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Comparison of shape reconstruction strategies in a complex flexible structure


Current control and performance requirements for large- aperture deployable structures call for precise displacement control, with some tolerances approaching micron levels. Given that strain gages are one of the most economically-deployed sensor architectures, we explored two methods for reconstructing displacement from a distributed strain sensing array. One method linearly maps displacement fields to local strains in a supervised learning mode. After loading the system with sufficient cases, a matrix can be established to approach the approximate displacement-strain relationship. The other method is based on linear regression of generalized basis function projections, typically mode shapes. Results of these two approaches are compared for accuracy, robustness, training time, and real-time feasibility. The second method has higher accuracy due to natural modal behaviors, while the first method is feasible if large amount of training cases and measuring points are available

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