Abstract:Most active structures involve the direct control of single parameters when there is a closed form relationship between required response and the control parameter. Building on a previous study of an adjustable structure, this paper describes geometric active control of a reusable tensegrity structure that has been enlarged to five modules with improved connections and equipped with actuators. Closely coupled strut and cable elements behave non-linearly (geometrically) even for small movements of the ten telescopic struts. The control criterion of maintaining upper surface slope has no closed form relationship with strut movements. The behavior of the structure is studied under 25 load cases. A newly developed stochastic search algorithm successfully identifies good control commands following computation times of up to one hour. Sequential application of the commands through sets of partial commands helps avoid exceeding limits during intermediate stages and adds robustness to the system. Reuse of a previously calculated command reduces the response time to less than one minute. Feasible storage and reuse of such commands confirm the potential for improving performance during service.

A tensegrity is a lightweight space structure consisting of compression members surrounded by a network of tension members. They can be easily dismantled and therefore provide innovative possibilities for reusable and modular structures. Tensegrities can adapt their shape by changing their self stress, and when equipped with sensors and actuators, they can adapt to changing environments. A full-scale prototype of an adjustable tensegrity has been built and tested at Swiss Federal Institute of Technology (EPFL). This paper begins with a description of important aspects of the design, assembly and static testing. Tests show that the structure behaves linearly when subjected to vertical loads applied to a single joint. Non-linearities are detected for small displacements-for loads applied to several joints and for adjusting combinations of telescoping compression members. To predict behavior, dynamic relaxation-a non-linear method-has been found to be reliable. Appropriate strut adjustments found by a stochastic search algorithm are identified for the control goal of constant roof slope and for the load conditions studied. When adjusting struts, an excessive number of adjustable members does not necessarily lead to improved performance.

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Structural analyses of tensegrity structures must account for geometrical nonlinearity. The dynamic relaxation method correctly models static behavior in most situations. However, the requirements for precision increase when these structures are actively controlled. This paper describes the use of neural networks to improve the accuracy of the dynamic relaxation method in order to correspond more closely to data measured from a full-scale laboratory structure. An additional investigation evaluates training the network during the service life for further increases in accuracy. Tests showed that artificial neural networks increased model accuracy when used with the dynamic relaxation method. Replacing the dynamic relaxation method completely by a neural network did not provide satisfactory results. First tests involving training the neural network online showed potential to adapt the model to changes during the service life of the structure.

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