| 引用本文: | 耿云海,吴炜平,马玉海.神经网络补偿的挠性卫星敏捷姿态机动[J].哈尔滨工业大学学报,2012,44(5):31.DOI:10.11918/j.issn.0367-6234.2012.05.006 |
| GENG Yun-hai,WU Wei-ping,MA Yu-hai.Neural network compensation of flexible satellite rapid maneuver[J].Journal of Harbin Institute of Technology,2012,44(5):31.DOI:10.11918/j.issn.0367-6234.2012.05.006 |
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| 摘要: |
| 采用单框架控制力矩陀螺( SGCMG)作为主要执行机构，研究了挠性卫星的敏捷机动问题．首先，为
挠性卫星建立带有时变参数及模型不确定性的刚柔耦合动力学模型；配置控制力矩陀螺／飞轮混合执行机构
以满足敏捷卫星快速机动和高精度的任务需求．然后，设计积分型变结构控制律，并利用神经网络补偿器逼
近不确定项，消除卫星挠性耦合特性和模型不确定性对系统的影响，最后，对算法进行了仿真验证．结果表明
所设计的控制系统能够在执行机构不产生奇异的前提下，有效地抑制挠性附件的振动，使卫星在外界干扰及
模型不确定性的影响下，快速达到要求的指向精度和稳定度 |
| 关键词: 挠性敏捷卫星 积分型变结构 振动抑制 神经网络 |
| DOI：10.11918/j.issn.0367-6234.2012.05.006 |
| 分类号:V448.2 |
| 基金项目:国家自然科学基金资助项目( 60904051) |
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| Neural network compensation of flexible satellite rapid maneuver |
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GENG Yun-hai1, WU Wei-ping1,2, MA Yu-hai1
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1.Research Center of Satellite Technology, Harbin Institute of Technology, 150001 Harbin, China;2.R&D Center, China Academy of Launch Vehicle Technology, 100076 Beijing, China
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| Abstract: |
| Employing the single-gimbal control moment gyroscope( SGCMG) as the main actuator, this paper
deals with the rapid maneuver problem of flexible satellite. Firstly, a rigid-flexible coupled dynamics model
with variable parameters and model uncertainties is established for the flexible satellite, and the mixed actuator
consisting of CMG and RW is configured for agile satellite to meet the requirements of rapid maneuver ability
and high precision. Then, a variable structure control law of the integral type is designed with a neural net-
work to estimate the uncertainties and eliminate their effects on the system. Finally, the proposed algorithm is
validated by simulation. Simulation results show that under external disturbances and model uncertainties, the
designed system could suppress vibrations of flexible appendages and achieve the pointing accuracy and high
stable accuracy required rapidly without encountering singularities of the actuator |
| Key words: flexible agile satellite integral variable structure vibration suppression neural network |
