Adaptive control of a deployable tensegrity structure

Deployable structures belong to a special class of moveable structures that are capable of form and size change. Controlling movement of deployable structures is important for successful deployment, in-service adaptation and safety. In this paper, measurements and control methodologies contribute to the development of an efficient learning strategy and a damage-compensation algorithm for a deployable tensegrity structure. The general motivation of this work is to develop an efficient bio-inspired control framework through real-time measurement, adaptation, and learning. Building on previous work, an enhanced deployment algorithm involves re-use of control commands in order to reduce computation time for mid-span connection. Simulations are integrated into a stochastic search algorithm and combined with case-reuse as well as real-time measurements. Although data collection requires instrumentation, this methodology performs significantly better than without real-time measurements. This paper presents the procedure and generally applicable methodologies to improve deployment paths, to control the shape of a structure through optimization, and to control the structure to adapt after a damage event.