Control- Relevant Neural Networks for Intelligent Motion Feedforward
Control- Relevant Neural Networks for Intelligent Motion Feedforward
复制标题
用于智能运动前馈的控制相关神经网络
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
T. Oomen
中科院分区：
文献类型：
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作者：
Leontine Aarnoudse;W. Ohnishi;Maurice Poot;P. Tacx;Nard Strijbosch;T. Oomen
神经网络具有很大的运动前馈潜力，因为它们能够近似各种功能。本文的目的是开发一个系统的框架，神经网络的运动前馈的应用，导致智能运动前馈的方法，在这个意义上说，它实现了灵活性，为不同的参考和高性能。迭代学习控制用于生成训练数据，并引入控制相关的性能函数。非因果前馈通过两种网络配置来实现，这两种网络配置分别实现有限和无限预览。该方法在工业平板打印机上进行了实验验证。
Neural networks have large potential for motion feedforward because of their ability to approximate a wide range of functions. The aim of this paper is to develop a systematic framework for application of neural networks to motion feedforward, that leads to an intelligent motion feedforward approach in the sense that it achieves both flexibility for varying references and high performance. Iterative learning control is used to generate training data, and a control-relevant performance function is introduced. Non-causal feedforward is enabled through two network configurations that enable respectively finite and infinite preview. The approach is experimentally validated on an industrial flatbed printer.