基于PSO优化RBF神经网络的MEMS陀螺温度补偿

MEMS Gyro Temperature Compensation Based on PSO Optimization RBF Neural Network Algorithm

摘要:

针对微电子机械系统(MEMS)陀螺温度变化影响其零偏误差的问题,提出了一种基于粒子群优化(PSO)和径向基函数(RBF)神经网络的陀螺零偏补偿方法。通过RBF神经网络对预处理后的陀螺零偏的温度误差建立模型,用PSO搜索RBF神经网络的最优参数来提高其泛化能力后,将PSO-RBF神经网络最优参数用于补偿陀螺零偏。实验结果证明了该算法的有效性,经PSO-RBF神经网络算法补偿后,MEMS陀螺零偏的最大误差从0.046(°)/s减小到0.0034(°)/s,标准差从0.0427(°)/s减小到 0.0013(°)/s,有效提升了陀螺的零偏稳定性。

Abstract:

Aiming at the problem of zero bias error of MEMS gyroscope caused by temperature change, a zero bias compensation method based on particle swarm optimization (PSO) and radial basis function (RBF) neural network was proposed. RBF neural network was used to build the model of the temperature error of gyro zero bias after pretreatment. After searching the optimal parameters of RBF neural network with PSO to improve its generalization ability, the optimal parameters of PSO-RBF neural network are used to compensate zero bias of gyro. Experimental results demonstrate the effectiveness of the proposed algorithm. After the compensation by PSO-RBF neural network algorithm, the maximum error of MEMS gyroscope zero bias decreases from 0.046(°)/s to 0.0034(°)/s, and the standard deviation decreases from 0.0427(°)/s to 0.0013(°)/s, which can effectively improve the stability of zero bias of MEMS gyroscope.