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

Abstract

Abstract 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 optimisation 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.

Keywords

• dynamic relaxation
• form‐finding
• noise‐tolerant zeroing neural network
• sequential quadratic programming
• Tensegrity