A Nonlinear Calibration Method Based on Sinusoidal Excitation and DFT Transformation for High-Precision Power Analyzers

For most high-precision power analyzers, the measurement accuracy may be affected due to the nonlinear relationship between the input and output signal. Therefore, calibration before measurement is important to ensure accuracy. However, the traditional calibration methods usually have complicated structures, cumbersome calibration process, and difficult selection of calibration points, which is not suitable for situations with many measurement points. To solve these issues, a nonlinear calibration method based on sinusoidal excitation and DFT transformation is proposed in this paper. By obtaining the effective value data of the current sinusoidal excitation from the calibration source, the accurate calibration process can be done, and the calibration efficiency can be improved effectively. Firstly, through Fourier transform, the phase value at the initial moment of the fundamental frequency is calculated. Then, the mapping relationship between the sampling value and the theoretical calculation value is established according to the obtained theoretical discrete expression, and a cubic spline interpolation method is used to further reduce the calibration error. Simulations and experiments show that the calibration method presented in this paper achieves high calibration accuracy, and the results are compensation value after calibration with a deviation of ± 3 × 10 − 4 .

The calibration of force offset for rocket engine based on deep belief network

Background Force offset is an important movement and control parameter in rocket motor development process, and its accurate measurement is a vital guarantee of rocket motor reliable operation, so there is an essential significance to achieve accurate force offset calibration. Methods A novel force offset nonlinear calibration method is proposed based on deep belief network. Experimental platform is established and force offset calibration test is completed. Because the Levenberg -Marquardt process has the advantage of both Newton method and gradient descent method, test data are trained with Levenberg -Marquardt, decreasing nonlinear mapping convergence errors and realizing nonlinear calibration of force offset. Results and Conclusions Training results show that the mean deviation rate of force offset after nonlinear calibration is less than 2.7%, better than the back-propagation neural network and least squares method, verifying the reasonableness and practicality of nonlinear compensation calibration method and effectively improving force offset calibration accuracy.