基于ADRC和RBF神经网络的MSCSG控制系统设计

李磊; 任元; 陈晓岑; 尹增愿

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

doi: 10.13700/j.bh.1001-5965.2019.0536

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

基金项目:

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

详细信息

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

通讯作者:
任元, E-mail: renyuan_823@aliyun.com

中图分类号: V448.2
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- 被引次数: 0
出版历程
- 收稿日期: 2019-10-09
- 录用日期: 2019-11-15
- 网络出版日期: 2020-10-20

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

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

Funds:

Aerospace Equipment Advance Research Project 305080506

Top Young Talents Program of Beijing High-Tech Innovation Program 2017000026833ZK23

More Information

Corresponding author: REN Yuan, E-mail: renyuan_823@aliyun.com

摘要

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。
- 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.
- Magnetic Suspension Control Sensitive Gyro (MSCSG) /
- Auto Disturbance Rejection Controller (ADRC) /
- adaptive control /
- Radial Basis Function (RBF) neural network /
- robust control

HTML全文

图 1 MSCSG定子及径向磁轴承结构

Figure 1. Stator and radial magnetic bearing structure of MSCSG

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图 2 MSCSG转子及轴向磁轴承结构

Figure 2. Rotor and axial magnetic bearing structure of MSCSG

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图 3 自抗扰控制器工作原理^[10]

Figure 3. Working principle of auto disturbance rejection controller^[10]

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图 4 二通道磁悬浮转子自抗扰控制系统

Figure 4. Auto disturbance rejection control system for two-channel magnetic suspension rotor

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图 5 RBF神经网络结构^[16]

Figure 5. Structure of RBF neural network^[16]

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图 6 基于RBF神经网络的自抗扰控制器

Figure 6. Auto disturbance rejection controller based on RBF neural network

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图 7 对突变阶跃扰动的抗扰性能对比

Figure 7. Comparison of anti-interference performance of step disturbance to mutations

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图 8 对突变正弦扰动的抗扰性能对比

Figure 8. Comparison of anti-interference performance of sinusoidal disturbance to mutations

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图 9 对突变随机扰动的抗扰性能对比

Figure 9. Comparison of anti-interference performance of random disturbances to mutations

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表 1 系统仿真参数

Table 1. System simulation parameters

参数	数值
J_X/(kg·m²)	0.009 7
J_Z/(kg·m²)	0.028 7
J_Y/(kg·m²)	0.009 7
l_m/m	0.115 8
N	200
k_p	1 000
R	200
L_p	0.005
F_r/Hz	30
B/T	0.4
TES	400
k_d	100
M	10 000
HID	20

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