A novel approach for the temperature prediction of ring laser gyroscope using teamwork optimization enabled bias-compensated long short-term memory

Abstract

Ring laser gyro (RLG) has turned out to be the predominant rotation rate sensor used in high-precision strap-down inertial navigation systems (SINS), extensively employed in the aerospace field nowadays. However, in actual space environments, temperature variation within the device leads to an uneven RLG bias error which results in performance degradation of SINS. To resolve this issue, diverse compensation methods have been performed carefully with comprehensive analysis to enhance environmental adaptability. Moreover, a bias of RLG changes with temperature in a nonlinear manner, which is a significant restraining factor to enhance the accuracy of RLG. In this paper, a novel method is developed using a deep learning model to predict the temperature model for the sensor. Initially, sensor data are pre-processed by min–max normalization and then, it is subjected to feature selection using correlation. The temperature prediction is carried out using bias-compensated long short-term memory (BC–LSTM) after selecting significant features, and the teamwork optimization algorithm (TOA) is used as the training algorithm to compensate for the bias error that occurs due to nonlinearity in the data. Moreover, the classification of sensors has been done using the proposed TOA-LSTM to identify the best sensors for critical space applications. The performance of proposed model is analysed using evaluation metrics such as accuracy, True Positive rate (TPR), True Negative rate (TNR), False Positive rate (FPR), mean squared error (MSE), mean absolute error (MAE) and root-mean-squared error (RMSE). The experimental investigation states that the proposed approach attains accuracy, TPR, TNR, and FPR with values of 0.960, 0.966, 0.949 and 0.0511, respectively.