Abstract
In this paper, the form-finding of symmetric tensegrity structure is discussed and investigated by means of force density method and neural network algorithm. An optimization model based on the rank deficiency maximization is established to solve feasible force density, and the nodal coordinates are determined by eigenvalue decomposition of force density matrix. Then, the Lagrangian multiplier method can transform constrained optimization problem into unconstrained optimization problem. To avoid high-dimensional Hessian matrix calculation, noise-tolerant neural algorithm (NTNA) is exploited to calculate the unconstrained optimization problem. Numerical results indicate that the proposed noise-tolerant neural-based quasi-Newton Broyden–Fletcher–Goldfarb–Shanno (NTN-QNBFGS) form-finding method enhances convergence speed. Furthermore, the consistency between the form-finding results and analytical solutions infers that the developed method can be effectively applied to the form-finding of tensegrity robot.








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The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
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Funding
The work is supported in part by the National Natural Science Foundation of China under Grant Nos 61873304, 62173048, 62106023 and in part by the China Postdoctoral Science Foundation-Funded Project under Grant Nos 2018M641784 and 2019T120240, and also in part by the Key Science and Technology Projects of Jilin Province, China, under Grant No. 20200404208YY, and also in part by the Changchun Science and Technology Project under Grant No. 21ZY41, and also in part by Beijing Natural Science Foundation under Grant No. 2022MQ05.
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Sun, Z., Heng, T., Zhao, L. et al. A novel form-finding method via noise-tolerant neurodynamic model for symmetric tensegrity structure. Neural Comput & Applic 35, 6813–6830 (2023). https://doi.org/10.1007/s00521-022-08039-x
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DOI: https://doi.org/10.1007/s00521-022-08039-x