A comparison between HMLP and HRBF for attitude control

IEEE Transactions on Neural Networks
L FortunaM G Xibilia

Abstract

In this paper the problem of controlling the attitude 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 is a feedback controller of the proportional derivative (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 obtained by using neural networks, especially when an external disturbance acts on the rigid body. In particular the HMLP network gave better results when considering trajectories not presented during the learning phase.

References

Aug 1, 1969·The American Journal of Psychiatry·H Ford
Mar 1, 1997·Neural Networks : the Official Journal of the International Neural Network Society·M G. XibiliaP Arena
Jan 1, 1991·IEEE Transactions on Neural Networks·S ChenP M Grant

Citations

Mar 1, 1997·Neural Networks : the Official Journal of the International Neural Network Society·M G. XibiliaP Arena
May 9, 2014·IEEE Transactions on Neural Networks and Learning Systems·Cyrus Jahanchahi, Danilo P Mandic
Jun 19, 2015·IEEE Transactions on Neural Networks and Learning Systems·Dongpo XuDanilo P Mandic
Sep 12, 2015·Royal Society Open Science·Dongpo XuDanilo P Mandic

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