کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7116259 | 1461179 | 2018 | 17 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Data-driven adaptive fractional order PI control for PMSM servo system with measurement noise and data dropouts
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
کنترل و سیستم های مهندسی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Data-driven adaptive fractional order PI control for PMSM servo system with measurement noise and data dropouts Data-driven adaptive fractional order PI control for PMSM servo system with measurement noise and data dropouts](/preview/png/7116259.png)
چکیده انگلیسی
In this paper, data-driven adaptive fractional order proportional integral (AFOPI) control is presented for permanent magnet synchronous motor (PMSM) servo system perturbed by measurement noise and data dropouts. The proposed method directly exploits the closed-loop process data for the AFOPI controller design under unknown noise distribution and data missing probability. Firstly, the proposed method constructs the AFOPI controller tuning problem as a parameter identification problem using the modified lp norm virtual reference feedback tuning (VRFT). Then, iteratively reweighted least squares is integrated into the lp norm VRFT to give a consistent compensation solution for the AFOPI controller. The measurement noise and data dropouts are estimated and eliminated by feedback compensation periodically, so that the AFOPI controller is updated online to accommodate the time-varying operating conditions. Moreover, the convergence and stability are guaranteed by mathematical analysis. Finally, the effectiveness of the proposed method is demonstrated both on simulations and experiments implemented on a practical PMSM servo system.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: ISA Transactions - Volume 75, April 2018, Pages 172-188
Journal: ISA Transactions - Volume 75, April 2018, Pages 172-188
نویسندگان
Yuanlong Xie, Xiaoqi Tang, Bao Song, Xiangdong Zhou, Yixuan Guo,