基于迭代学习观测器的高精度运动控制,适用于线性电机驱动系统的重复性运动任务

IF 5.2 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Open Journal of the Industrial Electronics Society Pub Date : 2024-01-29 DOI:10.1109/OJIES.2024.3359951
Zhitai Liu;Xinghu Yu;Weiyang Lin;Juan J. Rodríguez-Andina
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引用次数: 0

摘要

重复运动是线性电机(LM)驱动系统中最常见的运动任务之一。线性电机根据受控的周期性目标轨迹执行重复运动,从而导致某些系统不确定性的周期性特征。针对这类任务,本文提出了一种基于迭代学习观测器的高精度运动控制方案,该方案综合考虑了高精度模型补偿和周期性不确定性估计。该方案采用基于递归最小二乘(RLS)算法的间接适应策略来实现高精度参数估计和模型补偿。设计了饱和约束型迭代学习观测器,以有效估计和补偿周期性不确定性。由于采用了复合自适应鲁棒控制设计,在存在周期性和非周期性不确定性的情况下,系统的闭环稳定性都能得到保证。在 LM 驱动的运动平台上进行了对比实验,以验证所提控制方案的有效性和优势。此外,实验结果还证实了系统瞬态和稳态性能的增强。
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Iterative Learning Observer-Based High-Precision Motion Control for Repetitive Motion Tasks of Linear Motor-Driven Systems
Repetitive motion is one of the most common motion tasks in linear motor (LM)-driven system. The LM performs repetitive motion based on a periodic target trajectory under control, thus leading to periodic characteristics in certain system uncertainties. For this type of task, this article proposes an iterative learning observer-based high-precision motion control scheme that comprehensively considers high-accuracy model compensation and periodic uncertainties estimation. A recursive least squares (RLS) algorithm-based indirect adaptation strategy is used to achieve high-accuracy parameter estimation and model compensation. A saturated constrained-type iterative learning observer is designed to effectively estimate and compensate for periodic uncertainties. The closed-loop stability of the system is guaranteed in the presence of both periodic and nonperiodic uncertainties due to the composite adaptive robust control design. Comparative experiments are conducted on an LM-driven motion platform to verify the effectiveness and advantages of the proposed control scheme. Furthermore, the experimental results confirm the enhancement of both the transient and steady-state performance of the system.
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来源期刊
IEEE Open Journal of the Industrial Electronics Society
IEEE Open Journal of the Industrial Electronics Society ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
10.80
自引率
2.40%
发文量
33
审稿时长
12 weeks
期刊介绍: The IEEE Open Journal of the Industrial Electronics Society is dedicated to advancing information-intensive, knowledge-based automation, and digitalization, aiming to enhance various industrial and infrastructural ecosystems including energy, mobility, health, and home/building infrastructure. Encompassing a range of techniques leveraging data and information acquisition, analysis, manipulation, and distribution, the journal strives to achieve greater flexibility, efficiency, effectiveness, reliability, and security within digitalized and networked environments. Our scope provides a platform for discourse and dissemination of the latest developments in numerous research and innovation areas. These include electrical components and systems, smart grids, industrial cyber-physical systems, motion control, robotics and mechatronics, sensors and actuators, factory and building communication and automation, industrial digitalization, flexible and reconfigurable manufacturing, assistant systems, industrial applications of artificial intelligence and data science, as well as the implementation of machine learning, artificial neural networks, and fuzzy logic. Additionally, we explore human factors in digitalized and networked ecosystems. Join us in exploring and shaping the future of industrial electronics and digitalization.
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