Piezoelectric-Integrated Cable-Net Structure for Cable Force Prediction Using a Backpropagation Neural Network

As the primary load-bearing structure of deployable mesh antenna reflectors, the surface accuracy of cable-net structures directly determines the performance of cable-net antennas. To meet surface accuracy requirements, installed cable-net antennas must undergo surface adjustments, making the measurement of cable tension very important. However, constrained by measurement capabilities and conditions, large-scale cable tension measurement is highly challenging. To address this issue, this paper proposes a piezoelectric-integrated cable-net structure. By embedding piezoelectric patches at the nodes of the cable-net structure, the deformation of crimp terminals is converted into voltage signals via the direct piezoelectric effect. Furthermore, a cable force prediction method based on a BP neural network is introduced for piezoelectric-integrated cable-net structures. This method uses piezoelectric voltage values as the input layer and self-stress equilibrium factors of the cable-net as the output layer, thereby reducing the complexity of cable force prediction. Building on this, the influence of the quantity and placement of piezoelectric patches on the accuracy of the cable force prediction model is investigated. The study demonstrates that accurate prediction can be achieved when the number of piezoelectric patches is greater than or equal to the number of self-stress equilibrium factors. Additional piezoelectric patches and asymmetric placement can further enhance the prediction model’s accuracy. Finally, the predictive model was validated in triangular, quadrilateral, and tensegrity cable-net structures, demonstrating the validity of the cable force prediction method based on the backpropagation neural network. This work leverages neural networks to provide a new approach and solution for predicting cable forces in piezoelectric-integrated cable-net structures.