Wang Huan, Zeng Qinghua, Zhang Zongyu, Wang Hongfu
School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518107, China.
Micromachines (Basel). 2022 Aug 19;13(8):1351. doi: 10.3390/mi13081351.
A multi-channel pressure scanner is an essential tool for measuring and acquiring various pressure parameters in aerospace applications. It is important to note, however, that the pressure sensor of each of these channels will drift significantly with the increase in the temperature range of the pressure measurement, and the output voltage of each of these channels will show nonlinear characteristics, which will constrain the improvements in the accuracy of the measurement. In the regression fitting process, it is difficult to fit nonlinear data with the traditional least-squares method, which leaves pressure measurement accuracy unsatisfactory. A temperature compensation method based on an improved cuckoo search optimizing a BP neural network for a multi-channel pressure scanner is proposed in this paper to improve pressure measurement accuracy in a wide temperature range. Using the chaotic simplex algorithm, we first improved the cuckoo search algorithm, then optimized the connection weights and thresholds of the BP neural network, and finally constructed an experimental calibration system to investigate the temperature compensation of the multi-channel pressure scanning valves in the -40 °C to 60 °C temperature range. The compensation test results show that the algorithm has a better compensation effect and is more suitable for the temperature compensation of multi-channel pressure scanners than the traditional least-squares method and the standard RBF and BP neural networks. The maximum full-scale error of all 32 channels is 0.02% FS (full-scale error) and below, which realizes its high-accuracy multi-point pressure measurement in a wide temperature range.
多通道压力扫描仪是航空航天应用中测量和获取各种压力参数的重要工具。然而，需要注意的是，这些通道中的每个压力传感器都会随着压力测量温度范围的增加而显著漂移，并且这些通道中的每个通道的输出电压都会呈现非线性特性，这将限制测量精度的提高。在回归拟合过程中，传统的最小二乘法难以拟合非线性数据，导致压力测量精度不尽人意。本文提出了一种基于改进布谷鸟搜索优化BP神经网络的多通道压力扫描仪温度补偿方法，以提高宽温度范围内的压力测量精度。利用混沌单纯形算法，我们首先改进了布谷鸟搜索算法，然后优化了BP神经网络的连接权重和阈值，最后构建了一个实验校准系统，研究了多通道压力扫描阀在-40°C至60°C温度范围内的温度补偿。补偿测试结果表明，该算法具有较好的补偿效果，比传统的最小二乘法以及标准RBF和BP神经网络更适合多通道压力扫描仪的温度补偿。所有32个通道的最大满量程误差为0.02%FS（满量程误差）及以下，实现了其在宽温度范围内的高精度多点压力测量。
