Overview
- Shows how model predictive control can easily be implemented in industrial reference governors
- Equips the practising control engineer with means of significantly improving performance of process operations
- Assists the student reader to understand the ideas of optimization and mathematical modelling
Part of the book series: Advances in Industrial Control (AIC)
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About this book
The first part of the monograph introduces the concept of optimization-based reference governors, provides an overview of the fundamentals of convex optimization and MPC, and discusses a rigorous design procedure for MPC-based reference governors. The design procedure depends on the type of lower-level controller involved and four practical cases are covered:
- PID lower-level controllers;
- linear quadratic regulators;
- relay-based controllers; and
- cases where the lower-level controllers are themselves model predictive controllers.
For each case the authors provide a thorough theoretical derivation of the corresponding reference governor, followed by illustrative examples.
The second part of the book is devoted to practical aspects of MPC-based reference governor schemes. Experimental and simulation case studies from four applications are discussed in depth:
- control of a power generation unit;
- temperature control in buildings;
- stabilization of objects in a magnetic field; and
- vehicle convoy control.
Each chapter includes precise mathematical formulations of the corresponding MPC-based governor, reformulation of the control problem into an optimization problem, and a detailed presentation and comparison of results.
The case studies and practical considerations of constraints will help control engineers working in various industries in the use of MPC at the supervisory level. The detailed mathematical treatments will attract the attention of academic researchers interested in the applications of MPC.
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Keywords
Table of contents (11 chapters)
Reviews
“The book, equipped with many numerical results, figures, tables and references, can be recommended for readers interested in feedback control, model predictive control methods and applications.” (Kurt Marti, zbMATH 1421.93001, 2019)
Authors and Affiliations
About the authors
Dr. Martin Klaučo received his first MSc. degree from the Denmark University of Technology in automatic control in 2012. The second MSc. degree obtained from process control in 2013 from the Slovak University of Technology in Bratislava. He graduated summa cum laude in 2017 at the Slovak University of Technology in Bratislava and obtained the Ph.D. degree from process control. Dr. M. Klaučo published 7 peer-reviewed current-contents papers and more than 15 conference papers in the field of optimal process control. His research is focused on optimal control methods and machine-learning-based control systems.
Associate Professor Michal Kvasnica received his diploma in process control from the Slovak University of Technology in Bratislava (STUBA), Slovakia in 2000 and Ph.D. in electrical engineering from the Swiss Federal Institute of Technology in Zurich, Switzerland in 2008. Since 2012 he is a tenured associate professor (docent) of automation at STUBA. In 2012 he was a visiting researcher at the Czech Technical University, Prague, Czech Republic. His research interests include decision making and control supported by artificial intelligence, embedded optimization and control, security and safety of cyber-physical systems, and control of human-in-the-loop systems. He is a co-author and the main developer of the MPT Toolbox for explicit model predictive control. His publication record includes 20 CC journal papers (including 9 in Automatica and IEEE Transactions), and more than 60 contributions in leading peer-reviewed international conferences. He has been a member of consortia for several EU-funded projects, including the EU FP7 ITN TEMPO project, and the EU FP6 project HYCON.Bibliographic Information
Book Title: MPC-Based Reference Governors
Book Subtitle: Theory and Case Studies
Authors: Martin Klaučo, Michal Kvasnica
Series Title: Advances in Industrial Control
DOI: https://doi.org/10.1007/978-3-030-17405-7
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-17404-0Published: 29 May 2019
Softcover ISBN: 978-3-030-17407-1Published: 15 August 2020
eBook ISBN: 978-3-030-17405-7Published: 21 May 2019
Series ISSN: 1430-9491
Series E-ISSN: 2193-1577
Edition Number: 1
Number of Pages: XXIII, 137
Number of Illustrations: 22 b/w illustrations, 24 illustrations in colour
Topics: Control and Systems Theory, Industrial Chemistry/Chemical Engineering, Manufacturing, Machines, Tools, Processes, Automotive Engineering