基于逆模型辨识的Wiener型传感器动态补偿研究

Study on dynamic compensation of Wiener sensor based on inverse model identification

在测量系统中许多传感器动态特性是一个非线性Wiener模型,即存在着严重的静态非线性和动态响应滞后。为了补偿动态误差,采用模型参考和Wiener逆模型辨识的算法建立动态补偿单元。补偿单元由一个静态逆模型和动态逆模型构成。通过静态标定方法,采用单输入/单输出的模糊小脑神经网络(SISO-FCMAC)建立传感器静态非线性模型,再由SISO-FCMAC的逆运算建立静态逆模型。动态逆模型是一个IIR滤波器,其系数通过模型参考的系统辨识方法得到。该补偿算法具有学习简单、收敛速度快、函数逼近精度高等特点。通过实验验证了该算法的有效性。

In measurement systems, many sensors＇ dynamics charaetenstic is a nonlinear Wiener model, and have serious static nonlinear and dynamic response hysteresis. In order to compensate the error, a novel algorithm uses model reference and Wiener inversion model identification, is proposed for compensator modeling. The compensator consists of static inverse model and dynamic inverse model. The static nonlinear model of the sensor is built on a single-input-single-output fuzzy cerebllar model articulation controller （SISO-FCMAC）, and the static inverse model is established by inverse operation of SISO-FCMAC. The dynanmic inverse model is a IIR filter, and its coefficient is obtained through the system identification based on model reference. The algorithm has characteristics of simple study, quick convergence and high precision of function approximation, etc. Experiments show that the algorithm is effective.