| 引用本文: | 胡庆雷,肖冰,马广富.输入受限的航天器姿态调节小波滑模反步控制[J].哈尔滨工业大学学报,2010,42(5):678.DOI:10.11918/j.issn.0367-6234.2010.05.002 |
| HU Qing-lei,XIAO Bing,MA Guang-fu.Wavelet based backstepping sliding mode control for spacecraft attitude regulation under control input constraint[J].Journal of Harbin Institute of Technology,2010,42(5):678.DOI:10.11918/j.issn.0367-6234.2010.05.002 |
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| 摘要: |
| 对刚体卫星在轨机动时存在未知惯量特性、外部干扰及控制输入受限的控制问题,提出了一种将滑模反步姿态控制与小波相结合的鲁棒控制方法,这种方法在继承传统滑模反步控制的优点的同时,利用小波函数的逼近能力来补偿执行机构饱和非线性;对设计者而言,饱和非线性的结构特性无需了解,并基于Lya-punov方法从理论上证明了滑动模态的存在性及系统的全局稳定性.最后,将该方法应用于航天器的姿态调节控制,仿真结果表明此控制器能有效地处理航天器姿态调节过程中控制输入饱和受限的约束,在完成姿态调节控制的同时,具有良好的过渡过程品质. |
| 关键词: 姿态调节 反步法 滑模控制 小波神经网络 |
| DOI：10.11918/j.issn.0367-6234.2010.05.002 |
| 分类号:V448.22 |
| 基金项目:国家自然科学基金(60774062);高等学校博士学科点专项科研基金(20070213061);中央高校基本科研业务费专项资金(HIT.NSRIF.2009003);黑龙江省留学回国人员科学基金(LC08C01);哈尔滨市留学回国基金(2010RFLXG001) |
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| Wavelet based backstepping sliding mode control for spacecraft attitude regulation under control input constraint |
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HU Qing-lei, XIAO Bing, MA Guang-fu
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Dept.of Control Science and Engineering,Harbin Institute of Technology,Harbin 150001,China
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| Abstract: |
| In this paper,a wavelet based backstepping sliding mode controller for rotational maneuver is proposed for an orbiting three-axis stabilized spacecraft,taking the actuator saturation into account.Wavelet networks,which have superior learning capability in comparison to conventional neural network,are used for approximation of unknown system dynamics.This proposed controller comprises of a wavelet based saturation compensator and a robust backstepping sliding mode controller,and the latter one is designed to achieve the desired performance by attenuating the effect of approximation error caused by wavelet identifier.Lyapunov stability analysis shows that the resulting closed-loop system is globally asymptotically stable.To study the effectiveness of the corresponding control scheme,traditional methods are also developed for the control system.Both analytical and numerical results are presented to show the theoretical and practical merit of this approach. |
| Key words: attitude regulation backstepping sliding mode control wavelet neural network |
