Optimization Design of Magnetically Suspended Control and Sensitive Gyroscope Deflection Channel Controller Based on Neural Network Inverse System

Abstract

To meet the strong coupling characteristics of the MSCSG deflection channel and the demand for high control accuracy, a two-degree-of-freedom deflection channel model is firstly established for the structure and working principle of the MSCSG; to meet the strong coupling between the two channels, the inverse system method is used to decouple the model; then, the operation principle of the MSCSG system is introduced, and the modeling of the power amplifier is carried out; to meet the demand for high-precision control of the MSCSG rotor system, the RBF neural network is improved using the fuzzy method to achieve high-precision estimation of the residual coupling terms and deterministic disturbances, and the adaptive sliding mode controller is designed. For the high-precision control of the MSCSG rotor system, the fuzzy method is used to improve the RBF neural network to realize the high-precision estimation of the residual coupling term and uncertain perturbation, and the adaptive sliding mode controller is designed, and the convergence of the controller is proved on the basis of the Lyapunov stability criterion. Simulation analysis shows that the method has a large improvement in decoupling performance and anti-disturbance performance compared with the traditional method, and finally, the experiment verifies the effectiveness of the present method and achieves the optimization of the deflection channel controller. The method can be extended to other magnetic levitation actuators and related fields.

Keywords

• MSCSG
• inverse system decoupling
• fuzzy RBF neural network
• double-loop control