Optimization Design of Magnetically Suspended Control and Sensitive Gyroscope Deflection Channel Controller Based on Neural Network Inverse System
Abstract
:1. Introduction
2. MSCSG System Principles and Modeling
2.1. MSCSG Structure and Principle
2.2. Sensor Principle
2.3. Power Amplifier Principle Analysis and Modeling
3. MSCSG Deflection Channel Decoupling
4. Controller Design
5. Simulation Analysis
5.1. Simulation Analysis of Decoupling Capability
5.2. Anti-Interference Capability Analysis
6. Experimental Verification
6.1. Experimental Verification of Decoupling Performance
6.2. Verification of Anti-Interference Performance
7. Conclusions
- For the structure and working principle of MSCSG, establish a two-degree-of-freedom deflection channel model, and the equations show that there is a serious coupling between the two deflection channels; model and analyse the power amplifier, and equate it to an inertial link. Design the inverse system decoupling method, and use the fuzzy method to improve the RBF neural network to compensate the uncertain perturbation and residual coupling term; design the adaptive sliding mode controller, and based on the Lyapunov stability criterion, the controller is proved to converge.
- Simulation analysis shows that the method designed in this paper, relative to the traditional method, has a large improvement in decoupling effect and anti-jamming performance; the validity of this method is subsequently verified by experiments.
- The main principle of the fuzzy RBF neural network designed in this paper is to use the abstraction ability of the fuzzy method to the natural language to correct the sliding mode controller and improve the dynamic quality; in the actual experiments, the rules of the fuzzy method are set up by ourselves according to the accumulation of experience in the previous many experiments, which may not be the optimal rules, but the introduction of the learning ability of the RBF neural network to correct the fuzzy control’s subordinate degree function and fuzzy rules, which solves the subjectivity caused by fuzzy control relying too much on the empirical basis. In brief, it means that the fuzzy rules can be gradually narrowed down by correcting the fuzzy rules one by one while keeping the parameters of the RBF neural network unchanged; on the contrary, after optimising the rule intervals, the parameters of the RBF neural network can be corrected again until the experimental results are better.
- In the future, the fuzzy rules and RBF neural network can be derived in the more underlying logic, and strive to introduce adaptive methods into the two, parameter optimisation and rule correction on the form of indicators to reflect the use of optimisation methods to find the optimal parameters and rules.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Parameters | Value |
---|---|
Jx/(kg·m2) | 0.0097 |
Jz/(kg·m2) | 0.0287 |
Jy/(kg·m2) | 0.0097 |
L/m | 0.1158 |
B/(T) | 0.35 |
D/(mm) | 12 |
Parameters | Value |
---|---|
η | 0.3 |
α | 0.5 |
γ | 10 |
η | 0.1 |
ci | [−2 −1 0 1 2] |
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Chen, F.; Wang, W.; Yu, C.; Wang, S.; Zhang, W. Optimization Design of Magnetically Suspended Control and Sensitive Gyroscope Deflection Channel Controller Based on Neural Network Inverse System. Actuators 2024, 13, 302. https://doi.org/10.3390/act13080302
Chen F, Wang W, Yu C, Wang S, Zhang W. Optimization Design of Magnetically Suspended Control and Sensitive Gyroscope Deflection Channel Controller Based on Neural Network Inverse System. Actuators. 2024; 13(8):302. https://doi.org/10.3390/act13080302
Chicago/Turabian StyleChen, Feiyu, Weijie Wang, Chunmiao Yu, Shengjun Wang, and Weian Zhang. 2024. "Optimization Design of Magnetically Suspended Control and Sensitive Gyroscope Deflection Channel Controller Based on Neural Network Inverse System" Actuators 13, no. 8: 302. https://doi.org/10.3390/act13080302