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Optimal fractional-order predictive ...

Panneer Selvam, Arun Mozhi Devan.

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Optimal fractional-order predictive PI controllers = for process control applications with additional filtering /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optimal fractional-order predictive PI controllers/ by Arun Mozhi Devan Panneer Selvam ... [et al.].
Reminder of title:	for process control applications with additional filtering /
other author:	Panneer Selvam, Arun Mozhi Devan.
Published:	Singapore :Springer Nature Singapore : : 2022.,
Description:	xviii, 146 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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 -- Development of the Proposed Arithmetic-Trigonometric Optimization Algorithm -- Appendix.
Contained By:	Springer Nature eBook
Subject:	Fractional programming. -
Online resource:	https://doi.org/10.1007/978-981-19-6517-3
ISBN:	9789811965173

Optimal fractional-order predictive PI controllers = for process control applications with additional filtering /
Optimal fractional-order predictive PI controllersfor process control applications with additional filtering /[electronic resource] :by Arun Mozhi Devan Panneer Selvam ... [et al.]. - Singapore :Springer Nature Singapore :2022. - xviii, 146 p. :ill. (some col.), digital ;24 cm. - Studies in infrastructure and control,2730-6461. - Studies in infrastructure and control..

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 -- Development of the Proposed Arithmetic-Trigonometric Optimization Algorithm -- Appendix.

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.

ISBN: 9789811965173

Standard No.: 10.1007/978-981-19-6517-3doiSubjects--Topical Terms:

3607899
Fractional programming.

LC Class. No.: QA402.7

Dewey Class. No.: 515.83

Optimal fractional-order predictive PI controllers = for process control applications with additional filtering /
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245 0 0 $a Optimal fractional-order predictive PI controllers $h [electronic resource] : $b for process control applications with additional filtering / $c by Arun Mozhi Devan Panneer Selvam ... [et al.].
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300 $a xviii, 146 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in infrastructure and control, $x 2730-6461
505 0 $a 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 -- Development of the Proposed Arithmetic-Trigonometric Optimization Algorithm -- Appendix.
520 $a 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.
650 0 $a Fractional programming. $3 3607899
650 0 $a Filters (Mathematics) $3 556522
650 1 4 $a Control and Systems Theory. $3 3381515
650 2 4 $a Optimization. $3 891104
650 2 4 $a Algorithms. $3 536374
700 1 $a Panneer Selvam, Arun Mozhi Devan. $3 3607898
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Studies in infrastructure and control. $3 3495758
856 4 0 $u https://doi.org/10.1007/978-981-19-6517-3
950 $a Mathematics and Statistics (SpringerNature-11649)