A resurgence of interest in membrane structures in space is developing after a brief period of activity about two decades ago. This renewal is motivated in large part by the great potential for reduced launch mass and stowed volume that membrane structures can afford. Applications for such structures range from planar configurations in solar sails, concentrators, and shields, to inflatable lenticulars for radar, radio, and optical purposes. Three key factors are paramount for the success and user acceptance of this technology: deployment, longevity, and performance. Performance hinges critically on the precision of the membrane surface.
Nonlinear controllers developed to improve performance of such systems are often dependent on state estimation and parameter identification procedures. The existence of these procedures, within the control strategy, increases the size of the algorithms, limiting the system performance in real-time. The research presented has as a main objective to create an intelligent controller, based on feedback error learning, which is capable of extracting performance information from precision large membrane deployables, and subsequently using this information to achieve maximum surface precision.
This paper addresses the problem of modeling and controlling a class of nonlinear systems that can be considered as highly compliant structures, specifically planar and inflatable membranes, which are represented by complex nonlinear multi-variable models. Methods of noncontact local state estimation are considered in order to provide feedback on the membrane shape, which would then be coupled with a mathematical model of boundary perturbation and/or thermal effects for control efforts.