@article{Yang2026, 
author = {Yi Yang and Xuchen Wang and Richard M. Voyles and Xin Ma},
title = {A Predefined-Time Convergent and Noise-Tolerant Zeroing Neural Network Model for Time Variant Quadratic Programming with Application to Robot Motion Planning},
year = {2026},
journal = {Tsinghua Science and Technology},
volume = {31},
number = {3},
pages = {1610-1621},
keywords = {noise tolerance, Time-Variant Quadratic Programming (TVQP), robotic motion planning, Predefined-Time Convergent and Noise-Tolerant Fractional-Order Zeroing Neural Network (PTC-NT-FOZNN)},
url = {https://www.sciopen.com/article/10.26599/TST.2024.9010202},
doi = {10.26599/TST.2024.9010202},
abstract = {This paper develops a Predefined-Time Convergent and Noise-Tolerant Fractional-Order Zeroing Neural Network (PTC-NT-FOZNN) model, innovatively engineered to tackle Time-Variant Quadratic Programming (TVQP) challenges. The PTC-NT-FOZNN, stemming from a novel iteration within the variable-gain Zeroing Neural Network (ZNN) spectrum, known as FOZNNs, features diminishing gains over time and marries noise resistance with predefined-time convergence, making it ideal for energy-efficient robotic motion planning tasks. The PTC-NT-FOZNN enhances traditional ZNN models by incorporating a newly developed activation function that promotes optimal convergence irrespective of the model’s order. When evaluated against six established ZNNs, the PTC-NT-FOZNN, with parameter  0<α⩽1, demonstrates enhanced positional precision and resilience to additive noises, making it exceptionally suitable for TVQP tasks. Thorough practical assessments, including simulations and experiments using a Flexiv Rizon robotic arm, confirm the PTC-NT-FOZNN’s capabilities in achieving precise tracking and high computational efficiency, thereby proving its effectiveness for robust kinematic control applications.}
}