Kejun Xu

Development of Coriolis mass flowmeter with digital drive and signal processing technology.

Coriolis mass flowmeter (CMF) often suffers from two-phase flowrate which may cause flowtube stalling. To solve this problem, a digital drive method and a digital signal processing method of CMF is studied and implemented in this paper. A positive-negative step signal is used to initiate the flowtube oscillation without knowing the natural frequency of the flowtube. A digital zero-crossing detection method based on Lagrange interpolation is adopted to calculate the frequency and phase difference of the sensor output signals in order to synthesize the digital drive signal. The digital drive approach is implemented by a multiplying digital to analog converter (MDAC) and a direct digital synthesizer (DDS). A digital Coriolis mass flow transmitter is developed with a digital signal processor (DSP) to control the digital drive, and realize the signal processing. Water flow calibrations and gas-liquid two-phase flowrate experiments are conducted to examine the performance of the transmitter. The experimental results show that the transmitter shortens the start-up time and can maintain the oscillation of flowtube in two-phase flowrate condition.

Measurement of wrist force/torque using data fusion technique

This paper proposes a kind of measurement method of wrist force/torque for robots. The method adopts the data fusion technique according to the output variations of the finger force sensors installed in the gripper. The finger force sensors are used to measure the clamping force of the gripper in the design. When the accuracy of measurement is not required exactly and there is the limitation of weight and volume in space robots, we utilize the existing devices to estimate the wrist force/torque without the wrist force/torque sensor, which not only meets the practical requirement but also decreases the weight and cost of robots. An experimental bench is developed and the calibration experiments are conducted to detect the relationship between the wrist force/torque and finger forces. The experimental data are used to train a radial basis function (RBF) artificial neural network, and the construction and parameters of the network are obtained. The results of data fusion of the wrist force/torque are consistent with the practical calibration values, and the effectiveness of the wrist force/torque estimating technique is proved.