MPC-Based Reference Governors

1. Front Matter

   Pages i-xxiii

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2. Reference Governors

   • Martin Klaučo, Michal Kvasnica

   Pages 1-5

3. Theory

   1. Front Matter

      Pages 7-7

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   2. Mathematical Preliminaries and General Optimization

      • Martin Klaučo, Michal Kvasnica

      Pages 9-14

   3. Model Predictive Control

      • Martin Klaučo, Michal Kvasnica

      Pages 15-34

   4. Inner Loops with PID Controllers

      • Martin Klaučo, Michal Kvasnica

      Pages 35-43

   5. Inner Loops with Relay-Based Controllers

      • Martin Klaučo, Michal Kvasnica

      Pages 45-52

   6. Inner Loops with Model Predictive Control Controllers

      • Martin Klaučo, Michal Kvasnica

      Pages 53-68

4. Case Studies

   1. Front Matter

      Pages 69-69

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   2. Boiler–Turbine System

      • Martin Klaučo, Michal Kvasnica

      Pages 71-89

   3. Magnetic Levitation Process

      • Martin Klaučo, Michal Kvasnica

      Pages 91-101

   4. Thermostatically Controlled Indoor Temperature

      • Martin Klaučo, Michal Kvasnica

      Pages 103-112

   5. Cascade MPC of Chemical Reactors

      • Martin Klaučo, Michal Kvasnica

      Pages 113-132

   6. Conclusions and Future Research

      • Martin Klaučo, Michal Kvasnica

      Pages 133-135

5. Back Matter

   Pages 137-137

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This monograph focuses on the design of optimal reference governors using model predictive control (MPC) strategies. These MPC-based governors serve as a supervisory control layer that generates optimal trajectories for lower-level controllers such that the safety of the system is enforced while optimizing the overall performance of the closed-loop system.

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.

• Reference Governors
• Constraint Handling
• Predictive Reference Governors
• Command Governors
• Extended Command Governors
• Model Predictive Control
• Mixed-integer Optimization
• Optimal Reference Governors

“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)

• Institute of Information Engineering, Automation, and Mathematics, Slovak University of Technology in Bratislava, Bratislava, Slovakia

  Martin Klaučo, Michal Kvasnica

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.

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