IDENTIFICATION AND CONTROL OF LARGE STRUCTURES USING NEURAL NETWORKS

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.