کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
406724 | 678106 | 2013 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Adaptive neural tracking control of pure-feedback nonlinear systems with unknown gain signs and unmodeled dynamics
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Adaptive neural tracking control of pure-feedback nonlinear systems with unknown gain signs and unmodeled dynamics Adaptive neural tracking control of pure-feedback nonlinear systems with unknown gain signs and unmodeled dynamics](/preview/png/406724.png)
چکیده انگلیسی
In this paper, robust adaptive control is proposed for a class of pure-feedback nonlinear systems with unmodeled dynamics and unknown gain signs using radial basis function neural networks (RBFNNs). Dynamic uncertainties are dealt with using a dynamic signal. The unknown virtual gain signs are solved using the Nussbaum functions. Using mean value theorem and Young's inequality, only one learning parameter needs to be tuned online at each step of recursion. It is proved that the proposed design scheme can guarantee semi-global uniform ultimate boundedness (SGUUB) of all signals in the closed-loop system. Simulation results demonstrate the effectiveness of the proposed approach.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 121, 9 December 2013, Pages 290–297
Journal: Neurocomputing - Volume 121, 9 December 2013, Pages 290–297
نویسندگان
Tianping Zhang, Xiaocheng Shi, Qing Zhu, Yuequan Yang,