基于非线性GA算法的动态P模型的参数辨识与验证

针对现有的GMM-FBG电流传感器的磁滞非线性问题,提出了一种改进的动态Preisach磁滞模型。采用非线性遗传算法对改进动态Preisach磁滞数学模型进行参数辨识,提高了动态磁滞曲线的预测精度。运用改进动态Preisach模型对GMMFBG电流传感器进行建模及实验验证,实验及仿真结果表明该模型具有较好的预测性,预测误差在3.0%以内。经过磁滞补偿使得传感系统电流的测量灵敏度达到0.050 nm/A。

Parameter identification and verification of dynamic P model based on nonlinear genetic algorithm

Aiming at the hysteresis nonlinearity problem of GMM FBG current sensor, an improved dynamic Preisach hysteresis model is proposed. The parameter identification of the improved dynamic hysteresis Preisach model is performed using the nonlinear genetic algorithm, which improves the prediction accuracy of the dynamic hysteresis curve. The improved dynamic Preisach model was used to conduct modeling and experiment verification of the GMM FBG current sensor. Experiment and simulation results show that the proposed model has good prediction performance, the prediction error is within 3.0%; the current sensitivity of the measurement system is up to 0.050nm/A after hysteresis compensation.