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Multi-sensor fusion in Kalman-filter for high performance force sensing

机译:卡尔曼滤波器中的多传感器融合,可实现高性能力感测

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Sensorless force sensation by disturbance observer has been widely employed in numerous applications due to its superiority to the measurement by a force sensor. This paper introduces the development of the disturbance observer to obtain the high performance force sensing with a wideband force sensation. In this paper, a multi-sensor data fusion by Kalman-filter algorithm is exploited for velocity estimation which plays the role of an input of the disturbance observer. The combination of multi-sensor-based Kalman-filter and the disturbance observer provides the enhanced force sensing performance and the effective noise reduction. The proposed method is implemented in FPGA with the sampling period of 5 µs. Experimental results confirm the feasibility of the proposed method.
机译:由于其优于力传感器进行测量的优势,因此扰动观测器实现的无传感器力传感已被广泛应用于许多应用中。本文介绍了扰动观测器的发展,以获得具有宽带力感测的高性能力感测。本文利用卡尔曼滤波算法对多传感器数据进行融合,以进行速度估计,该方法起着扰动观测器输入的作用。基于多传感器的卡尔曼滤波器和干扰观测器的组合提供了增强的力感测性能和有效的降噪效果。所提出的方法在FPGA中以5 µs的采样周期实现。实验结果证实了该方法的可行性。

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