• Semi-active vibration control...

Treffer: Semi-active vibration control of an eleven degrees of freedom suspension system using neuro inverse model of magnetorheological dampers

Title:
Semi-active vibration control of an eleven degrees of freedom suspension system using neuro inverse model of magnetorheological dampers
Authors:
ZAREH, Seiyed Hamid, ABBASI, Meisam, MAHDAVI, Hadi, OSGOUIE, Kambiz Ghaemi
Source:
Journal of mechanical science and technology. 26(8):2459-2467
Publisher Information:
Seoul: Korean Society of Mechanical Engineers, 2012.
Publication Year:
2012
Physical Description:
print, 21 ref
Original Material:
INIST-CNRS
Subject Terms:
Mechanics acoustics, Mécanique et acoustique, Sciences exactes et technologie, Exact sciences and technology, Physique, Physics, Domaines classiques de la physique (y compris les applications), Fundamental areas of phenomenology (including applications), Mécanique des solides, Solid mechanics, Mécanique des structures et des milieux continus, Structural and continuum mechanics, Vibration, onde mécanique, stabilité dynamique (aéroélasticité, contrôle vibration...), Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...), Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Automatique théorique. Systèmes, Control theory. Systems, Synthèse des systèmes de commande, Control system synthesis, Genie mecanique. Construction mecanique, Mechanical engineering. Machine design, Organes de machines, Machine components, Ressorts. Amortisseurs, Springs and dampers, Transports terrestres, transports aeriens, transports maritimes, constructions navales, Ground, air and sea transportation, marine construction, Généralités, General, Algorithme optimal, Optimal algorithm, Algoritmo óptimo, Amortisseur hydraulique, Hydraulic damper, Amortiguador hidráulico, Commande LQ, LQ control, Control LQ, Commande LQG, LQG control, Control LQG, Commande force, Force control, Control fuerza, Commande linéaire, Linear control, Control lineal, Commande optimale, Optimal control, Control óptimo, Confort, Comfort, Comodidad, Contrôle vibration, Vibration control, Control vibración, Durée service, Service life, Duración servicio, Effet non linéaire, Non linear effect, Efecto no lineal, Fluide magnétorhéologique, Magnetorheological fluid, Fluido magnetoreologico, Modélisation, Modeling, Modelización, Problème inverse, Inverse problem, Problema inverso, Réseau neuronal, Neural network, Red neuronal, Suspension véhicule, Vehicle suspension, Suspensión vehículo, Synthèse commande, Control synthesis, Síntesis control, Système actif, Active system, Sistema activo, Système passif, Passive system, Sistema pasivo, Temps retard, Delay time, Tiempo retardo, Clipped optimal algorithm, Inverse model, MR damper, Semi-active control, Suspension system
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
School of Science and Engineering Sharif University of Technology, Iran, Islamic Republic of
Department of Mechanical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran, Islamic Republic of
ISSN:
1738-494X
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=26238317
Rights:
Copyright 2015 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:
Building. Public works. Transport. Civil engineering

Computer science; theoretical automation; systems

Mechanical engineering. Mechanical construction. Handling

Physics: solid mechanics
Accession Number:
edscal.26238317
Database:
PASCAL Archive

Weitere Informationen

A semi-active controller-based neural network for a suspension system with magnetorheological (MR) dampers is presented and evaluated. An inverse neural network model (NIMR) is constructed to replicate the inverse dynamics of the MR damper. The typical control strategies are linear quadratic regulator (LQR) and linear quadratic gaussian (LQG) controllers with a clipped optimal control algorithm, while inherent time-delay and non-linear properties of MR damper lie in these strategies. LQR part of LQG controller is also designed to produce the optimal control force. The LQG controller and the NIMR models are linked to control the system. The effectiveness of the NIMR is illustrated and verified using simulated responses of a full-car model. The results demonstrate that by using the NIMR model, the MR damper force can be commanded to follow closely the desirable optimal control force. The results also show that the control system is effective and achieves better performance and less control effort than the optimal in improving the service life of the suspension system and the ride comfort of a car.