一种磁性液体加速度传感器模型的参数辨识方法

Parameter Identification Method for Ferrofluid Acceleration Sensor Model

针对磁性液体加速度传感器模型的参数辨识问题,提出一种改进混沌粒子群算法。该算法利用改进的logistic映射对粒子群进行混沌初始化,并采用抛物线规律对迭代过程中的惯性权重进行实时调节,算法最终辨识出了传感器中磁芯吸附磁性液体后的等效长度、等效半径及等效相对磁导率。实验结果表明,该方法辨识出的参数相对误差小于0.85%,且辨识曲线与参考曲线的拟合度在94.37%以上。

Aiming at the parameter identification of ferrofluid acceleration sensor model,an improved chaotic particle swarm optimization algorithm is proposed. The algorithm uses the improved logistic mapping to initialize the particle swarm chaos,and adopts the parabolic law to adjust the inertia weight in the iterative process in real time. The algorithm finally identifies the equivalent length,the equivalent radius and the equivalent relative permeability when the magnetic core adsorbed ferrofluids in the sensor. The experimental results show that the relative error of the parameters identified by this method is less than 0.85%,and the fitting degree between identification curve and the reference curve is above 94.37%.