Optimal Fractional-order Predictive Pi Controllers: For Process Control Applications With Additional Filtering - Rilegato

Selvam, Arun Mozhi Devan Panneer; Hussin, Fawnizu Azmadi; Ibrahim, Rosdiazli; Bingi, Kishore; M., Nagarajapandian

Rilegato

ISBN 10: 9811965161 ISBN 13: 9789811965166

Casa editrice: Springer Nature, 2022

Riassunto

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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Informazioni sull?autore

P. ARUN MOZHI DEVAN received the B.Eng. degree (Hons.) in electronics and instrumentation engineering from the Muthayammal Engineering College, Rasipuram, Tamil Nadu, India, in 2012, and the M.Eng. degree (Hons.) in Control and Instrumentation Engineering from Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India, in 2016. He is currently pursuing the Ph.D. degree with the Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Perak, Malaysia. He was with Sri Ramakrishna Engineering College as an Assistant Professor (O.G) in the Department of Electronics and Instrumentation Engineering from 2016 to 2018. His current research interests include Fractional-order Control, wireless networked control systems, process control and optimization. 

 

FAWNIZU AZMADI HUSSIN received the bachelor’s degree in Electrical engineering from the University of Minnesota, Twin Cities, Minneapolis, MN, USA, in 1999, the M.Eng. Sc. degree in Systems and Control from the University of New South Wales, Sydney, NSW, Australia, in 2001, and the Ph.D. degree in core based testing of system-on-a-chip (SoCs) from the Nara Institute of Science and Technology, Ikoma, Japan, in 2008, under the scholarship from the Japanese Government (Monbukagakusho). He is currently an Associate Professor in Electrical and Electronics Engineering at Universiti Teknologi PETRONAS, He was the Program Manager of Master by coursework program (2009-2013), the Deputy Head of Electrical & Electronic Engineering department (2013-2014) and the Director of Strategic Alliance Office (2014-2018) at UTP. He spent one year as a Visiting Professor at Intel Microelectronics (Malaysia)’s SOC DFx department in 2012-13. He is actively involved with the IEEE Malaysia Section as volunteers since 2009. He was the 2013 & 2014 Chair of the IEEE Circuits and Systems Society Malaysia Chapter and currently serving as the Chair of IEEE Malaysia Section (2019 & 2020)

 

ROSDIAZLI IBRAHIM received the B.Eng. degree (Hons.) in Electrical Engineering from Universiti Putra Malaysia, Kembangan, Malaysia, in 1996, the M.Sc. degree (Hons.) in Automation and Control from Newcastle University, Newcastle upon Tyne, U.K., in 2000, and the Ph.D. degree in Electrical and Electronic Engineering from the University of Glasgow, U.K., in 2008. He is currently an Associate Professor with the Department of Electrical and Electronics Engineering, Universiti Teknologi Petronas (UTP), Seri Iskandar, Perak, Malaysia. He is currently the Dean with the Centre of Graduate Studies at UTP. His current research interests include intelligent control and non-linear multi-variable process modelling for control application. He is a Registered Engineer with the Board of Engineering Malaysia.

 

KISHORE BINGI received the B.Tech. degree in Electrical and Electronics Engineering from Bapatla Engineering College, Andhra Pradesh, India, in 2012, the M.Tech. degree in Instrumentation and Control Systems from National Institute of Technology (NIT) Calicut, Kerala, India, in 2014, and the Ph.D. degree in the Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS (UTP), Perak, Malaysia in 2019. He worked as a Research Scientist and Post-doctoral researcher in the Institute of Autonomous Systems, Universiti Teknologi PETRONAS, Perak, Malaysia from 2019 to 2020. He also worked with TATA Consultancy Service (TCS) as an Assistant Systems Engineer from 2014 to 2015. He is currently an Assistant Professor (Senior Grade) in the Department of Control & Automation, School of Electrical Engineering (SELECT), Vellore Institute of Technology, Vellore, India. His current research interests include Non-linear Process Modeling, Fractional-order Control and Optimization.

 

M. NAGARAJAPANDIAN received his B.Eng. degree (Hons.) in Electronics and Instrumentation Engineering from Arunai Engineering College, Tiruvannamalai in 2010, and M.Eng. degree (Hons.) degree in Applied Electronics from Shri Andal Alagar College of Engineering, in 2012. He is currently an Assistant Professor in the Department of Electronics and Instrumentation Engineering, Sri Ramakrishna Engineering College, Coimbatore. He has ten years of experience as an Assistant Professor. Also, he is currently pursuing the Ph.D. degree with the Electrical and Electronic Engineering Department, PSG College of Technology, Coimbatore, Tamil Nadu, India. His areas of research interest are Control Systems, Non-linear control and Process Control Optimization.

Dalla quarta di copertina

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.

Dettagli dell?edizione

• Editore: Springer Nature
• Data di pubblicazione: 2022
• ISBN 10: 9811965161
• ISBN 13: 9789811965166
• Rilegatura: Copertina rigida
• Lingua: Inglese
• Numero edizione: 1
• Numero di pagine: 164