数控机床进给系统 GMS 摩擦模型的有效参数识别

IF 5.4 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
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引用次数: 0

摘要

摩擦是影响机床进给系统跟踪性能的主要因素之一。通过创建精确的摩擦模型并根据模型实施前馈补偿,可以有效减少摩擦的负面影响。广义麦克斯韦-滑移(GMS)模型常用于建立进给系统摩擦模型,但缺乏简单有效的参数识别方法。本文提出了一种基于元启发式高斯群优化(GSO)算法的参数识别方法。该方法通过理论推导将参数分为两部分,并利用高斯群优化算法依次识别每一部分。所提出的 GSO 是一种受高斯概率函数启发的新型元启发式算法。GSO 的优异性能确保了摩擦参数能被准确、快速地识别。仿真和物理识别实验结果表明,基于 GSO 的识别方法可以准确识别 GMS 模型的参数,平均和最大相对误差分别为 3.96% 和 14.05%。识别出的模型可以准确预测进给系统的摩擦力。此外,摩擦补偿后,跟踪误差平均降低了 78.9%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effective parameter identification of the GMS friction model for feed systems in CNC machines

One of the main factors influencing machine tool feed system tracking performance is friction. By creating an accurate friction model and implementing feed-forward compensation based on the model, the negative impacts of friction can be efficiently reduced. The generalized Maxwell-slip (GMS) model is commonly used to model feed system friction; however, simple and effective parameter identification methods are lacking. In this paper, a parameter identification method based on a metaheuristic Gaussian swarm optimization (GSO) algorithm is proposed. The method divides the parameters into two parts via a theoretical derivation, and employs GSO to identify each part successively. The proposed GSO is a novel metaheuristic algorithm inspired by the Gaussian probability function. The excellent performance of the GSO ensures that the friction parameters can be accurately and quickly identified. The results of the simulation and physical identification experiments show that the proposed GSO-based identification method can accurately identify the parameters of the GMS model with average and maximum relative errors of 3.96% and 14.05%, respectively. The identified model can accurately predict the friction of the feed system. Additionally, after friction compensation, the tracking error was decreased by an average of 78.9%.

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来源期刊
Control Engineering Practice
Control Engineering Practice 工程技术-工程:电子与电气
CiteScore
9.20
自引率
12.20%
发文量
183
审稿时长
44 days
期刊介绍: Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper. The scope of Control Engineering Practice matches the activities of IFAC. Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.
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