NDLI: Adaptive Neural Output Feedback Controller Design With Reduced-Order Observer for a Class of Uncertain Nonlinear SISO Systems

Content Provider	IEEE Xplore Digital Library
Author	Yan-Jun Liu Shao-Cheng Tong Wang, D. Tie-Shan Li Chen, C.L.P.
Copyright Year	1990
Abstract	An adaptive output feedback control is studied for uncertain nonlinear single-input-single-output systems with partial unmeasured states. In the scheme, a reduced-order observer (ROO) is designed to estimate those unmeasured states. By employing radial basis function neural networks and incorporating the ROO into a new backstepping design, an adaptive output feedback controller is constructively developed. A prominent advantage is its ability to balance the control action between the state feedback and the output feedback. In addition, the scheme can be still implemented when all the states are not available. The stability of the closed-loop system is guaranteed in the sense that all the signals are semiglobal uniformly ultimately bounded and the system output tracks the reference signal to a bounded compact set. A simulation example is given to validate the effectiveness of the proposed scheme.
Sponsorship	IEEE Computational Intelligence Society
Page Count	7
File Size	298360
Starting Page	1328
Ending Page	1334
File Format	PDF
ISSN	10459227
Volume Number	22
Issue Number	8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Nonlinear systems Output feedback Observers Silicon Adaptive systems Lyapunov methods Artificial neural networks reduced-order observer Adaptive neural control nonlinear systems output feedback control
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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