Adaptive control of space structures via recurrent neural networks

The paper presents a study on the adaptive control of large space structures, based on the use of real time recurrent neural networks. The controller relies on two interconnected neural networks. One is used for system identification and works in parallel with the real structure. The other performs the actual control task, and feeds back onto the structure and the identification net. The two networks are trained on line, with their synaptic weights adapting to time Varying system configurations. A series of numerical examples on the model of a large structure provides the basis for understanding the performances and robustness of the method proposed, and indicates the feasibility of real on line applications.