9789811965166 - Optimal Fractional-order Predictive Pi Controllers: for Process Control Applications with Additional Filtering (studies in Infrastructure and Control) by Panneer Selvam, Arun Mozhi Devan; Hussin, Fawnizu Azmadi; Ibrahim, Rosdiazli; Bingi, Kishore; M., Nagarajapandian (15 results)
Optimal Fractional-order Predictive Pi Controllers : For Process Control Applications With Additional Filtering
Selvam, Arun Mozhi Devan Panneer; Hussin, Fawnizu Azmadi; Ibrahim, Rosdiazli; Bingi, Kishore; M., Nagarajapandian
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Optimal Fractional-order Predictive Pi Controllers : For Process Control Applications With Additional Filtering
Selvam, Arun Mozhi Devan Panneer; Hussin, Fawnizu Azmadi; Ibrahim, Rosdiazli; Bingi, Kishore; M., Nagarajapandian
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Hardcover. Condition: new. Hardcover. This book presents the study to design, develop, and implement improved PI control techniques using dead-time compensation, structure enhancements, learning functions and fractional ordering parameters. Two fractional-order PI controllers are proposed and designed: fractional-order predictiv…e PI and hybrid iterative learning based fractional-order predictive PI controller. Furthermore, the proposed fractional-order control strategies and filters are simulated over first- and second-order benchmark process models and further validated using the real-time experimentation of the pilot pressure process plant. In this book, five chapters are structured with a proper sequential flow of details to provide a better understanding for the readers. A general introduction to the controllers, filters and optimization techniques is presented in Chapter 1. Reviews of the PI controllers family and their modifications are shown in the initial part of Chapter 2, followed by the development of the proposed fractional-order predictive PI (FOPPI) controller with dead-time compensation ability. In the first part of chapter 3, a review of the PI based iterative learning controllers, modified structures of the ILC and their modifications are presented. Then, the design of the proposed hybrid iterative learning controller-based fractional-order predictive PI controller based on the current cyclic feedback structure is presented. Lastly, the results and discussion of the proposed controller on benchmark process models and the real-time experimentation of the pilot pressure process plant are given. Chapter 4 presents the development of the proposed filtering techniques and their performance comparison with the conventional methods. Chapter 5 proposes the improvement of the existing sine cosine algorithm (SCA) and arithmetic optimization algorithm (AOA) to form a novel arithmetic-trigonometric optimization algorithm (ATOA) to accelerate the rate of convergence in lesser iterations with mitigation towards getting caught in the same local position. The performance analysis of the optimization algorithm will be carried out on benchmark test functions and the real-time pressure process plant. Two fractional-order PI controllers are proposed and designed: fractional-order predictive PI and hybrid iterative learning based fractional-order predictive PI controller. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Optimal Fractional-order Predictive PI Controllers: For Process Control Applications with Additional Filtering (Studies in Infrastructure and Control)
Panneer Selvam, Arun Mozhi Devan; Hussin, Fawnizu Azmadi; Ibrahim, Rosdiazli; Bingi, Kishore; M., Nagarajapandian
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Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
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Condition: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Introduction.- Fractional-order Predictive PI Controller for Dead-time Process Plants.- Hybrid Iterative Learning Controller Based Fractional-order Predictive PI Controller.- Development of Proposed Fractional-order Filtering Techniques.- D…evelopment of the Proposed Arithmetic-Trigonometric Optimization Algorithm.- Appendix.
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Hardcover. Condition: Brand New. 164 pages. 9.25x6.10x0.63 inches. In Stock.
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents the study to design, develop, and implement improved PI control techniques using dead-time compensation, structure enhancements, learning functions and fractional ordering parameters. Two fractional-order PI controllers are proposed and… designed: fractional-order predictive PI and hybrid iterative learning based fractional-order predictive PI controller. Furthermore, the proposed fractional-order control strategies and filters are simulated over first- and second-order benchmark process models and further validated using the real-time experimentation of the pilot pressure process plant.In this book, five chapters are structured with a proper sequential flow of details to provide a better understanding for the readers. A general introduction to the controllers, filters and optimization techniques is presented in Chapter 1. Reviews of the PI controllers family and their modifications are shown in the initial part of Chapter 2, followed by the development of the proposed fractional-order predictive PI (FOPPI) controller with dead-time compensation ability. In the first part of chapter 3, a review of the PI based iterative learning controllers, modified structures of the ILC and their modifications are presented. Then, the design of the proposed hybrid iterative learning controller-based fractional-order predictive PI controller based on the current cyclic feedback structure is presented. Lastly, the results and discussion of the proposed controller on benchmark process models and the real-time experimentation of the pilot pressure process plant are given. Chapter 4 presents the development of the proposed filtering techniques and their performance comparison with the conventional methods. Chapter 5 proposes the improvement of the existing sine cosine algorithm (SCA) and arithmetic optimization algorithm (AOA) to form a novel arithmetic-trigonometric optimization algorithm (ATOA) to accelerate the rate of convergence in lesser iterations with mitigation towards getting caught in the same local position. The performance analysis of the optimization algorithm will be carried out on benchmark test functions and the real-time pressure process plant.
