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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 /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimal fractional-order predictive PI controllers/ by Arun Mozhi Devan Panneer Selvam ... [et al.].
其他題名:	for process control applications with additional filtering /
其他作者:	Panneer Selvam, Arun Mozhi Devan.
出版者:	Singapore :Springer Nature Singapore : : 2022.,
面頁冊數:	xviii, 146 p. :ill. (some col.), digital ;24 cm.
內容註:	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
標題:	Fractional programming. -
電子資源:	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

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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.
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