数据驱动的振动控制:综述

IF 15.3 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-08-15 DOI:10.1109/JAS.2024.124431
Weiyi Yang;Shuai Li;Xin Luo
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引用次数: 0

摘要

随着传感器网络和数据采集技术在制造、航空和医疗保健等各种系统中的不断进步,数据驱动振动控制(DDVC)引起了工业界和学术界的广泛兴趣。输入整形(IS)作为一种简单有效的前馈方法,在 DDVC 方法中需求量很大。它无需参数动力学和闭环系统结构,就能将所需输入指令与脉冲序列相融合,从而单独抑制残余振动。在对最先进的 DDVC 方法进行深入研究的基础上,本研究做了以下工作:1) 介绍了 IS 理论和典型的输入整形器;2) 对 DDVC 方法的最新进展进行了分类;3) 总结了 DDVC 普遍采用的指标;以及 4) 讨论了 DDVC 的工程应用和未来趋势。通过这些研究,本研究从设计到优化的角度对现有的 DDVC 方法进行了系统而全面的概述,旨在促进未来对这一新兴重要问题的研究。
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Data Driven Vibration Control: A Review
With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing, aviation, and healthcare, the data driven vibration control (DDVC) has attracted broad interests from both the industrial and academic communities. Input shaping (IS), as a simple and effective feedforward method, is greatly demanded in DDVC methods. It convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure, thereby suppressing the residual vibration separately. Based on a thorough investigation into the state-of-the-art DDVC methods, this survey has made the following efforts: 1) Introducing the IS theory and typical input shapers; 2) Categorizing recent progress of DDVC methods; 3) Summarizing commonly adopted metrics for DDVC; and 4) Discussing the engineering applications and future trends of DDVC. By doing so, this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives, aiming at promoting future research regarding this emerging and vital issue.
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来源期刊
Ieee-Caa Journal of Automatica Sinica
Ieee-Caa Journal of Automatica Sinica Engineering-Control and Systems Engineering
CiteScore
23.50
自引率
11.00%
发文量
880
期刊介绍: The IEEE/CAA Journal of Automatica Sinica is a reputable journal that publishes high-quality papers in English on original theoretical/experimental research and development in the field of automation. The journal covers a wide range of topics including automatic control, artificial intelligence and intelligent control, systems theory and engineering, pattern recognition and intelligent systems, automation engineering and applications, information processing and information systems, network-based automation, robotics, sensing and measurement, and navigation, guidance, and control. Additionally, the journal is abstracted/indexed in several prominent databases including SCIE (Science Citation Index Expanded), EI (Engineering Index), Inspec, Scopus, SCImago, DBLP, CNKI (China National Knowledge Infrastructure), CSCD (Chinese Science Citation Database), and IEEE Xplore.
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