Temperature error compensation for digital closed-loop fiber optic gyroscope based on RBF neural network
Chunxi Zhang · Optics and Precision Engineering · 2008
A scheme based on Radial Basis Function(RBF) neural networks was designed for temperature error compensation and the scale factor error model and the bias error model were investigated.Based on the temperature error distribution of Fiber Optic Gyroscope(FOG),a scheme combined scale factor error compensation with bias error compensation was designed for temperature error compensation.A separate algorithm based on multiscale analysis was used in the preprocess of modeling data for higher modeling accuracy.Then,the two RBF neural network models were developed and their learning algorithms were improved to avoid over-fitting.Finally,the effects of the models' input vectors on the models' scale were discussed as well.The simulation results indicate that Residual Mean Square(RMS) of the scale factor error model is 0.73(bit/((°)/s))2 and the RMS of the bias error model is 0.051(bit/((°)/s))2.The error models can satisfy the requirements of real-time temperature compensation for mid and high precision FOGs.