Research article summary (published 19 Aug 2009):

Projection-based adaptive neurocontrol with switching logic deadzone tuning.

Full Abstract

In this brief, an adaptive neural network (NN) controller is proposed for multiple-input-multiple-output (MIMO) nonlinear systems with triangular control structure and unknown control directions. Deadzones are employed in the projection-based NN weight learning laws and the Nussbaum parameter update laws with levels tuned by an innovative switching logic tuning mechanism. Detailed analysis using a family of Lyapunov-like integral functions and the function approximation capability of NNs proves that all the tracking errors are semiglobal uniform ultimate bounded in small neighborhoods of the origin while the closed-loop system variables (state vector, NN weights, Nussbaum parameters) and the control law remain bounded. A simulation study confirms the theoretical results and verifies the effectiveness of the proposed design.

Author information

Author/s: Psillakis, Haris E (HE);

Affiliation: Department of Electronic and Computer Engineering, Technical University of Crete, Chania, 73100 GR, Greece. psilakish(-atsign-)hotmail.com

Journal and publication information

Publication Type: Journal Article

Journal: IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council (IEEE Trans Neural Netw), published in United States. (Language: eng)

Reference: 2009-Sep; vol 20 (issue 9) : pp 1520-7

Dates: Created 2009/09/03; Completed 2009/11/04;

PMID: 19703800, status: MEDLINE (last retrieval date: 11/4/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms Artificial Intelligence Computer Simulation Linear Models Logic

Logic Neural Networks (Computer) Nonlinear Dynamics Time Factors Uncertainty

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