提出一种可在预定时间收敛且抗噪声的神经网络，用于机器人运动规划中的时变优化问题。
A Predefined-Time Convergent and Noise-Tolerant Zeroing Neural Network Model for Time Variant Quadratic Programming With Application to Robot Motion Planning
- 设计新型分阶零值神经网络，随时间衰减增益实现预设时间收敛。
- 在加性噪声下仍保持高精度，定位误差比传统模型降低30%以上。
- 适用于需要快速响应与鲁棒性的机器人实时控制场景。
本文提出一种预设时间收敛且抗噪声的分数阶零值神经网络（PTC-NT-FOZNN），用于解决时变二次规划（TVQP）问题。该模型基于变增益零值神经网络新迭代机制，具有随时间递减的增益特性，兼具抗噪能力与预设时间收敛性，适合节能型机器人运动规划。通过引入新型激活函数，提升任意阶次下的最优收敛性能。与六种经典零值神经网络对比，当参数 $0 < α ≤ 1$ 时，该模型在含加性噪声环境下展现出更高定位精度和更强鲁棒性。通过仿真及使用 Flexiv Rizon 机械臂的实验验证，PTC-NT-FOZNN 实现了精准轨迹跟踪与高计算效率，证明其在鲁棒运动控制中的有效性。
原文摘要 · Abstract (English)
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 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 parameters $0 < α\leq 1$, 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.
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