{"title":"Dynamic Separation Model-Based Sliding Mode Control with Adaptive Neural Network Compensators for a Reluctance Actuator Motion System","authors":"Yunlang Xu, Xinyi Su, Xiaofeng Yang","doi":"10.1007/s12541-024-01036-1","DOIUrl":null,"url":null,"abstract":"<p>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 (<i>I</i>–<i>B</i>) relationship and the flux density–force (<i>B</i>–<i>F</i>) 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.</p>","PeriodicalId":14359,"journal":{"name":"International Journal of Precision Engineering and Manufacturing","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Precision Engineering and Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s12541-024-01036-1","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
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 (I–B) relationship and the flux density–force (B–F) 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.
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.