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.

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

Autorentext

Dr. Martin Klauco 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. Klauco 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.

Inhalt
Reference Governors.- Part I: Theory.- Mathematical Preliminaries and General Optimization.- Model Predictive Control.- Inner Loops with PID Controllers.- Inner Loops with Relay-Based Controllers.- Inner Loops with LQ Controllers.- Inner Loops with Model Predictive Controllers.- Part II: Case Studies.- Boiler-Turbine System.- Magnetic-Levitation Process.- Thermostatically Controlled Indoor Temperature.- Cascade Model Predictive Control of Chemical Reactors.- Conclusions and Future Work.