Deployment process control of space masts via iterative learning control

The paper presents the initial study on the deployment control of the deployable tube-boom mast, a novel design concept of large space masts. The work first analyzes the deploying dynamics of the mast structure and identifies the issues of the free deployment in structural mechanics, which motivates the application of active process control. A data-driven iterative learning controller (ILC) is then proposed to function as an offline control signal trainer so that the well-trained deploying process could be able to track the desired smooth and slow trajectory and reduce the deployment-induced vibrations. To improve the computation efficiency, a sliding window method is incorporated in ILC to shorten the time period of trails for the control signal training iterations. Numerical investigation is carried out on a single boom of the deployable mast, showing the satisfactory trajectory tracking performance and strong capability for low-frequency dynamics attenuation. As the first application of ILC methods in the field of deployable structures, such results demonstrate that the proposed method is feasible to reform the deploying process of the deployable mast structure with enhanced structural stability and reliability.