Joint Friction during Deployment of a Near-Full-Scale Tensegrity Footbridge

Most deployable structures, such as operable roofs and masts, move over one degree of freedom. This paper describes a structure that involves loosely coupled movement over several degrees of freedom. Analysis models of these structures are typically inaccurate. A source of inaccuracy is joint friction. Static and kinetic friction are studied experimentally and analytically. Simulations have been modified to account for these effects, and two methods are used to quantify friction effects. Friction has a significant effect on the movement of the tensegrity structure. Of two candidate parameters, cable tension and interior cable angle, cable angle is the factor that best characterizes friction effects. Values of static and kinetic friction coefficients are not significantly different in this context, and this leads to a reduction in the complexity of the friction model for simulation. Including friction effects in analysis decreases the difference between simulations and tests. Lastly, strut elements of the tensegrity structure are most critically affected by friction. (C) 2017 American Society of Civil Engineers

Biomimetic adaptive control of a deployable tensegrity structure

Biomimetic behavior includes aspects such as learning from previous experience, self-diagnosis, and adaptation. This thesis describes control methodologies that are essential to development towards biomimetic behavior of a complex deployable structure. Simulations of the structure are improved from previous work to include friction effects of cables sliding over joints. Despite improved simulations, testing shows that significant uncertainties in the behavior of the structure remain. Therefore, control is not effective with predetermined actuation movements. Special methodologies for feedback control using simulations and measurements are required. A near-full-scale deployable tensegrity structure is used to test methodologies. Active control methods are proposed for deployment, midspan connection of both halves of the structure, and self-stress. This thesis presents a method using feedback to compare measurements and simulations to modify control commands. Since collision of elements is possible in the folded state and produce undesirable bending stresses, a path-planning algorithm is implemented for the first stage of deployment. Error in nodal positions at midspan is successfully reduced through the use of the path-planning method and deployment time is significantly reduced compared with previous work. Lastly, algorithms for self-stress, involving penalty and rejection constraints on element stress, are useful for correcting nodal positions after deployment. Damage is detected in this thesis using vibration measurements. The method uses dynamic behavior of the structure to determine whether or not the structure is damaged. Using parameters of the structure and a set of candidate locations for the damaged element, candidates are successively excluded until few candidates remain, successfully including the true location of damage. Adaptation and learning are demonstrated by mitigation methods after damage and in-service loading (such as pedestrians). Active control is useful to manipulate the shape of the tensegrity structure to reduce the member stresses and vertical downwards displacement caused by a damaged element. Though the response improves the condition of the structure to respect the serviceability limit for vertical downwards displacement, the tensegrity structure cannot be fully restored to the design configuration. Since correction of end-node coordinates can be grouped by the direction of correction and resulting cable-length changes, case-based reasoning is useful to reduce time of execution and to reduce unnecessary cable-length changes. Single pedestrian and crowd loading configuration is applied analytically and experimentally to the tensegrity structure. Application of mitigation techniques is useful beyond serviceability thresholds for a moving load used to simulate in-service loading. The research question of this thesis: "Is it feasible for a deployable tensegrity structure to improve movement and service performance through behavior biomimetics?". The answer is yes. This work presents methods inspired by those observed in nature for efficient movement that is generalizable for future deployable structures

Element location and classification following a damage event of a near-full-scale deployable tensegrity structure

Current infrastructure is designed and built such that it must resistall possible loads. This leads to overdesigned structures that are inefficient in terms energy and cost. A structure that can self-identify damage, adapt, and learn for future events results from research intothe emerging field of intelligent infrastructure and structural health monitoring.Two halves of a “hollow-rope” tensegrity structure deploy from supports to join at midspan by controlling the length of active cables on each half of the structure. These active cables are continuous throughthe length of the half-structure, guided by intermediary jointswhere cables slide. Springs along the circumference of the structure facilitate deployment due to increasing the diameter of the structure when folding and subsequent decreasing during deployment. Although previous work has addressed damage locationand mitigationof ruptured cableswhen the cables are the load-critical elements of the structure, this work hasnot studiedthe classification of the type of element that is damaged, element locationand damage mitigation.This paper presents work on element classification, detection,and location of damaged elements in a deployable tensegrity footbridge. The footbridge isstudied through monitoring dynamic behavior. Displacement and strain values are measured before, during, and after cable breakage. Natural frequencies inhealthy and damaged states are compared. Free-vibration dynamic behavior of the tensegrity structure are characterized for two situations,deployment and in-service. Examination of ambient vibrations for the half structure and forced vibrations for the full structure successfully led to detection of ruptured cables. Correlation methods using strain measurements also successfullydetect and locate a ruptured cable. Detection of abuckled strut and aruptured cableis successful by observing differences of natural frequencies between healthy and damaged states. Location of a damaged element is successful using nodal-position measurements through excluding possible damage scenarios and using strain measurements to identify elements of significant changes in eigenvector coefficients using principal component analysis. Therefore, excluding scenarios from a population for damageidentification is effective for highly-coupled structures that are capable of large shape changes. These methods reveal the potential for damage identification of complex sensed structures.Classification and location of a damaged element on a complex near-full-scale structure is successful using nodal position measurements through excluding possible damage cases and using strain measurements to identify elements of significant changes in eigenvector coefficients using principal component analysis. Implementing error-domain model falsification to exclude possible scenarios for location of damaged elements successfully reduced the number of probable casesof damage location. Paterns of influence from damaged cables and struts are useful to classify the type of element that is damaged. Therefore, the methodology involving error-domain model falsification (EDMF) for damage location is useful for closely-coupled structures that are capable of large shape changes

Damage mitigation of a near-full-scale deployable tensegrity structure through behavior biomimetics

Current infrastructure is designed and built such that it must simultaneously comply with all possible loads. This leads to overdesigned structures that are inefficient in terms energy and cost. A structure that can self-identify damage, adapt, and learn for future events addresses the emerging field of intelligent infrastructure through inspiration from biology. The required material and embodied energy of structural elements is reduced while maintaining structural integrity with a small increase in operational energy for active control. Tensegrity structures are cable-strut systems held in equilibrium due to self-stress. There is potential for damage tolerance when they are kinematically redundant. This paper presents active control algorithms applied for damage mitigation and service loading of the tensegrity structure. Active control using a path-planning RRT*-connect algorithm and soft-constraint algorithm changes the shape of the tensegrity structure to reduce member stresses and to restrict vertical (downward) displacement caused by a damaged element. Though the effect improves the configuration of the structure, it cannot be fully restored to the design configuration. Effectiveness of the RRT*-connect and soft-constraint algorithms for the half structure depends on the relative change of nodal positions and feasibility to bring the structure back within serviceability limits. Since correction of end-node coordinates can be grouped according to the direction vector to mitigate the configuration of the structure and resulting cable-length changes, case-based reasoning (CBR) is useful to reduce time of execution and to avoid unnecessary cable-length changes. Mitigation techniques are successfully demonstrated for serviceability thresholds for a uniformly distributed load

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 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