In this paper, a novel force-sensing method is proposed for a high-performance force control system based on friction-free and noise-free force observation. A periodic signal is inserted into the control system for friction reduction. A combination of a high-order disturbance observer (DOB) (HDOB) and a Kalman filter is constructed to perform the force sensing. The HDOB is designed to estimate the force and reject oscillatory disturbances in the estimation. The force-sensing bandwidth is improved through effective noise suppression by the Kalman filter. Additionally, this paper proposes the application of the HDOB to the bilateral control system of a different master-slave mechanism. All the control algorithms are implemented in a field-programmable gate array with a high sampling rate that also enables the widening of the bandwidth of the force control system. The effectiveness of the proposed method is verified by experimental results.

FPGA-Based High-Performance Force Control System With Friction-Free and Noise-Free Force Observation

FPGA-based Wideband Force Control System with Friction-Free and Noise-Free Force Observation

In this work research results of dynamic data processing with adaptive Kalman filter are presented. In filtering process parameters of filter are change on results of difference between measuring and expected coordinate. Modeling results of adaptive Kalman filtering are comparing with classic Kalman filter. Modeling results using adaptive Kalman filtering for coordinate estimation shows that estimation error decrease in points when parameters of movement change very rapidly. Results of adaptive Kalman filter modeling and filter use for GPS receiver information processing are described. Ill. 11, bibl. 4 (in English; abstracts in English and Lithuanian). http://dx.doi.org/10.5755/j01.eee.111.5.351

/pdf/dynamic-data-processing-with-kaiman-filter-pien6tkwrz.pdf

Dynamic Data Processing with Kaiman Filter

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.

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

This study aimed to develop a software to enable recognition of moving objects and detection of their orbits by processing the images obtained through a camera. The secondary objective was to design a GPS-based mobile robot which tracks moving objects.

A GPS-based system design for the recognition and tracking of moving targets

In this paper, a novel adaptive filtering algorithm using real-time deviation of velocity estimation is presented for maneuvering target tracking. This new algorithm will be called adaptive filtering algorithm of velocity estimation. A number of simulation results indicate that the algorithm not only has good performance on tracking maneuvering target, but also greatly improves the capacity for tracking non-maneuvering target. So it is worthy to be applied widely in practice.

An adaptive filtering algorithm of velocity estimation

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An adaptive filtering algorithm of velocity estimation