新型神经网络可快速精准求解机器人运动控制中的时变优化问题。
A strictly predefined-time convergent and anti-noise fractional-order zeroing neural network for solving time-variant quadratic programming in kinematic robot control
- 采用分数阶导数与新型激活函数，实现预设时间收敛且抗噪声。
- 在两种机械臂仿真和真实机器人实验中，定位精度优于现有方法。
- 适合对响应速度和鲁棒性要求高的机器人实时控制场景。
本文提出一种严格预设时间收敛且抗噪声的分数阶零化神经网络（SPTC-AN-FOZNN），专门用于解决时变二次规划（TVQP）问题。该模型是首个同时具备严格预设时间收敛性和抗噪声能力的变增益零化神经网络，专为机器人运动控制设计。通过遵循莱布尼茨法则的容度分数阶导数，提升了传统零化神经网络的性能；并引入新激活函数，确保收敛性不依赖于模型阶次。与五种最新递归神经网络相比，当 $0<α≤1$ 时，SPTC-AN-FOZNN 在应对加性噪声下表现出更优的位置精度和鲁棒性。大量实验包括两种机械臂仿真及 Flexiv Rizon 机器人实测，验证了其在精确轨迹跟踪与计算效率方面的有效性，证明其适用于高可靠性运动控制。
原文摘要 · Abstract (English)
This paper proposes a strictly predefined-time convergent and anti-noise fractional-order zeroing neural network (SPTC-AN-FOZNN) model, meticulously designed for addressing time-variant quadratic programming (TVQP) problems. This model marks the first variable-gain ZNN to collectively manifest strictly predefined-time convergence and noise resilience, specifically tailored for kinematic motion control of robots. The SPTC-AN-FOZNN advances traditional ZNNs by incorporating a conformable fractional derivative in accordance with the Leibniz rule, a compliance not commonly achieved by other fractional derivative definitions. It also features a novel activation function designed to ensure favorable convergence independent of the model's order. When compared to five recently published recurrent neural networks (RNNs), the SPTC-AN-FOZNN, configured with $0<α\leq 1$, exhibits superior positional accuracy and robustness against additive noises for TVQP applications. Extensive empirical evaluations, including simulations with two types of robotic manipulators and experiments with a Flexiv Rizon robot, have validated the SPTC-AN-FOZNN's effectiveness in precise tracking and computational efficiency, establishing its utility for robust kinematic control.
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