A new open-loop fiber optic gyro error compensation method based on angular velocity error modeling.

Area of Science:

• Instrumentation and Measurement
• Optical Engineering
• Control Systems

Background:

• Open-loop fiber optic gyros (OFOGs) are susceptible to errors, particularly under large dynamic angular velocities.
• Existing models struggle to accurately represent OFOG error characteristics in these conditions.

Purpose of the Study:

• To develop a novel modeling scheme for OFOGs that accurately compensates for errors.
• To improve the precision of OFOGs, especially during high dynamic angular velocity measurements.

Main Methods:

• Extracted angular velocity error (Δω) from OFOG signals.
• Trained a Radial-Basis-Function (RBF) neural network using OFOG output voltage (u), temperature (T), and Δω as learning samples.
• Established a nonlinear mapping model relating T, u, and Δω for automatic error calculation.

Main Results:

• The RBF neural network model effectively compensated for nonlinear OFOG errors.
• Maximum, minimum, and mean square errors of OFOG angular velocity were reduced by over 97%.
• The proposed method demonstrated superior performance compared to previous direct modeling techniques, with further error reductions of 1.4-1.6%.

Conclusions:

• The developed RBF neural network model provides a robust solution for compensating nonlinear OFOG errors.
• This approach significantly enhances the accuracy and reliability of OFOGs in dynamic environments.
• The method offers improved performance over existing error compensation strategies for fiber optic gyros.