Performance enhancement of active structures during service lives

This thesis describes a successful application of advanced computational methods to tasks in the field of active structural control. The control-task involves finding good control movements for a highly coupled, non-linear structure. It is demonstrated how these methods improve the accuracy of the analytical model. Also, stochastic search techniques are compared for the same task. Furthermore, the performance of the system can be enhanced during service life by storing, retrieving and adapting good solutions. The structure studied, a Tensegrity, is a special type of cable structure. Tensegrities stimulate the imagination of artists, researchers and engineers. Varying the amount of selftress changes structural shape as well as the load-bearing capacity. They offer unique applications, as deployable structures in the context of aerospace applications and more generally, as actively controlled structures. However, the non-linear behavior of tensegrities is difficult to model. Aspects of this work involve subjects such as tensegrity structures, active structural control, search algorithms and artificial intelligence. The focus of this thesis is on the last two subjects. This work demonstrates how advanced computing techniques can be used in order to increase solution quality. A hybrid approach, employing neural networks, increases the accuracy of the analytical model that is employed for simulating tensegrity structures. A comparison of three stochastic search techniques shows that computational time, first estimated to take centuries when adapting a "brute-force" approach, can be reduced to hours. Case-based reasoning (CBR) is used for a further tenfold decrease in computation time. The time needed to find good control solutions decreased from hours, when stochastic search is used, to minutes with CBR. CBR also provides possibilities for improving performance over service life. Successfully solved situations are stored as cases in a case-base. In new situations, a case close to the new situation is retrieved and then adapted. By storing additional cases, the system is able to retrieve better cases for adaptation. With increasing case-base size, adaptation time decreases. The combination of these techniques has much potential for improving the performance of complex structures during service lives. Results should contribute to the development of innovative structural solutions. Finally, it is expected that the findings in this thesis will become points of departure for subsequent studies.