Traditional temperature error model of ring laser gyroscope (RLG) based on neural network faces great problems in the repeatability performance under different temperature conditions. To reduce the temperature error and improve the generalization ability of traditional neural network, a novel error model based on radial basis function neural network optimized by particle swarm optimization (PSO) and regularization approach was proposed. The temperature error is analyzed and preprocessed. The PSO method is used to search the optimal configuration of the network, and regularization method is used as the evaluation criterion to further optimize the coefficients of the network. The experimental results show that the proposed method can improve the precision and environmental adaptability of RLG, which had been practically applied in RLG position and orientation system.

In order to improve the precision of Strapdown Inertial Navigation System (SINS) and reduce the complexity of the traditional calibration method, a novel calibration and compensation scheme is proposed. An optimization calibration method with four-direction rotations is designed to calculate all error coefficients of Ring Laser Gyroscope (RLG) SINS in a series of constant temperatures. According to the actual working environment, the temperature errors of RLG SINS are compensated by a nonlinear interpolation compensation algorithm. The experimental results show that the inertial navigation errors of the proposed method are reduced.

This paper bring forward a kind of pendulum micromechanical angular accelerometer with force feedback, which might be proposed by us, first. The sensor adopts a pendulum with mass joined to the anchors by a pair torsion-spring beams as sensing device, differential-capacitor device for detecting the angular acceleration about the X axis, an electrostatic force device for feedback loop. The sensor's structure and basic operating principle were discussed. The theoretical relation of angular acceleration and output of sensor was derived in detail based on the geometrical and dynamical models. The simulation result shows that the scale factor of sensor is 0.388917 V / r / s and verifies the accuracy of the theories model. It can be used in robust motor control of industrial robots and aerocraft.