The maglev vibration isolation system exhibits excellent micro-vibration isolation performance (0.01 Hz to 100 Hz band) in the space environment. However, a collision between the base and the floating platform may occur in an ultra-low frequency range (≤0.01 Hz). To avoid collision, the relative position and attitude between the base and the floating platform needs to be accurately tracked and controlled. In this study, a novel measurement method with four groups of two-dimensional position-sensitive detectors equipped with four laser light sources was proposed. A high-precision relative position and attitude measurement model was established based on the geometric relationship of space coordinates. A proportional-differential (PD) fixed-point control algorithm was adopted to realize tracking control. The control performance of the system was evaluated through simulation. Experiments were also carried out to verify the stability of the system and the precision of the control algorithm. A maglev vibration isolation system prototype was constructed and a test system was established. The proposed relative position and attitude measurement model was verified and the six degrees of freedom relative position and attitude response of the system was tested. Based on the measurement model, the tracking control of the system was proven to have high precision.

Tracking Control of a Maglev Vibration Isolation System Based on a High-Precision Relative Position and Attitude Model

In the long-term forecast for the investment demand of power grid, the error of independent variable will result in a large error between forecast result and actual result. In this paper, the correction parameters of factors which have an influence on investment demand of power grid is introduced and the correction model of long-term forecast is built. Firstly, through the correlation analysis and the principle comment analysis (PCA), the variables which the model needs are selected. Secondly, the long-term forecast model based on linear regression is built and the variable coefficient is calculated through least square method. Thirdly, after analyzing historical data of forecast value and actual value, correction model of long-term forecast is built through introducing correction parameters of each variables. Finally, compared with the calculation results which get from correction model and the long-term forecast model, it is proved that the error of correction model is smaller.

https://www.dpi-proceedings.com/index.php/dteees/article/download/34020/32607

A Long-Term Forecast Method for the Investment Demand of Power Grid Based on Linear Regression and Error Correction of Variable

Scanning mirrors appear to be key components in optoelectronic systems for line-of-sight (LOS) stabilization. For improving the angular accuracy of a scanning mirror based on the eddy current displacement sensor measurement, an angular error-correction method is proposed and demonstrated. A mathematic angular error model with physical parameters was developed, and the cross-validation method was employed to determine the reasonable order of the Maclaurin series used in the error model, which increased the exactitude and robustness of the correction method. The error parameters were identified by accurately fitting the calibrated angular errors with the error model, which showed excellent error prediction performance. Based on the angular calculation model corrected by the error model, the closed-loop control system was established to obtain accurate deflection angles. Experimental results show that within the deflection angle of ±1.5 deg, the angular accuracy was improved from 0.28 deg to less than 1.1 arcsec, and the standard deviation for six measurements was less than 1.2 arcsec, which indicates that the angle correction method was effective in improving the linearity of the eddy current sensors and reducing the influence of manufacturing and installation errors.

https://www.mdpi.com/1424-8220/19/2/367/pdf

Kewei Chen

Papers

Improving Angular Accuracy of a Scanning Mirror Based on Error Modeling and Correction.