Load is the main external disturbance of a parallel robot manipulator. This disturbance will cause dynamic coupling among different degrees of freedom and make heaps of model-based control methods difficult to apply. In order to compensate this disturbance, it is crucial to obtain an accurate dynamic model of load. However, in practice, the load is always uncertain and its dynamic parameters are arduous to know a priori. To cope with this problem, this paper proposes a novel and simple approach to identify the dynamic parameters of load. Firstly, the dynamic model of the parallel robot manipulator with uncertain load is established and the dynamic coupling caused by load is also analyzed. Then, according to the dynamic model, the excitation signal is designed and a weak nonlinear dynamic model is derived. Furthermore, the identification model is presented and the identification algorithm based on the extended Kalman filter is designed. Lastly, numerical simulation results, obtained using a six-degree-of-freedom Gough–Stewart parallel manipulator, demonstrate the good estimation performance of the proposed method.

中文翻译：

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基于扩展卡尔曼滤波的并联机器人机械臂载荷参数识别

负载是并联机器人操纵器的主要外部干扰。这种干扰将导致不同自由度之间的动态耦合，并使基于模型的控制方法难以应用。为了补偿这种干扰，获得准确的负载动态模型至关重要。然而，在实践中，负载始终是不确定的，并且其动态参数很难事先知道。为了解决这个问题，本文提出了一种新颖而简单的方法来识别负载的动态参数。首先，建立了具有不确定负载的并联机器人机械手的动力学模型，并分析了负载引起的动态耦合。然后，根据动力学模型，设计激励信号，并推导了弱非线性动力学模型。此外，提出了识别模型，并设计了基于扩展卡尔曼滤波器的识别算法。最后，使用六自由度Gough-Stewart并联机械手获得的数值模拟结果证明了该方法的良好估计性能。