Abstract:
In this article, a noise-suppression variable-parameter zeroing neural network (NSVPZNN) is proposed to handle the dynamic Sylvester equation. Differing from the previous...Show MoreMetadata
Abstract:
In this article, a noise-suppression variable-parameter zeroing neural network (NSVPZNN) is proposed to handle the dynamic Sylvester equation. Differing from the previous zeroing neural networks (ZNNs), a new nonlinear activation function and an especially constructed time-variant parameter are developed to construct the novel NSVPZNN model. Therefore, the NSVPZNN model can achieve faster predefined-time convergence without noise disturbance and have stronger robust performance under multiple noises. Furthermore, the convergence upper bound of the NSVPZNN model is theoretically calculated, and a detailed proof of guaranteeing noise-tolerance performance is given. Numerical simulations verify that the NSVPZNN has better performance than the ZNN, the finite-time convergence ZNN model, the predefined-time convergence ZNN model, and the other variable-parameter ZNN when handling the dynamic Sylvester equation. Finally, the design method of the NSVPZNN is applied to the wheeled manipulator for tracking the butterfly trajectory, which further illustrates the model's reliability.
Published in: IEEE Transactions on Industrial Informatics ( Volume: 17, Issue: 11, November 2021)