Treffer: Neural gain scheduling multiobjective genetic fuzzy PI control

Title:
Neural gain scheduling multiobjective genetic fuzzy PI control
Authors:
PASCOLI BOTTURA, Celso, SERRA, Ginalber Luiz De Oliveira
Source:
Proceedings of the 2004 IEEE International Symposium on Intelligent Control (September 2-4, 2004, The Grand Hotel, Taipei, Taiwan). :483-488
Publisher Information:
Piscataway, NJ: IEEE Service Center, 2004.
Publication Year:
2004
Physical Description:
print, 12 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Automatique théorique. Systèmes, Control theory. Systems, Systèmes adaptatifs, Adaptative systems, Synthèse des systèmes de commande, Control system synthesis, Commande optimale, Optimal control, Robotique, Robotics, Actionneur, Actuator, Accionador, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme optimal, Optimal algorithm, Algoritmo óptimo, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Base donnée, Database, Base dato, Commande adaptative, Adaptive control, Control adaptativo, Commande floue, Fuzzy control, Control difusa, Commande intelligente, Intelligent control, Control inteligente, Commande non linéaire, Non linear control, Control no lineal, Commande optimale, Optimal control, Control óptimo, Commande proportionnelle intégrale, Integral proportional control, Control PI, Commande vitesse, Speed control, Control velocidad, Dépassement, Overshoot, Rebasamiento, Lissage, Smoothing, Alisamiento, Manipulateur, Manipulator, Manipulador, Minimisation, Minimization, Minimización, Moteur courant continu, Dc motor, Motor corriente continua, Optimisation, Optimization, Optimización, Ordonnancement, Scheduling, Reglamento, Point fonctionnement, Operating point, Punto funcionamiento, Programmation multiobjectif, Multiobjective programming, Programación multiobjetivo, Robotique, Robotics, Robótica, Réponse temporelle, Time response, Respuesta temporal, Réseau neuronal, Neural network, Red neuronal, Synthèse commande, Control synthesis, Síntesis control, Système flou, Fuzzy system, Sistema difuso, Séquencement gain, Gain scheduling, Planificación ganancia, Temps discret, Discrete time, Tiempo discreto, Temps réponse, Response time, Tiempo respuesta
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Control and Intelligent Systems Laboratory, School of Electrical and Computer Engineering, State University of Campinas, 400, Cid. Univ. Zeferino Vaz, 13083-970, Campinas-SP, Brazil
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=17415075
Rights:
Copyright 2006 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:
Computer science; theoretical automation; systems
Accession Number:
edscal.17415075
Database:
PASCAL Archive

Weitere Informationen

This work proposes a gain scheduling adaptive control scheme based on fuzzy systems, neural networks and genetic algorithms for nonlinear plants. A fuzzy PI controller is developed, which is a discrete time version of a conventional one. Its data base as well as the constant PI control gins are optimally designed by using a genetic algorithm for simultaneously satisfying the following specifications: overshoot and settling time minimizations and output response smoothing. Hence, the optimization problem is a multiobjective one, from which results an optimal fuzzy PI controller. A neural gain scheduler is designed, by the backpropagation algorithm, to tune the optimal parameters of the fuzzy PI controller at some operating points. Simulation results are shown to demonstrate the efficiency of the proposed structure for a DC servomotor adaptive speed control system used as an actuator of robotic manipulators.