Precision Temperature Control for the Laser Gyro Inertial Navigation System in Long-Endurance Marine Navigation

Abstract

In the Ring Laser Gyro Inertial Navigation System (RLG INS), the temperature characteristics of the accelerometer can directly influence the measurement results. In order to improve navigation accuracy in long-endurance marine navigation, the operating temperature of the accelerometer should be precisely controlled. Based on thermal studies on the accelerometer, temperature control precision should be better than 0.01 °C to achieve 1 × 10−5 m/s2 output accuracy of the accelerometer. However, this conclusion is obtained by approximate calculations and cannot be directly applied to different inertial navigation systems. In order to verify this thermal conclusion and broaden its application, the Back Propagation Neural Network (BP-NN) algorithm is adopted to validate the feasibility of temperature control in this paper. In addition, a multi-level temperature control system is also set up and carefully designed to support the validation and experiments under different conditions. Test results of the temperature control system prove that operating temperature variation can be reduced to 0.01 °C. Meanwhile, the standard deviation per hundred seconds of the accelerometer outputs, after temperature control, reaches 1 × 10−5 m/s2. Static altitude and navigation results were improved by 41.97% and 62.91%, respectively, with the precision temperature control system, which meets the long-endurance marine navigation requirements.

A temperature control algorithm for lithography machine based on generalized predictive control and BP neural network PI control

“…5 Generally, the problem of large inertial constant, time delays, and multiple disturbances are the main challenges faced by temperature control system. The combination of control algorithms, such as fuzzy control, 6,7 genetic algorithm, 8 neural network control, 9,10 feedforward control, 11,12 and Smith predictor 13 with PID control algorithms, is a common used approach at present. Moreover, the integration of intelligent control strategies, such as fuzzy algorithms 14 and iterative learning control, 15 with advanced techniques like predictive control, holds significant promise in addressing challenges encountered in industrial control systems like time delays 16 and external disturbances.…”