随机学习使 "无模型 "ILC 更快收敛：应用于工业印刷中的 MIMO 前馈

IF 3.9 4区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Adaptive Control and Signal Processing Pub Date : 2024-09-02 DOI:10.1002/acs.3903

Leontine Aarnoudse, Tom Oomen

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引用次数: 0

摘要

无模型迭代学习控制（ILC）可以通过在真实系统上进行专门实验来取代传统模型，从而完全减弱重复干扰，实现高性能。本文旨在为 MIMO ILC 开发一种快速数据驱动方法，该方法采用高效无偏梯度估计形式的随机学习。这是通过开发一种随机共轭梯度算法来实现的，其中搜索方向和最佳步长都是通过专门的实验产生的。该方法被应用于 MIMO 自动前馈调整。仿真和实验结果表明，该方法优于早期的随机和确定性方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Random Learning Leads to Faster Convergence in ‘Model‐Free’ ILC: With Application to MIMO Feedforward in Industrial Printing

Model‐free iterative learning control (ILC) can lead to high performance by attenuating repeating disturbances completely, using dedicated experiments on the real system to replace the traditional model. The aim of this paper is to develop a fast data‐driven method for MIMO ILC that uses random learning in the form of efficient unbiased gradient estimates. This is achieved by developing a stochastic conjugate gradient algorithm, in which the search direction and optimal step size are generated using dedicated experiments. The approach is applied to MIMO automated feedforward tuning. Simulation and experimental results show that the method is superior to earlier stochastic and deterministic methods.

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来源期刊

International Journal of Adaptive Control and Signal Processing 工程技术-工程：电子与电气

CiteScore

5.30

自引率

16.10%

发文量

163

审稿时长

5 months

期刊介绍： The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material. Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include: Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers Nonlinear, Robust and Intelligent Adaptive Controllers Linear and Nonlinear Multivariable System Identification and Estimation Identification of Linear Parameter Varying, Distributed and Hybrid Systems Multiple Model Adaptive Control Adaptive Signal processing Theory and Algorithms Adaptation in Multi-Agent Systems Condition Monitoring Systems Fault Detection and Isolation Methods Fault Detection and Isolation Methods Fault-Tolerant Control (system supervision and diagnosis) Learning Systems and Adaptive Modelling Real Time Algorithms for Adaptive Signal Processing and Control Adaptive Signal Processing and Control Applications Adaptive Cloud Architectures and Networking Adaptive Mechanisms for Internet of Things Adaptive Sliding Mode Control.