n this paper 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 feed-forward controller, which is implemented either by means of an Hypercomplex MultiLayer Perceptron (HMLP) neural network or by means of an 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.

A comparison between HMLP and HRBF Neural Networks for Attitude Control

XIBILIA, Maria Gabriella
1998

Abstract

n this paper 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 feed-forward controller, which is implemented either by means of an Hypercomplex MultiLayer Perceptron (HMLP) neural network or by means of an 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11570/6569
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