The dynamic relaxation form finding method aided with advanced recurrent neural network  

作　　者：Liming Zhao Zhongbo Sun Keping Liu Jiliang Zhang 

机构地区：[1]Department of Mechanical and Electrical Engineering,Changchun University of Technology,Changchun,China [2]Department of Control Engineering,Changchun University of Technology,Changchun,China [3]Department of Electronic and Electrical Engineering,The University of Sheffield,Sheffield,England

出　　处：《CAAI Transactions on Intelligence Technology》2023年第3期635-644,共10页智能技术学报（英文）

基　　金：supported in part by the National Natural Science Foundation of China under grants 61873304,62173048,62106023;in part by the China Postdoctoral Science Foundation Funded Project under grants 2018M641784 and 2019T120240;also in part by the Key Science and Technology Projects of Jilin Province,China,under grant 20210201106GX;also in part by the Changchun Science and Technology Project under grant 21ZY41.

摘　　要：How to establish a self‐equilibrium configuration is vital for further kinematics and dynamics analyses of tensegrity mechanism.In this study,for investigating tensegrity form‐finding problems,a concise and efficient dynamic relaxation‐noise tolerant zeroing neural network(DR‐NTZNN)form‐finding algorithm is established through analysing the physical properties of tensegrity structures.In addition,the non‐linear constrained opti-misation problem which transformed from the form‐finding problem is solved by a sequential quadratic programming algorithm.Moreover,the noise may produce in the form‐finding process that includes the round‐off errors which are brought by the approximate matrix and restart point calculating course,disturbance caused by external force and manufacturing error when constructing a tensegrity structure.Hence,for the purpose of suppressing the noise,a noise tolerant zeroing neural network is presented to solve the search direction,which can endow the anti‐noise capability to the form‐finding model and enhance the calculation capability.Besides,the dynamic relaxation method is contributed to seek the nodal coordinates rapidly when the search direction is acquired.The numerical results show the form‐finding model has a huge capability for high‐dimensional free form cable‐strut mechanisms with complicated topology.Eventually,comparing with other existing form‐finding methods,the contrast simulations reveal the excellent anti‐noise performance and calculation capacity of DR‐NTZNN form‐finding algorithm.

关 键 词：dynamic relaxation form‐finding noise‐tolerant zeroing neural network sequential quadratic programming TENSEGRITY 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]