A comparison between HMLP and HRBF for attitude control
A comparison between HMLP and HRBF for attitude control
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DOI：
10.1109/cca.1998.728321
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发表时间：
1998-09
期刊：
影响因子：
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通讯作者：
L. Fortuna;G. Muscato;M. Xibilia
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文献类型：
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作者：
L. Fortuna;G. Muscato;M. Xibilia
通过引入超复代数中发展的两种新的控制策略，研究了三维空间中航天器等刚体的姿态控制问题。所提出的方法是基于两个并行控制器，它们都是由四元数代数推导出来的。第一种是PD型反馈控制器，第二种是前馈控制器，它可以用超复数多层感知器(HMLP)神经网络实现，也可以用超复数径向基函数(HRBF)神经网络实现。几个仿真结果显示了这两种方法的性能。仿真结果还与经典PD控制器和自适应控制器进行了比较，表明了神经网络的使用对系统性能的改善，特别是当外部扰动作用于刚体时。
The attitude control problem of a rigid body, such as a spacecraft, in three-dimensional space is approached by introducing two new control strategies developed in hypercomplex algebra. The proposed approaches are based on two parallel controllers both derived in quaternion algebra. The first one is a feedback controller of PD type, while the second is a feedforward controller, which is implemented either by means of a hypercomplex multilayer perceptron (HMLP) neural network or by means of a hypercomplex radial basis function (HRBF) neural network. Several simulations show the performance of the two approaches. The results are also compared with a classical PD controller and with an adaptive controller, showing the improvements due to the use of the neural networks, especially when an external disturbance acts on the rigid body.