Dynamic Compensation of Pressure Sensors Based on Brain Storm Optimization Algorithm and Its Application in Shock Wave Testing

Abstract

In the process of shock wave testing, pressure sensor will generate dynamic error by the lack of dynamic characteristics. In order to solve this problem, a pressure sensor dynamic compensation method which is based on Modified Brain Storm Optimization (MBSO) algorithm is proposed. There are two improvements of the MBSO algorithm to the Brain Storm Optimization (BSO), one of them is to adjust the parameters of population selection strategy, and another is to change the way in which individuals fuse to generate new individuals. Both improvements not only can improve the convergence speed, but also improve the accuracy of the algorithm. In order to obtain the sensor dynamic compensation transfer model, the shock tube test data is used as input data, and the standard step data is used as output data. The model parameters are calculated by the MBSO algorithm, and the model is verified by the measured shock wave data. The experimental results show that the overshoot of the shock tube calibration data is reduced from 54.7% to 5.6% and the rise time is increased to 12μs. The pressure sensor dynamic compensation system obtained by MBSO optimization has obvious inhibitory effect on the sensor resonance frequency, which can improve the dynamic response characteristics of the sensor and reduce the influence of dynamic error.