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An advanced form-finding of tensegrity structures aided with noise-tolerant zeroing neural network

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Abstract

A high-efficiency form-finding algorithm is crucially important for finding a stabilized tensegrity structure. In this paper, a modified Broyden-Fletcher-Goldfarb-Shanno noise-tolerant zeroing neural network (MBFGS-NTZNN) form-finding approach is developed and investigated for the form-finding problems of tensegrity systems. A modified BFGS algorithm (MBFGS) is employed to solve the irreversibility of the Hessian matrix, which could avoid the non-positive definite circumstance of the stiffness matrix. Additionally, the approach could be utilized to make a reduction in algorithm calculation complexity. Moreover, to find a group of suitable nodal coordinates, a zeroing neural network (ZNN) based NTZNN is considered to suppress the noise, which may include rounding errors and external disturbance during the form-finding process. Besides, the 0-stable and global convergence under the pollution of noise are verified. Eventually, numerical simulations and an application example are conducted to ascertain the superiority and availability of the MBFGS-NTZNN algorithm in the fields of form-finding.

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Authors and Affiliations

  1. Department of Control Engineering, Changchun University of Technology, Changchun, 130012, China

    Zhongbo Sun, Liming Zhao & Keping Liu

  2. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

    Long Jin

  3. State Key Laboratory for Turbulence and Complex Systems, Department of Mechanics and Engineering Science, BIC-ESAT, College of Engineering, Peking University, Beijing, 100871, China

    Junzhi Yu

  4. Center for Robotics and Neural Systems, Plymouth University, Plymouth, PL48AA, UK

    Chunxu Li

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  1. Zhongbo Sun
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  2. Liming Zhao
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  3. Keping Liu
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  4. Long Jin
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  5. Junzhi Yu
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  6. Chunxu Li
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Correspondence to Keping Liu.

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The work is supported in part by the National Natural Science Foundation of China under Grants 61873304, in part by the China Postdoctoral Science Foundation Funded Project under Grant 2018M641784 and 2019T120240, and also in part by the Key Science and Technology Projects of Jilin Province, China, Grant Nos. 20200201291JC.

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Sun, Z., Zhao, L., Liu, K. et al. An advanced form-finding of tensegrity structures aided with noise-tolerant zeroing neural network. Neural Comput & Applic 34, 6053–6066 (2022). https://doi.org/10.1007/s00521-021-06745-6

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  • DOI: https://doi.org/10.1007/s00521-021-06745-6

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