Temperature drift modeling and compensation of RLG based on PSO tuning SVM

Precision and generalization ability are the two main requirements for modeling the temperature drift of a Ring Laser Gyroscope (RLG). Traditional methods such as the least square fitting and artificial neural network cannot achieve the optimal performance for both aspects. To solve this problem, a novel modeling method based on particle swarm optimization (PSO) tuning support vector machine (SVM) with multiple temperature variables input is proposed. First, the temperature drift data for modeling is preprocessed by adaptive forward linear prediction (FLP) filter. Then, the SVM method is employed to construct the drift model and guarantee the generalization ability. And the PSO algorithm is used to tune the parameters of SVM and improve the precision of established model. The results of experiment validate the superiority of the proposed method in both aspects. The method has been practically applied to a high precision RLG position and orientation system.