Temperature compensation method of laser gyroscope based on PSO-BP neural network

After the laser strapdown inertial navigation system(SINS) is powered on and started, the bias of the laser gyro will undergo a process of dynamic change to stability with the temperature change, which will affect the application accuracy of the SINS. Therefore, a temperature compensation method for laser gyro based on particle swarm optimization-back propagation (PSO-BP) neural network is proposed. PSO is used to find the optimal weights and thresholds of the neural network model. Temperature and temperature gradient are used as independent variables to establish a network of gyro zero-bias compensation model. Temperature test results within the working temperature range of the laser SINS show that compared with the traditional BP neural network algorithm, the training speed of the proposed PSO-BP neural network model is improved by 4 times. The model fitting accuracy is higher. The problem that the BP algorithm is easy to fall into the local optimal solution is avoided. After compensation by PSO-BP algorithm, the gyro bias stability is improved by 60%, which further verifies the validity of the model. © 2022, Editorial Department of Journal of Chinese Inertial Technology. All right reserved.