Ellis / Liu / Christofides Economic Model Predictive Control

Theory, Formulations and Chemical Process Applications
1. Auflage 2017
ISBN: 978-3-319-41108-8
Verlag: Springer Nature Switzerland
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 311 Seiten

Reihe: Advances in Industrial Control

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.

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.

Autoren/Hrsg.

Ellis, Matthew

Liu, Jinfeng

Christofides, Panagiotis D.

Inhaltsverzeichnis

1;Series Editors’ Foreword;6
2;Preface;9
3;Contents;11
4;List of Figures;15
5;List of Tables;23
6;1 Introduction;25
6.1;1.1 Motivation;25
6.2;1.2 Tracking Versus Economic Model Predictive Control: A High-Level Overview;28
6.3;1.3 Chemical Processes and Time-Varying Operation;30
6.3.1;1.3.1 Catalytic Oxidation of Ethylene;31
6.3.2;1.3.2 Continuously-Stirred Tank Reactor with Second-Order Reaction;34
6.4;1.4 Objectives and Organization of the Book;39
6.5;References;41
7;2 Background on Nonlinear Systems, Control, and Optimization;44
7.1;2.1 Notation;44
7.2;2.2 Stability of Nonlinear Systems;45
7.2.1;2.2.1 Lyapunov's Direct Method;48
7.2.2;2.2.2 LaSalle's Invariance Principle;49
7.3;2.3 Stabilization of Nonlinear Systems;50
7.3.1;2.3.1 Control Lyapunov Functions;50
7.3.2;2.3.2 Stabilization of Nonlinear Sampled-Data Systems;52
7.3.3;2.3.3 Tracking Model Predictive Control;57
7.3.4;2.3.4 Tracking Lyapunov-Based MPC;59
7.4;2.4 Brief Review of Nonlinear and Dynamic Optimization;60
7.4.1;2.4.1 Notation;61
7.4.2;2.4.2 Definitions and Optimality Conditions;62
7.4.3;2.4.3 Nonlinear Optimization Solution Strategies;65
7.4.4;2.4.4 Dynamic Optimization;69
7.5;References;76
8;3 Brief Overview of EMPC Methods and Some Preliminary Results;79
8.1;3.1 Background on EMPC Methods;79
8.1.1;3.1.1 Class of Nonlinear Systems;79
8.1.2;3.1.2 EMPC Methods;81
8.2;3.2 Application of EMPC to a Chemical Process Example;89
8.3;References;93
9;4 Lyapunov-Based EMPC: Closed-Loop Stability, Robustness, and Performance;96
9.1;4.1 Introduction;96
9.2;4.2 Lyapunov-Based EMPC Design and Implementation;97
9.2.1;4.2.1 Class of Nonlinear Systems;97
9.2.2;4.2.2 Stabilizability Assumption;97
9.2.3;4.2.3 LEMPC Formulation;98
9.2.4;4.2.4 Implementation Strategy;101
9.2.5;4.2.5 Satisfying State Constraints;102
9.2.6;4.2.6 Extensions and Variants of LEMPC;104
9.3;4.3 Closed-Loop Stability and Robustness Under LEMPC;106
9.3.1;4.3.1 Synchronous Measurement Sampling;106
9.3.2;4.3.2 Asynchronous and Delayed Sampling;112
9.3.3;4.3.3 Application to a Chemical Process Example;117
9.4;4.4 Closed-Loop Performance Under LEMPC;125
9.4.1;4.4.1 Stabilizability Assumption;125
9.4.2;4.4.2 Formulation and Implementation of the LEMPC with a Terminal Equality Constraint;126
9.4.3;4.4.3 Closed-Loop Performance and Stability Analysis;127
9.5;4.5 LEMPC with a Time-Varying Stage Cost;133
