声的鲁棒性更好,能获得系统精确的动力学模型.%The 2-DOF dynamic model of these two joint was built by using Euler - Lagrange formulation. It was simplified to a linear-in-parameter mode. The relationship between joint torque and actuator force was deduced by differentiating the function of relationship between the joint angle and cylinder displacement, and friction was introduced into the actuator force. The recursive least square method (RLS ) and recursive stochastic Newton algorithm ( RSNA ) were used to identify the unknown combination parameters respectively. Comparison experiments showed the predictive errors of joint torque of RSNA was reduced by 65% and 63% for the dipper joint and bucket joint respectively. The results demonstrated RSNA was more robust to the system noise. It can identify the dynamic parameters precisely.
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