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RBFNN-Based Sliding Mode Control for Flexible Spacecraft Vibration Suppress

机译:基于RBFNN的滑模控制用于挠性航天器的振动抑制

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Vibration suppress of a flexible spacecraft during attitude maneuvering is a challenging task. In this paper, the proposed control includes two major parts: command profile is designed to maneuver a flexible spacecraft with very little residual vibration and an intelligent control scheme consists of an online radial basis function neural network(RBFNN) based slide mode control is performed such that improved tracking accuracy is accomplished in the presence of parametric uncertainties. The input shape is designed to modify the command for elimination of the residual vibration in the open loop, and an RBF neural network based slide mode control law is proposed to achieve system stability and good performance in the closed-loop. With the proposed control law, simulation results verified that the control scheme can achieve the maneuver with high accuracy of little residual vibration.
机译:在姿态操纵过程中抑制挠性航天器的振动是一项艰巨的任务。在本文中,所提出的控制包括两个主要部分:指令配置文件用于操纵具有很小残余振动的挠性航天器,并且执行基于在线径向基函数神经网络(RBFNN)的滑模控制的智能控制方案。在存在参数不确定性的情况下,可以提高跟踪精度。设计输入形状以修改用于消除开环中残余振动的命令,并提出了一种基于RBF神经网络的滑模控制律,以实现闭环中的系统稳定性和良好的性能。利用所提出的控制律,仿真结果验证了该控制方案能够以很小的残留振动的高精度实现机动。

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