کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4944065 | 1437977 | 2018 | 22 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Decentralized adaptive neural control for high-order stochastic nonlinear strongly interconnected systems with unknown system dynamics
ترجمه فارسی عنوان
کنترل عصبی انطباق منحصربفرد برای سیستم های به شدت متقابل غیر خطی با سیستم های دوردست سیستم ناشناخته
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
This paper studies the problem of decentralized adaptive neural backstepping control for a class of high-order stochastic nonlinear systems with unknown strongly interconnected nonlinearity. During the control of the high-order nonlinear interconnected systems, only one adaptive parameter is used to overcome the over-parameterization problem, and radial basis function (RBF) neural networks are employed to tackle the difficulties brought about by completely unknown system dynamics and stochastic disturbances. In addition, to address the problem arising from high-order strongly interconnected nonlinearities with full states of the overall system, the variable separation technique is introduced based on the monotonically increasing property of the bounding functions. Next, a decentralized adaptive neural control method is proposed based on Lyapunov stability theory, in which the controller is designed to decrease the number of learning parameters. It is shown that the designed controller can ensure that all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. Finally, two simulation examples are offered to illustrate the effectiveness of the proposed control scheme.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 424, January 2018, Pages 137-158
Journal: Information Sciences - Volume 424, January 2018, Pages 137-158
نویسندگان
Wenjie Si, Xunde Dong, Feifei Yang,