Dynamic Separation Model-Based Sliding Mode Control with Adaptive Neural Network Compensators for a Reluctance Actuator Motion System

IF 1.9 4区 工程技术 Q2 Engineering
Yunlang Xu, Xinyi Su, Xiaofeng Yang
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Abstract

The maglev technology has been recently used for advanced semiconductor equipment. The stringent accuracy requirement of the semiconductor manufacturing processes has posed new challenges about modeling and control of maglev systems (MLSs). This paper presents a new sliding mode control (SMC) scheme, named as SMCLFF, to tackle the impacts of inherent non-linearities caused by leakage and fringing fluxes (LFF), and external disturbances caused by the gap measurement mismatch (GMM) and non-orthogonal force (NOF) on the control of the MLS. A dynamic separation model (DSM) is designed to model the LFF effects in both the current–flux density (IB) relationship and the flux density–force (BF) relationship. The system is linearized by the DSM firstly, and the residual LFF effects and the external disturbances are suppressed by adaptive RBF neural networks (NNs) in SMCLFF respectively. The stability of the closed-loop control system was analyzed. Experiments were performed on a one-dimensional MLS plant. Results show that the DSM can effectively compensate for the LFF effects, and SMCLFF can enable the MLS to obtain high performance in a closed-loop control system.

Abstract Image

采用自适应神经网络补偿器的基于动态分离模型的滑动模式控制,适用于电感致动器运动系统
磁悬浮技术最近被用于先进的半导体设备。半导体制造工艺对精度的严格要求对磁悬浮系统(MLS)的建模和控制提出了新的挑战。本文提出了一种名为 SMCLFF 的新型滑模控制(SMC)方案,以解决由泄漏和边缘通量(LFF)引起的固有非线性以及由间隙测量不匹配(GMM)和非正交力(NOF)引起的外部干扰对 MLS 控制的影响。我们设计了一个动态分离模型(DSM),以模拟电流-通量密度(I-B)关系和通量密度-力(B-F)关系中的 LFF 效应。首先通过 DSM 对系统进行线性化,然后通过 SMCLFF 中的自适应 RBF 神经网络(NN)分别抑制残余 LFF 效应和外部干扰。分析了闭环控制系统的稳定性。实验在一维 MLS 工厂上进行。结果表明,DSM 能有效补偿 LFF 效应,而 SMCLFF 能使 MLS 在闭环控制系统中获得高性能。
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来源期刊
CiteScore
4.10
自引率
10.50%
发文量
115
审稿时长
3-6 weeks
期刊介绍: The International Journal of Precision Engineering and Manufacturing accepts original contributions on all aspects of precision engineering and manufacturing. The journal specific focus areas include, but are not limited to: - Precision Machining Processes - Manufacturing Systems - Robotics and Automation - Machine Tools - Design and Materials - Biomechanical Engineering - Nano/Micro Technology - Rapid Prototyping and Manufacturing - Measurements and Control Surveys and reviews will also be planned in consultation with the Editorial Board.
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