Dynamics simulation of parallel kinematic manipulators (PKM) and non-linear control methods require a precisely identified dynamics model and explicit generalized mass matrix. Standard methods, which identify so-called dynamic base-parameters, are not sufficient to this end. Algorithms for identifying the complete set of dynamic parameters were proposed for serial manipulators. A dedicated identification method for PKM does not exist, however. Such a method is introduced here for the large class of Delta-like PKM exploiting the parallel structure and making use of model simplifications specific to this class. The proposed method guarantees physical consistency of the identified parameters, and in particular a positive definite generalized mass matrix. The method is applied to a simulated model with exactly known parameters, which allows for verification of the obtained dynamic parameters. The results show that the generalized mass matrix, the acceleration, and the Coriolis, gravitation and friction terms in the equations of motion (EOM) are well approximated. The second example is a real 4-DOF industrial Delta robot ABB IRB 360-6/1600. For this robot, a physically consistent set of inertia and friction parameters is identified from measurements. The method allows prescribing estimated parameters, but does not rely on such data, e.g. from manufacturer or CAD.

中文翻译：

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类 Delta 机器人的专用动态参数识别

并联运动机械臂 (PKM) 的动力学仿真和非线性控制方法需要精确识别的动力学模型和显式广义质量矩阵。识别所谓动态基本参数的标准方法不足以达到此目的。为串行机械手提出了识别完整动态参数集的算法。然而，不存在 PKM 的专用识别方法。这里针对大类类似 Delta 的 PKM 引入了这样的方法，利用并行结构并利用特定于此类的模型简化。所提出的方法保证了所识别参数的物理一致性，特别是正定广义质量矩阵。该方法应用于具有确切已知参数的模拟模型，从而可以验证所获得的动态参数。结果表明，运动方程 (EOM) 中的广义质量矩阵、加速度以及科里奥利、重力和摩擦项都得到了很好的近似。第二个示例是真正的 4 自由度工业 Delta 机器人 ABB IRB 360-6/1600。对于该机器人，通过测量确定了一组物理上一致的惯性和摩擦参数。该方法允许规定估计参数，但不依赖于此类数据，例如来自制造商或CAD的数据。