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 ecient learning strategy and a damage-compensation algorithm for a deployable tensegrity structure. The general motivation of this work is to develop an ecient 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 signicantly 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.