Economic Model Predictive Control

1. Front Matter

   Pages i-xxiv

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2. Introduction

   • Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides

   Pages 1-19

3. Background on Nonlinear Systems, Control, and Optimization

   • Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides

   Pages 21-55

4. Brief Overview of EMPC Methods and Some Preliminary Results

   • Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides

   Pages 57-73

5. Lyapunov-Based EMPC: Closed-Loop Stability, Robustness, and Performance

   • Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides

   Pages 75-133

6. State Estimation and EMPC

   • Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides

   Pages 135-170

7. Two-Layer EMPC Systems

   • Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides

   Pages 171-232

8. EMPC Systems: Computational Efficiency and Real-Time Implementation

   • Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides

   Pages 233-289

9. Back Matter

   Pages 291-292

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This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key theoretical and practical considerations including recursive feasibility, closed-loop stability, closed-loop performance, and computational efficiency.  Specifically, the book proposes:

• Lyapunov-based EMPC methods for nonlinear systems;
•  two-tier EMPC architectures that are highly computationally efficient; and
•  EMPC schemes handling explicitly uncertainty, time-varying cost functions, time-delays and multiple-time-scale dynamics.

The proposed methods employ a variety of tools ranging from nonlinear systems analysis, through Lyapunov-based control techniques to nonlinear dynamic optimization. The applicability and performance of the proposed methods are demonstrated through a number of chemical process examples.

The book presents state-of-the-art methods for the design of economic model predictive control systems for chemical processes.In addition to being mathematically rigorous, these methods accommodate key practical issues, for example, direct optimization of process economics, time-varying economic cost functions and computational efficiency. Numerous comments and remarks providing fundamental understanding of the merging of process economics and feedback control into a single framework are included. A control engineer can easily tailor the many detailed examples of industrial relevance given within the text to a specific application.

The authors present a rich collection of new research topics and references to significant recent work making Economic Model Predictive Control an important source of information and inspiration for academics and graduate students researching the area and for process engineers interested in applying its ideas.

• Model Predictive Control
• Process Economic Optimization
• Nonlinear Systems
• Computational Efficiency
• Time-varying Cost Function
• Time-delay Systems
• Multiple-time-scale Dynamics

“The reviewed book deals with stability and performance analysis of nonlinear control systems under economic model predictive control (EMPC). … the book builds a bridge between the theory and practice and provides an excellent balance between theoretical results and their application-specific implementation.” (Petro Feketa, zbMATH 1405.93004, 2019)

“This book presents a comprehensive introduction to the topic of economic model predictive control (EMPC). … Every chapter contains illustrations of the presented results though applications to chemical process control.” (Dante Kalise, Mathematical Reviews, February, 2019)

• Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, USA

  Matthew Ellis, Panagiotis D. Christofides

• Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Canada

  Jinfeng Liu

Dr. Liu received the BS and MS degrees in Control Science and Engineering from Zhejiang University in 2003 and 2006, respectively. He received the PhD degree in Chemical Engineering from the University of California, Los Angeles in 2011. Before joining the University of Alberta in April, 2012, Dr. Liu was a postdoctoral researcher at the University of California, Los Angeles. His research interests are in the general areas of process control theory and practice with emphasis on model predictive control, networked and distributed control, process monitoring, and real-time control of chemical processes and energy generation systems.

Professor Panagiotis Christofides obtained his PhD from the University of Minnesota in 1996 and he has been a professor at the University of California, Los Angeles since 2004. He is a fellow of various professional societies:  the American Association for the Advancement of Science, the International Federation of Automatic Control and the IEEE.He is the author of numerous research papers, as well as two previous books published by Springer and has much experience of conference organization having served on various boards at various times, among them as the AIChE Director on the American Automatic Control Council. 

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