Control system design for large space structures, possessing nonlinear
dynamics which are often time-varying and likely ill-modeled, present
s great challenges for all currently advocated methodologies. The purs
uits of an autonomous control system for such nonlinear structures hav
e led to the use of artificial neural networks. In the present paper,
we propose the use of radial basis function networks as a learning con
troller to achieve vibration suppression and trajectory maneuvering. T
he ability of connectionist systems to approximate arbitrary continuou
s functions provides an efficient means of modeling, identification an
d control of complex systems. Based on the model reference adaptive co
ntrol architecture, a neural controller learns to function as a closed
-loop compensator and to force the dynamics of the nonlinear plant to
match a given reference model. This paper addresses the theoretical fo
undation of the architecture and demonstrates its applicability via se
veral examples.