基于ADRC和RBF神经网络的MSCSG控制系统设计

doi:10.13700/j.bh.1001-5965.2019.0536

北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (10): 1966-1972.doi: 10.13700/j.bh.1001-5965.2019.0536

基于ADRC和RBF神经网络的MSCSG控制系统设计

李磊¹, 任元², 陈晓岑¹, 尹增愿¹

1. 航天工程大学研究生院, 北京 101416;
2. 航天工程大学宇航科学与技术系, 北京 101416

收稿日期:2019-10-09 发布日期:2020-10-29
通讯作者: 任元 E-mail:renyuan_823@aliyun.com
作者简介:李磊男,硕士研究生。主要研究方向:航天器导航与控制技术;任元男,博士,副教授,博士生导师。主要研究方向:新型磁悬浮惯性机构和量子精密测量技术;陈晓岑女,博士研究生。主要研究方向:网络化滤波、航天器姿态控制;尹增愿男,博士研究生。主要研究方向:航天器导航与控制技术。
基金资助:
航天装备预先研究项目（305080506）；北京市“高创计划”青年人才拔尖项目（2017000026833ZK23）

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

LI Lei¹, REN Yuan², CHEN Xiaocen¹, YIN Zengyuan¹

1. Graduate School, Aerospace Engineering University, Beijing 101416, China;
2. Department of Aerospace Science and Technology, Aerospace Engineering University, Beijing 101416, China

Received:2019-10-09 Published:2020-10-29
Supported by:
Aerospace Equipment Advance Research Project (305080506); Top Young Talents Program of Beijing High-Tech Innovation Program (2017000026833ZK23)

摘要/Abstract

摘要： 为了克服外部扰动突变对磁悬浮转子悬浮稳定度和磁悬浮控制敏感陀螺（MSCSG）输出力矩精度的影响，提出了一种基于自抗扰控制器（ADRC）和径向基函数（RBF）神经网络相结合的MSCSG径向偏转控制方法。阐明了ADRC参数对MSCSG控制效果的影响，通过优化设计ADRC，并将RBF神经网络和ADRC结合运用，实现对控制器参数的实时调试，从而克服外界扰动突变的影响。仿真证明所提方法相较于单ADRC控制，不仅改善了解耦控制精度，而且提高了系统对外部扰动和参数变化的响应速度和鲁棒性，可应用于MSCSG的高精度、快响应、强鲁棒控制。

关键词: 磁悬浮控制敏感陀螺(MSCSG), 自抗扰控制器(ADRC), 自适应控制, 径向基函数(RBF)神经网络, 鲁棒控制

Abstract: 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.

Key words: Magnetic Suspension Control Sensitive Gyro (MSCSG), Auto Disturbance Rejection Controller (ADRC), adaptive control, Radial Basis Function (RBF) neural network, robust control

中图分类号:

V448.2

李磊, 任元, 陈晓岑, 尹增愿. 基于ADRC和RBF神经网络的MSCSG控制系统设计[J]. 北京航空航天大学学报, 2020, 46(10): 1966-1972.

LI Lei, REN Yuan, CHEN Xiaocen, YIN Zengyuan. Design of MSCSG control system based on ADRC and RBF neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(10): 1966-1972.

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基于ADRC和RBF神经网络的MSCSG控制系统设计

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

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