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Torque estimation of robot joint with harmonic drive transmission using a redundant adaptive robust extended Kalman filter

机译:使用冗余自适应鲁棒扩展卡尔曼滤波器的带有谐波传动的机器人关节扭矩估算

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摘要

A torque estimation method with adaptive robustness and optimality adjustment according to the load for modular and reconfigurable robot joint with harmonic drive transmission is proposed, on the basis of harmonic drive compliance model and redundant adaptive robust extended Kalman filter (RAREKF). The proposed approach can adapt torque estimation filtering optimality and robustness to the load variations by self-tuning the filtering gain and self-switching the filtering modes between optimal and robust. The redundant factor of RAREKF is designed as a function of the load to provide desirable tolerant capability to the modeling error and load-dependent filtering mode switching. The proposed joint torque estimation method has been experimentally studied in comparison with a commercial sensor, and the results have demonstrated the effectiveness of the proposed torque estimation technique.
机译:在谐波驱动依从模型和冗余自适应鲁棒扩展卡尔曼滤波器(RAREKF)的基础上,提出了一种针对负载的模块化和可重构的带有谐波传动的机器人关节,具有自适应鲁棒性和最优性的转矩估计方法。所提出的方法可以通过自调整滤波增益并在最佳和鲁棒性之间自动切换滤波模式,从而使转矩估计滤波的最优性和鲁棒性适应负载变化。 RAREKF的冗余因数被设计为负载的函数,以为建模误差和依赖于负载的滤波模式切换提供理想的容错能力。与商用传感器相比,已对提出的联合扭矩估算方法进行了实验研究,结果证明了所提出的扭矩估算技术的有效性。

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