Design of MSCSG control system based on ADRC and RBF neural network

被引：0

作者：

Li, Lei ^{[1
]}

Ren, Yuan ^{[2
]}

Chen, Xiaocen ^{[1
]}

Yin, Zengyuan ^{[1
]}

机构：

[1] Graduate School, Aerospace Engineering University, Beijing,101416, China

[2] Department of Aerospace Science and Technology, Aerospace Engineering University, Beijing,101416, China

来源：

Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2020年 / 46卷 / 10期

关键词：

Disturbance rejection - Gyroscopes - Suspensions (components) - Radial basis function networks - Robustness (control systems) - Magnetism - Adaptive control systems - Controllers - Functions;

D O I：

10.13700/j.bh.1001-5965.2019.0536

中图分类号：

学科分类号：

摘要：

In order to overcome the influence of external disturbance mutation on the suspension stability of magnetic suspension rotor and the output torque precision of Magnetic Suspension Control Sensitive Gyro (MSCSG), a MSCSG radial deflection control method based on the combination of Auto Disturbance Rejection Controller (ADRC) and Radial Basis Function (RBF) neural network is proposed. The influence of ADRC parameters on the control effect of MSCSG is clarified. By optimizing the design of ADRC and combining RBF neural network with ADRC, the real-time debugging of controller parameters can be realized so as to overcome the impact of external disturbance mutation. It is proved by simulation that compared with single ADRC control, this method not only improves the accuracy of decoupling control, but also improves the response speed and robustness of the system to external disturbances and parameter changes. It can be applied to the MSCSG with high precision, fast response and strong robustness control. © 2020, Editorial Board of JBUAA. All right reserved.

引用

页码：1966 / 1972

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