9.5.1;4.5.1 Class of Economic Costs and Stabilizability Assumption;133
9.5.2;4.5.2 The Union of the Stability Regions;134
9.5.3;4.5.3 Formulation of LEMPC with Time-Varying Economic Cost;137
9.5.4;4.5.4 Implementation Strategy;139
9.5.5;4.5.5 Stability Analysis;140
9.5.6;4.5.6 Application to a Chemical Process Example;142
9.6;4.6 Conclusions;153
9.7;References;153
10;5 State Estimation and EMPC;155
10.1;5.1 Introduction;155
10.1.1;5.1.1 System Description;156
10.1.2;5.1.2 Stabilizability Assumption;156
10.2;5.2 High-Gain Observer-Based EMPC Scheme;157
10.2.1;5.2.1 State Estimation via High-Gain Observer;159
10.2.2;5.2.2 High-Gain Observer-Based EMPC;160
10.2.3;5.2.3 Closed-Loop Stability Analysis;162
10.2.4;5.2.4 Application to a Chemical Process Example;166
10.3;5.3 RMHE-Based EMPC Scheme;173
10.3.1;5.3.1 Observability Assumptions;174
10.3.2;5.3.2 Robust MHE;175
10.3.3;5.3.3 RMHE-Based EMPC;177
10.3.4;5.3.4 Stability Analysis;180
10.3.5;5.3.5 Application to a Chemical Process Example;185
10.4;5.4 Conclusions;189
10.5;References;189
11;6 Two-Layer EMPC Systems;191
11.1;6.1 Introduction;191
11.1.1;6.1.1 Notation;192
11.2;6.2 Two-Layer Control and Optimization Framework;194
11.2.1;6.2.1 Class of Systems;194
11.2.2;6.2.2 Formulation and Implementation;195
11.2.3;6.2.3 Application to a Chemical Process;205
11.3;6.3 Unifying Dynamic Optimization with Time-Varying Economics and Control;211
11.3.1;6.3.1 Stabilizability Assumption;212
11.3.2;6.3.2 Two-Layer EMPC Scheme Addressing Time-Varying Economics;213
11.3.3;6.3.3 Application to a Chemical Process Example;221
11.4;6.4 Addressing Closed-Loop Performance;228
11.4.1;6.4.1 Class of Systems;229
11.4.2;6.4.2 Stabilizability Assumption;230
11.4.3;6.4.3 Two-Layer EMPC Structure;231
11.4.4;6.4.4 Application to Chemical Process Example;240
11.5;6.5 Conclusions;250
11.6;References;251
12;7 EMPC Systems: Computational Efficiency and Real-Time Implementation;253
12.1;7.1 Introduction;253
12.2;7.2 Economic Model Predictive Control of Nonlinear Singularly Perturbed Systems;254
12.2.1;7.2.1 Class of Nonlinear Singularly Perturbed Systems;254
12.2.2;7.2.2 Two-Time-Scale Decomposition;255
12.2.3;7.2.3 Stabilizability Assumption;257
12.2.4;7.2.4 LEMPC of Nonlinear Singularly Perturbed Systems;258
12.2.5;7.2.5 Application to a Chemical Process Example;269
12.3;7.3 Distributed EMPC: Evaluation of Sequential and Iterative Architectures;272
12.3.1;7.3.1 Centralized EMPC;274
12.3.2;7.3.2 Sequential DEMPC;275
12.3.3;7.3.3 Iterative DEMPC;278
12.3.4;7.3.4 Evaluation of DEMPC Approaches;281
12.4;7.4 Real-Time Economic Model Predictive Control of Nonlinear Process Systems;282
12.4.1;7.4.1 Class of Systems;284
12.4.2;7.4.2 Real-Time LEMPC Formulation;285
12.4.3;7.4.3 Implementation Strategy;286
12.4.4;7.4.4 Stability Analysis;290
12.4.5;7.4.5 Application to a Chemical Process Network;295
12.5;7.5 Conclusions;307
12.6;References;308
13;Index;310