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Intelligent shape control for precision membrane antennae and reflectors in space

After a brief history of use in space about two decades ago, a resurgence of interest in membrane structures in space is developing, motivated in large part by a great potential for reduced launch mass and stowed volume. Applications for such structures range from planar configurations in solar sails, concentrators and shields, to inflatable lenticulars for radar, radio and optics. Three key factors are paramount for the success and user acceptance of this technology: deployment, longevity and performance. The performance hinges critically on the precision of the membrane surface. The amount of precision is highly mission dependent and may entail one or more of the following issues: surface smoothness, deviation from desired surface profile and slope error. Surface precision is often estimated to be between 1/50 to 1/20 of the wavelength of interest; thus values on the order of a micron (or less) to a millimeter root mean square (RMS) are often presented. It is unlikely that such surface precision can be achieved through purely passive means. This paper addresses the problem of modeling and controlling a class of nonlinear systems that can be considered as highly compliant structures. We consider specifically planar and inflatable membranes, which are represented by complex nonlinear multi-variable models. Boundary perturbations and thermal gradients are demonstrated to be potential actuation schemes for improving the reflector profile. Nonlinear controllers developed to improve performance 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. This research 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.