A unifying framework for form-finding and topology-finding of tensegrity structures. (15th April 2021) - Wang, Yafeng; Xu, Xian; Luo, Yaozhi
Highlights: A unifying framework for form-finding and topology-finding of tensegrity structures. The computational framework is based on rank-constrained linear matrix inequalities. A Newton-like algorithm is employed to solve the rank-constrained LMIs efficiently. Tensegrity structures with single and with multiple self-stress state(s) can be handled. Symmetric/regular and non-symmetric/irregular tensegrity structures can be handled. Abstract: This paper presents a unifying framework for the form-finding and topology-finding of tensegrity structures. The novel computational framework is based on rank-constrained linear matrix inequalities. For form-finding, given the topology (i.e., member connectivities), the determination of the member force densities is formulated into a linear matrix inequality (LMI) problem with a constraint on the rank of the force density matrix. The positive semi-definiteness and rank deficiency condition of the force density matrix are well managed by the rank-constrained LMI-based formulation. A Newton-like algorithm is employed to solve the rank-constrained LMI problem. Two methods, named direct method and indirect method, are proposed to determine the nodal coordinates once the force densities have been obtained. For topology-finding, given the geometry (i.e., nodal coordinates), the determination of the topology is also formulated into an LMI problem with a constraint on the rank of the tangent stiffness matrix. Numerical examples demonstrate that different types of form-finding problems (such as tensegrity structures with single and with multiple self-stress states, symmetric and irregular tensegrity structures) can be uniformly and efficiently solved by the proposed approach. Furthermore, three well-known tensegrity structures are reproduced to verify the effectiveness of the proposed formulation on the topology-finding of tensegrity structures.