PSO优化RBF神经网络的MEMS陀螺温度补偿

Temperature Compensation of MEMS Gyroscope Gased on PSO Optimized RBF Neural Network

摘要:

针对微机械（MEMS）陀螺零偏受温度影响较大的问题，提出了一种基于粒子群优化（PSO）算法和径向基函数（RBF）神经网络的陀螺零偏温度补偿方法。通过对温度误差进行了分析和预处理，用粒子群优化算法搜索RBF神经网络的最优配置，再进一步优化RBF神经网络系数。实验结果表明，在-40℃至+60℃温度范围内，采用PSO-RBF神经网络的温度补偿方法使MEMS陀螺仪的零偏的最大误差和标准差减小到0.0034°/s和0.0013°/s，验证了该方法的可行性，对提升陀螺测量精度具有较强的工程应用价值。

Abstract:

Aiming at the problem that MEMS gyroscope bias is greatly affected by temperature, a gyro bias temperature compensation method based on particle swarm optimization (PSO) algorithm and radial basis function (RBF) neural network is proposed. Through the analysis and preprocessing of the temperature error, the particle swarm optimization algorithm is used to search for the optimal configuration of the RBF neural network, and then the RBF neural network coefficients are further optimized. The experimental results show that in the temperature range of -40℃ to +60℃, the temperature compensation method of PSO-RBF neural network can reduce the maximum error and standard deviation of the MEMS gyroscope''s zero bias to 0.0034°/s and 0.0013°/s, the feasibility of the method is verified, and it has strong engineering application value for improving the accuracy of gyroscope measurement.