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一个严格预定义的时间收和反噪声分数顺序零化神经网络,用于解决动力机器人控制中的时间变量二次编程

Yi Yang¹, Xiao Li², Xuchen Wang¹

¹Multi-Scale Medical Robotics Center, The Chinese University of Hong Kong, 999077, Hong Kong, China; Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, 999077, Hong Kong, China.

Neural networks : the official journal of the International Neural Network Society

|February 26, 2025

概括

一个新的小数级神经网络提供了快速,精确的机器人控制. 这种严格预定义的时间融合和反噪声模型在时间变量二次编程任务中表现出色,即使有噪声干扰.

科学领域:

机器人技术机器人技术机器人技术
控制理论控制理论
计算神经科学是一种神经科学.

背景情况:

传统的归零神经网络 (ZNN) 在复杂的控制任务中面临着对聚合时间和噪声强度的限制.
时间变量二次编程 (TVQP) 对于实时机器人动力运动控制至关重要.
现有的分数顺序ZNN模型往往缺乏保证的严格预定义时间融合或全面的防噪能力.

研究的目的:

引入一种新的严格预定义时间收和反噪声分数顺序归零神经网络 (SPTC-AN-FOZNN) 模型.
解决机器人运动控制中的时间变量二次编程 (TVQP) 的挑战.
开发一个可变增益的ZNN,增强融合速度和噪声弹性.

主要方法:

使用莱布尼茨规则开发一个分数顺序的ZNN,其中包含一个符合的分数导数.
引入一种新的激活功能,确保顺序独立的趋同.
对TVQP的五个最先进的循环神经网络 (RNN) 的比较分析.

主要成果:

与现有的RNN相比,SPTC-AN-FOZNN显示出更高的位置精度和对抗添加噪声的稳定性.
对机器人操纵器和Flexiv Rizon机器人的实证评估证实了精确的跟踪能力.
该模型在运动控制应用中实现了高效的计算和可靠的性能.

结论:

SPTC-AN-FOZNN是第一个可变增益ZNN,可以实现TVQP的严格预定义时间融合和噪声弹性.
拟议的模型在速度,准确性和机器人动力控制的稳定性方面提供了显著的优势.
SPTC-AN-FOZNN为现实世界机器人应用提供了计算高效和有效的解决方案.

关键词:

分数顺序归零神经网络的神经网络.运动控制的动力控制.抗噪声能力抗噪声能力严格预定义的时间收.时间变量二次编程.

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