Treffer: Trigonometric RBF neural robust controller design for a class of nonlinear system with linear input unmodeled dynamics

Title:
Trigonometric RBF neural robust controller design for a class of nonlinear system with linear input unmodeled dynamics
Authors:
XINYU WANG, JUNWEI LEI, CHANGPENG PAN
Source:
Special issue on intelligent computing theory and methodologyApplied mathematics and computation. 185(2):989-1002
Publisher Information:
New York, NY: Elsevier, 2007.
Publication Year:
2007
Physical Description:
print, 43 ref
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Combinatoire. Structures ordonnées, Combinatorics. Ordered structures, Combinatoire, Combinatorics, Plans d'expériences et configurations, Designs and configurations, Algèbre, Algebra, Géométrie algébrique, Algebraic geometry, Analyse mathématique, Mathematical analysis, Fonctions spéciales, Special functions, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Algèbre linéaire numérique, Numerical linear algebra, Algèbre linéaire numérique, Numerical linear algebra, Algebra lineal numérica, Analyse numérique, Numerical analysis, Análisis numérico, Augmentation, Increase, Aumentación, Conception, Design, Diseño, Contrôleur, Controller, Supervisor, Dynamique, Dynamics, Dinámica, Entrée ordinateur, Input, Entrada ordenador, Entrée sortie, Input output, Entrada salida, Estimation erreur, Error estimation, Estimación error, Fonction Lyapunov, Lyapunov function, Función Lyapunov, Fonction trigonométrique, Trigonometric function, Función trigonométrica, Fonction échelon, Step function, Función escala, Force, Fuerza, Inversion matrice, Matrix inversion, Inversión matriz, Mathématiques appliquées, Applied mathematics, Matemáticas aplicadas, Matrice, Matrices, Méthode adaptative, Adaptive method, Método adaptativo, Méthode différentielle, Differential method, Método diferencial, Méthode directe, Direct method, Método directo, Norme, Standards, Norma, Plan expérience, Experimental design, Plan experiencia, Reconstruction, Reconstrucción, Réseau neuronal, Neural network, Red neuronal, Simulation statistique, Statistical simulation, Simulación estadística, Système information, Information system, Sistema información, Système linéaire, Linear system, Sistema lineal, Système non linéaire, Non linear system, Sistema no lineal, 05Bxx, 14C20, 33B10, 37B25, 37L45, 65F05, Fonction base, Voisinage, Backstepping, Input unmodeled: RBF neural network, Robust
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Institute of Science and Technology for Opto-electronic Information, Yantai University, Yantai, Shandong Province 264001, China
Department of Control Engineering, Naval Aeronautical Engineering Institute, Yantai 264001, China
ISSN:
0096-3003
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18637806
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.18637806
Database:
PASCAL Archive

Weitere Informationen

Considered both the situation with unknown control function matrices and the situation with linear unmodeled input dynamics, adaptive neural robust controller was designed by using adaptive backstepping method for a class of multi-input to multi-output nonlinear systems which could be turned to standard block control type. It was proved by constructing Lyapunov function step by step that all signals of the system are bounded and exponentially converge to the neighborhood of the origin globally. And by adopting the trigonometric function as basis function, the input need not be force to between -1 and 1, and there is no need to choose the centre of basis function which reduced the difficulty of doing simulation and made the neural net work more practical. And the variable structure control is adopt to eliminate the error of approximalion. Also the method of differential reconstruction of neural network is used to increase the damp of neural network and it makes the system more stable. Finally, simulation study is given to demonstrate that the proposed method is effective and the known information of system was made use of as maximally as possible by introducing the PID control.