Synopsis
Despite the fact that PID controllers are undoubtedly the most popular controller used in industrial control processes for decades, they do not perform well when applied to systems with significant time-delay. Recently ,researchers have realized that the IMC is particularly good for controlling time-delayed plants. Using of two algorithms Least square & Genetic algorithm designed the new internal model controller ,the issue of designing a internal model controller for dealing with the parameter uncertainties existing in the plant. The genetic algorithm controller design technique for dealing with parameter uncertainty existing in the plant will then be deployed to design internal model controllers. In particular the objective of the thesis are to: Investigate the deployment of the genetic algorithm in designing system identification for the plant. Developed simplified approach in designing IMC . Investigate the deployment of the genetic algorithm in designing IMC controller. Compare the performance of genetically tuned controller with Least square . Implement the genetically & least square tuned IMC controller on a real system and compare the performanc
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About the Author
FARHAN MAHBUB Born 1985 in Dhaka,Bangladesh.Completed MSc In Control system Engineer from University of Salford,Manchester.With control system skills, educational background, and professional experience wants to increasing level of responsibility and advancement and opportunity to work for a quality organization.
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System Identification Of Internal Model Control
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Despite the fact that PID controllers are undoubtedly the most popular controller used in industrial control processes for decades, they do not perform well when applied to systems with significant time-delay. Recently ,researchers have realized that the IMC is particularly good for controlling time-delayed plants. Using of two algorithms Least square & Genetic algorithm designed the new internal model controller ,the issue of designing a internal model controller for dealing with the parameter uncertainties existing in the plant. The genetic algorithm controller design technique for dealing with parameter uncertainty existing in the plant will then be deployed to design internal model controllers. In particular the objective of the thesis are to: Investigate the deployment of the genetic algorithm in designing system identification for the plant. Developed simplified approach in designing IMC . Investigate the deployment of the genetic algorithm in designing IMC controller. Compare the performance of genetically tuned controller with Least square . Implement the genetically & least square tuned IMC controller on a real system and compare the performanc 84 pp. Englisch. Seller Inventory # 9783846523520
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System Identification Of Internal Model Control
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Mahbub FarhanFARHAN MAHBUB Born 1985 in Dhaka,Bangladesh.Completed MSc In Control system Engineer from University of Salford,Manchester.With control system skills, educational background, and professional experience wants to increas. Seller Inventory # 5496528
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System Identification Of Internal Model Control
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ISBN 10: 3846523526
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Despite the fact that PID controllers are undoubtedly the most popular controller used in industrial control processes for decades, they do not perform well when applied to systems with significant time-delay. Recently ,researchers have realized that the IMC is particularly good for controlling time-delayed plants. Using of two algorithms Least square & Genetic algorithm designed the new internal model controller ,the issue of designing a internal model controller for dealing with the parameter uncertainties existing in the plant. The genetic algorithm controller design technique for dealing with parameter uncertainty existing in the plant will then be deployed to design internal model controllers. In particular the objective of the thesis are to: ¿ Investigate the deployment of the genetic algorithm in designing system identification for the plant. ¿ Developed simplified approach in designing IMC . ¿ Investigate the deployment of the genetic algorithm in designing IMC controller. ¿ Compare the performance of genetically tuned controller with Least square . ¿ Implement the genetically & least square tuned IMC controller on a real system and compare the performancVDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 84 pp. Englisch. Seller Inventory # 9783846523520
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System Identification Of Internal Model Control : Internal Model Control
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Despite the fact that PID controllers are undoubtedly the most popular controller used in industrial control processes for decades, they do not perform well when applied to systems with significant time-delay. Recently ,researchers have realized that the IMC is particularly good for controlling time-delayed plants. Using of two algorithms Least square & Genetic algorithm designed the new internal model controller ,the issue of designing a internal model controller for dealing with the parameter uncertainties existing in the plant. The genetic algorithm controller design technique for dealing with parameter uncertainty existing in the plant will then be deployed to design internal model controllers. In particular the objective of the thesis are to: Investigate the deployment of the genetic algorithm in designing system identification for the plant. Developed simplified approach in designing IMC . Investigate the deployment of the genetic algorithm in designing IMC controller. Compare the performance of genetically tuned controller with Least square . Implement the genetically & least square tuned IMC controller on a real system and compare the performanc. Seller Inventory # 9783846523520
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System Identification Of Internal Model Control: Internal Model Control
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