基于改进QR-PSO算法的压力传感器的动态补偿方法

The Dynamic Compensation Method Based on Improving QR-PSO Algorithm for Pressure Sensor

针对压力传感器在实际使用中动态特性难以满足测试需求这一问题,利用激波管对压力传感器进行动态标定,获取实验样本,依赖样本估计逆模型,提出了基于QR分解和改进粒子群算法构建补偿系统的设计方法。采用QR分解确定模型阶次,降低了简化传感器模型带来的动态补偿运算误差,并结合改进粒子群算法,高效、智能的确定补偿系数。通过实测样本对补偿系统进行重复性验证,结果表明压力传感器的动态响应性能显著地提高了,补偿效果令人满意。

Aiming at the problem that the dynamic characteristics of the pressure sensor can not meet the requirements of the test in practical application,using the shock tube to calibrate the pressure sensor dynamically,obtain the experimental samples,and then estimate the inverse model.A design method of constructing compensation system based on QR decomposition and improving particle swarm optimization（PSO）algorithm.The QR decomposition is used to determine the order of the model,which reduces the dynamic compensation operation error caused by the simplifying the sensor model,combined with the improved particle swarm optimization algorithm,the compensation coefficient is determined efficiently and intelligently.By verifying the repeatability of the compensation system through the measured samples,the experimental results show that the dynamic response of the pressure sensor is improved significantly and the compensation effect is satisfactory.