ملخص
This article presents a Lyapunov function based neural network tracking (LNT) strategy for single-input, single-output (SISO) discrete-time nonlinear dynamic systems. The proposed LNT architecture is composed of two feedforward neural networks operating as controller and estimator. A Lyapunov function based back propagation learning algorithm is used for online adjustment of the controller and estimator parameters. The controller and estimator error convergence and closed-loop system stability analysis is performed by Lyapunov stability theory. Moreover, two simulation examples and one real-time experiment are investigated as case studies. The achieved results successfully validate the controller performance.
اللغة الأصلية | English |
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الصفحات (من إلى) | 363-374 |
عدد الصفحات | 12 |
دورية | ISA Transactions |
مستوى الصوت | 59 |
المعرِّفات الرقمية للأشياء | |
حالة النشر | Published - نوفمبر 2015 |
ASJC Scopus subject areas
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