A COOPERATING NEURAL APPROACH FOR SPACECRAFTS ATTITUDE-CONTROL

A locally recurrent neural network is described as a key component of a control system able to rule an artificial satellite whose attitude m ust be kept close to zero-angle with respect to an inertial reference system earth centred. The main idea is to join a simple linear adaptiv e controller with a neural network trained to compensate the inadequac y 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 s uch co-operation, the resulting system is easily trainable and perform s efficiently. In fact, the whole system acts as a MRAC controller who se accuracy has been tested on numerical simulations of an Olympus cla ss spacecraft, Considerations on stability, reactions to unexpected so licitations, extension to non-geocentric missions and power consumptio n are included as well.