Multi-Objective Genetic Algorithm Optimization based Fuzzy Control

Mohammad Javad Mahmoodabadi

ISBN 10: 3659914118 / ISBN 13: 9783659914119

Editore: LAP LAMBERT Academic Publishing Okt 2017, 2017

Riguardo questo articolo

Neuware -A fuzzy controller for anti-swing and positioning control of an overhead traveling crane is proposed based on the SIRMs (Single Input Rule Modules) dynamically connected fuzzy inference model. The trolley position and velocity, the rope swing angle and angular velocity are selected as input items, and the trolley acceleration as output item. With a simple structure, the controller can autonomously adjust the influence of each input item. The control system is further proved to be asymptotically stable near destination. Multi-objective genetic algorithm optimization is successfully implemented to find the controller gains. Control simulation results show that the controller is robust to different rope lengths and has generalization ability for different initial positions. Compared with linear state feedback controller, the fuzzy controller can drive the crane to destination in short time with small swing angle.Books on Demand GmbH, Überseering 33, 22297 Hamburg 100 pp. Englisch.

Codice articolo 9783659914119

Dati bibliografici

• Titolo: Multi-Objective Genetic Algorithm Optimization based Fuzzy Control
• Editore: LAP LAMBERT Academic Publishing Okt 2017
• Data di pubblicazione: 2017
• Lingua: Inglese
• ISBN 10: 3659914118
• ISBN 13: 9783659914119
• Rilegatura: Taschenbuch
• Condizione: Neu

Descrizione articolo

Riassunto

A fuzzy controller for anti-swing and positioning control of an overhead traveling crane is proposed based on the SIRMs (Single Input Rule Modules) dynamically connected fuzzy inference model. The trolley position and velocity, the rope swing angle and angular velocity are selected as input items, and the trolley acceleration as output item. With a simple structure, the controller can autonomously adjust the influence of each input item. The control system is further proved to be asymptotically stable near destination. Multi-objective genetic algorithm optimization is successfully implemented to find the controller gains. Control simulation results show that the controller is robust to different rope lengths and has generalization ability for different initial positions. Compared with linear state feedback controller, the fuzzy controller can drive the crane to destination in short time with small swing angle.

Informazioni sull?autore

Mohammad Javad Mahmoodabadi received his B.Sc. and M.Sc. degrees at Mechanical Engineering from Shahid Bahonar University of Kerman in 2005 and 2007, respectively. He completed his Ph.D. degree at Mechanical Engineering in the University of Guilan in 2012. Currently, he is an Assistant Professor in Sirjan University of Technology, Sirjan, Iran.