Data-Enabled Stochastic Iterative Shape Control for Assembly of Flexible Structures

Abstract Joining flexible structures such as in the assembly of fuselage sections in an aircraft and on-orbit assembly of modular space structures requires precise shape matching at the joint interface to avoid large local stresses that can lead to joint failure. However, it is difficult to accurately determine the forces needed (using analytical or numerical methods) for correcting the shape variations caused during the manufacturing process. Current assembly approaches use shims to reduce stresses in the presence of shape differences between the structures being joined. In contrast, the main contribution of this work is a data-based approach to experimentally reshape flexible structures to a desired shape. Specifically, data are used to develop predictive models that are then used to iteratively control the shape of those structures using a limited number of fixed actuators. The iterative control method accounts for uncertainties in the data-enabled predictive models and noise from the system to reshape the structure accurately. Experimental results with a relatively large (about 3.3 m long) structure demonstrate that the proposed approach accurately reshapes the structure with a substantial order-of-magnitude reduction (89%) in the maximum shape error, reducing the maximum shape error from 1.19×10−2 m to 1.27×10−3 m over the length of the structure.