Dynamic Support Vector Regression Control System for Overlay Error Compensation With Stochastic Metrology Delay

Khakifirooz, Marzieh; Chien, Chen–Fu; Chen, Ying-Jen

doi:10.1109/tase.2019.2935179

Cited by 18 publications

(2 citation statements)

References 35 publications

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“…The support vector machine model has been extensively applied in control systems and forecasting models [37,38]. Support vector machines are very effective for solving non-linear problems and deal with a limited sample of training datasets.…”

Section: Support Vector Machinementioning

confidence: 99%

Fractional Order Sliding Mode Controller Based on Supervised Machine Learning Techniques for Speed Control of PMSM

et al. 2023

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Tracking the speed and current in permanent magnet synchronous motors (PMSMs) for industrial applications is challenging due to various external and internal disturbances such as parameter variations, unmodelled dynamics, and external load disturbances. Inaccurate tracking of speed and current results in severe system deterioration and overheating. Therefore, the design of the controller for a PMSM is essential to ensure the system can operate efficiently under conditions of parametric uncertainties and significant variations. The present work proposes a PMSM speed controller using machine learning (ML) techniques for quick response and insensitivity to parameter changes and disturbances. The proposed ML controller is designed by learning fractional-order sliding mode control (FOSMC) controller behavior. The primary purpose of using ML in FOSMC is to avoid the self-tuning of the parameters and ensure the speed reaches the reference value in finite time with faster convergence and better tracking precision. Furthermore, the ML model does not require the mathematical model of the speed controller. In this work, several ML models are empirically evaluated on their estimation accuracy for speed tracking, namely ordinary least squares, passive-aggressive regression, random forest, and support vector machine. Finally, the proposed controller is implemented on a real-time hardware-in-the-loop (HIL) simulation platform from PLECS Inc. Comparative simulation and experimental results are presented and discussed. It is shown from the comparative study that the proposed FOSMC based on ML outperformed the traditional sliding mode control (SMC), which is more commonly used in industry in terms of tracking speed and accuracy.

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Section: Support Vector Machinementioning

confidence: 99%

Fractional Order Sliding Mode Controller Based on Supervised Machine Learning Techniques for Speed Control of PMSM

et al. 2023

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“…Due to high demand and rigorous control demands, process and quality control has become increasingly difficult for nano technology nodes. Novel approaches for AQC, APC, and AEC have been developed to control production and enhance yield, including prognostic and health management (PHM), run-to-run (R2R) control, fault detection and classification (FDC) tools for analyzing large amounts of data, such as ex situ metrology data and/or in situ monitoring tool-level data, to improve process effectiveness and enhance quality [ 6 , 7 , 8 ]. R2R control approaches, including feed-forward, feedback and feed-forward/feedback controls, have been developed to proactively deal with process shifts between machine runs [ 9 ].…”

Section: Smart Manufacturing and R2r Control For Covid-19 Vaccine Manufacturingmentioning

confidence: 99%

How Smart Manufacturing Can Help Combat the COVID-19 Pandemic

2021

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Model-free MIMO self-tuning controller based on support vector regression for nonlinear systems

Uçak

Günel

2021

Neural Comput & Applic

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Dynamic Support Vector Regression Control System for Overlay Error Compensation With Stochastic Metrology Delay

Cited by 18 publications

References 35 publications

Fractional Order Sliding Mode Controller Based on Supervised Machine Learning Techniques for Speed Control of PMSM

Fractional Order Sliding Mode Controller Based on Supervised Machine Learning Techniques for Speed Control of PMSM

How Smart Manufacturing Can Help Combat the COVID-19 Pandemic

Model-free MIMO self-tuning controller based on support vector regression for nonlinear systems

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