کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5005201 | 1369013 | 2010 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Backstepping control for periodically time-varying systems using high-order neural network and Fourier series expansion
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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: Backstepping control for periodically time-varying systems using high-order neural network and Fourier series expansion Backstepping control for periodically time-varying systems using high-order neural network and Fourier series expansion](/preview/png/5005201.png)
چکیده انگلیسی
An adaptive backstepping tracking scheme is developed for a class of strict-feedback systems with unknown periodically time-varying parameters and unknown control gain functions. High-order neural network (HONN) and Fourier series expansion (FSE) are combined into a new function approximator to model each uncertain term in the system. The dynamic surface control (DSC) approach is used to solve the problem of 'explosion of complexity' in the backstepping design procedure. Nussbaum gain function (NGF) is employed to deal with the unknown control gain functions. The uniform boundedness of all closed-loop signals is guaranteed. The tracking error is proved to converge to a small residual set around the origin. Two simulation examples are provided to demonstrate the effectiveness of the control scheme designed in this paper.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: ISA Transactions - Volume 49, Issue 3, July 2010, Pages 283-292
Journal: ISA Transactions - Volume 49, Issue 3, July 2010, Pages 283-292
نویسندگان
Weisheng Chen, Wei Li, Qiguang Miao,