Published by Springer Nature Singapore, 2022
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Hardcover. Condition: gut. 2022. Optimal Fractional-order Predictive PI Controllers In deutscher Sprache. pages.
Optimal Fractional-order Predictive PI Controllers
Panneer Selvam, Arun Mozhi Devan; Hussin, Fawnizu Azmadi; Ibrahim, Rosdiazli; Bingi, Kishore; M., Nagarajapandian
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents the study to design, develop, and implement improved PI control techniques using dead-time compensation, structure enhancements, learning functions and fractional ordering parameters. Two fractional-order PI controllers…are proposed and designed: fractional-order predictive PI and hybrid iterative learning based fractional-order predictive PI controller. Furthermore, the proposed fractional-order control strategies and filters are simulated over first- and second-order benchmark process models and further validated using the real-time experimentation of the pilot pressure process plant.In this book, five chapters are structured with a proper sequential flow of details to provide a better understanding for the readers. A general introduction to the controllers, filters and optimization techniques is presented in Chapter 1. Reviews of the PI controllers family and their modifications are shown in the initial part of Chapter 2, followed by the development of the proposed fractional-order predictive PI (FOPPI) controller with dead-time compensation ability. In the first part of chapter 3, a review of the PI based iterative learning controllers, modified structures of the ILC and their modifications are presented. Then, the design of the proposed hybrid iterative learning controller-based fractional-order predictive PI controller based on the current cyclic feedback structure is presented. Lastly, the results and discussion of the proposed controller on benchmark process models and the real-time experimentation of the pilot pressure process plant are given. Chapter 4 presents the development of the proposed filtering techniques and their performance comparison with the conventional methods. Chapter 5 proposes the improvement of the existing sine cosine algorithm (SCA) and arithmetic optimization algorithm (AOA) to form a novel arithmetic-trigonometric optimization algorithm (ATOA) to accelerate the rate of convergence in lesser iterations with mitigation towards getting caught in the same local position. The performance analysis of the optimization algorithm will be carried out on benchmark test functions and the real-time pressure process plant. 164 pp. Englisch.
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Published by Springer, Berlin|Springer Nature Singapore|Yayasan UTP Fundamental Research|Springer, 2023
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book presents the study to design, develop, and implement improved PI control techniques using dead-time compensation, structure enhancements, learning functions and fractional ordering parameters. Two fractional…-order PI controllers are proposed an.
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Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents the study to design, develop, and implement improved PI control techniques using dead-time compensation, structure enhancements, learning functions and fractional ordering parameters. Two fractional-order PI controllers are…proposed and designed: fractional-order predictive PI and hybrid iterative learning based fractional-order predictive PI controller. Furthermore, the proposed fractional-order control strategies and filters are simulated over first- and second-order benchmark process models and further validated using the real-time experimentation of the pilot pressure process plant.In this book, five chapters are structured with a proper sequential flow of details to provide a better understanding for the readers. A general introduction to the controllers, filters and optimization techniques is presented in Chapter 1. Reviews of the PI controllers family and their modifications are shown in the initial part of Chapter 2, followed by the development of the proposed fractional-order predictive PI (FOPPI) controller with dead-time compensation ability. In the first part of chapter 3, a review of the PI based iterative learning controllers, modified structures of the ILC and their modifications are presented. Then, the design of the proposed hybrid iterative learning controller-based fractional-order predictive PI controller based on the current cyclic feedback structure is presented. Lastly, the results and discussion of the proposed controller on benchmark process models and the real-time experimentation of the pilot pressure process plant are given. Chapter 4 presents the development of the proposed filtering techniques and their performance comparison with the conventional methods. Chapter 5 proposes the improvement of the existing sine cosine algorithm (SCA) and arithmetic optimization algorithm (AOA) to form a novel arithmetic-trigonometric optimization algorithm (ATOA) to accelerate the rate of convergence in lesser iterations with mitigation towards getting caught in the same local position. The performance analysis of the optimization algorithm will be carried out on benchmark test functions and the real-time pressure process plant.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 164 pp. Englisch.
Optimal Fractional-order Predictive PI Controllers: For Process Control Applications with Additional Filtering (Studies in Infrastructure and Control)
Panneer Selvam, Arun Mozhi Devan; Hussin, Fawnizu Azmadi; Ibrahim, Rosdiazli; Bingi, Kishore; M., Nagarajapandian
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Optimal Fractional-order Predictive PI Controllers: For Process Control Applications with Additional Filtering (Studies in Infrastructure and Control)
Panneer Selvam, Arun Mozhi Devan; Hussin, Fawnizu Azmadi; Ibrahim, Rosdiazli; Bingi, Kishore; M., Nagarajapandian
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