A co-operating neural approach for spacecrafts attitude control

[摘要] A locally recurrent neural network is described as a key component of a control system able to rule an artificial satellite whose attitude must be kept close to zero-angle with respect to an inertial reference system earth centred. The main idea is to join a simple linear adaptive controller with a neural network trained to compensate the inadequacy of the former. The control signal is the sum of the signal computed by the two devices; the feedback for training the neural network comes from the attitude error w.r.t. a reference trajectory and is computed by means of a linear inversion of the satellite dynamics. Thanks to such co-operation, the resulting system is easily trainable and performs efficiently. In fact, the whole system acts as a MRAC controller whose accuracy has been tested on numerical simulations of an Olympus class spacecraft, Considerations on stability, reactions to unexpected solicitations, extension to non-geocentric missions and power consumption are included as well.

[发布日期] 1997-09-15

[关键词] recurrent neural network;neural controller;co-operative control;attitude orbit control systems