Voltage overshoot and ring will occur during metal-oxide semiconductor field-effect transistor (MOSFET) turn-off, when it is embedded in circuits with inductive load and operates in pulse width modulation mode. The voltage overshoot and ring not only increase the system voltage stress, but also produce radiated and conducted electromagnetic interference. Based on the analysis of the influence of the gate voltage on the MOSFET turn-off behaviour, this study presents an active closedloop gate voltage control method to mitigate the voltage overshoot and ring. The proposed method senses the instant when the MOSFET drain current starts to fall by comparing the drain voltage with the bus voltage. Once the instant has been detected, a turn-off assistance voltage with proper amplitude and adjustable duration is generated and applied to the gate to mitigate the overshoot and ring. The delay of the gate control loop, including the delay of the comparator, can be compensated by proper choice of the components. Both simulations and experiments results indicate that the MOSFET turnoff voltage overshoot is largely reduced and the ring is almost completely eliminated. At the same time, the turn-off loss and EMI are also minimised.

This paper presents a capacitive sensor-based micro-angle measurement (CSMAM) method that uses an angular-to-linear displacement conversion to achieve high accuracy. The principal and secondary error components of CSMAMs are modeled and analyzed to reveal their impacts on the measurement accuracy. The theoretical accuracies of six types of commonly used CSMAMs are analyzed to determine the optimum configuration of capacitive sensors for 1D and 2D micro-angle measurements. An angular-to-linear displacement conversion method with a linear motional stage and a hemisphere decoupler is used to eliminate the principal error of CSMAM. Experimental results indicate that the optimized CSMAM can achieve accuracies of 0.157 arc sec and 0.052 arc sec in the ranges of ±900 arc sec and ±300 arc sec, respectively, in the case that the effective length of the rotation arm is 100 mm and the linear displacement measurement accuracy of the capacitive sensor is 2 nm. These results can be used as a reference to further improve CSMAM designs and achieve high accuracy in a large measurement range, for use in a wide range of precision engineering applications including angle metrology, micro- and nano-radian angle generators, beam steering mechanisms, and high-performance precision stages.

A high-precision autocollimation method based on multiscale convolution neural network (MSCNN) for angle measurement is proposed. MSCNN is integrated with the traditional measurement model. Using the multiscale representation learning ability of MSCNN, the relationship between spot shape (large-scale feature), gray distribution (small-scale feature), and the influence of aberration and assembly error in the collimating optical path is extracted. The constructed accurate nonlinear measurement model directly improves the uncertainty of angle measurement. Experiments demonstrate that the extended uncertainty reaches 0.29 arcsec (k = 2), approximately 7 times higher than that with the traditional measurement principle, and solves the nonlinear error caused by aberration and assembly error in the autocollimation system. Additionally, this method has a good universality and can be applied to other autocollimation systems.

This study presents a two-dimensional micro-/nanoradian angle generator (2D-MNAG) that achieves high angular displacement resolution and repeatability using a piezo-driven flexure hinge for two-dimensional deflections and three capacitive sensors for output angle monitoring and feedback control. The principal error of the capacitive sensor for precision microangle measurement is analyzed and compensated for; so as to achieve a high angle output resolution of 10 nrad (0.002 arcsec) and positioning repeatability of 120 nrad (0.024 arcsec) over a large angular range of ±4363 μrad (±900 arcsec) for the 2D-MNAG. The impact of each error component, together with the synthetic error of the 2D-MNAG after principal error compensation are determined using Monte Carlo simulation for further improvement of the 2D-MNAG.