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Economic Model Predictive Control Theory, Formulations and Chemical Process Applications

:	Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
:	Economic Model Predictive Control Theory, Formulations and Chemical Process Applications
:	Springer-Verlag
:	9783319411088
:	Advances in Industrial Control
:	1
:	CHF 124.00
:

:	Elektronik, Elektrotechnik, Nachrichtentechnik
:	English

:	311
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

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:

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

he 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 makingEconomic 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.

	Series Editors’ Foreword	6
	Preface	9
	Contents	11
	List of Figures	15
	List of Tables	23
	1 Introduction	25
	1.1 Motivation	25
	1.2 Tracking Versus Economic Model Predictive Control: A High-Level Overview	28
	1.3 Chemical Processes and Time-Varying Operation	30
	1.3.1 Catalytic Oxidation of Ethylene	31
	1.3.2 Continuously-Stirred Tank Reactor with Second-Order Reaction	34
	1.4 Objectives and Organization of the Book	39
	References	41
	2 Background on Nonlinear Systems, Control, and Optimization	44
	2.1 Notation	44
	2.2 Stability of Nonlinear Systems	45
	2.2.1 Lyapunov's Direct Method	48
	2.2.2 LaSalle's Invariance Principle	49
	2.3 Stabilization of Nonlinear Systems	50
	2.3.1 Control Lyapunov Functions	50
	2.3.2 Stabilization of Nonlinear Sampled-Data Systems	52
	2.3.3 Tracking Model Predictive Control	57
	2.3.4 Tracking Lyapunov-Based MPC	59
	2.4 Brief Review of Nonlinear and Dynamic Optimization	60
	2.4.1 Notation	61
	2.4.2 Definitions and Optimality Conditions	62
	2.4.3 Nonlinear Optimization Solution Strategies	65
	2.4.4 Dynamic Optimization	69
	References	76
	3 Brief Overview of EMPC Methods and Some Preliminary Results	79
	3.1 Background on EMPC Methods	79
	3.1.1 Class of Nonlinear Systems	79
	3.1.2 EMPC Methods	81
	3.2 Application of EMPC to a Chemical Process Example	89
	References	93
	4 Lyapunov-Based EMPC: Closed-Loop Stability, Robustness, and Performance	96
	4.1 Introduction	96
	4.2 Lyapunov-Based EMPC Design and Implementation	97
	4.2.1 Class of Nonlinear Systems	97
	4.2.2 Stabilizability Assumption	97
	4.2.3 LEMPC Formulation	98
	4.2.4 Implementation Strategy	101
	4.2.5 Satisfying State Constraints	102
	4.2.6 Extensions and Variants of LEMPC	104
	4.3 Closed-Loop Stability and Robustness Under LEMPC	106
	4.3.1 Synchronous Measurement Sampling	106
	4.3.2 Asynchronous and Delayed Sampling	112
	4.3.3 Application to a Chemical Process Example	117
	4.4 Closed-Loop Performance Under LEMPC	125
	4.4.1 Stabilizability Assumption	125
	4.4.2 Formulation and Implementation of the LEMPC with a Terminal Equality Constraint	126
	4.4.3 Closed-Loop Performance and Stability Analysis	127
	4.5 LEMPC with a Time-Varying Stage Cost	133
	4.5.1 Class of Economic Costs and Stabilizability Assumption	133
	4.5.2 The Union of the Stability Regions	134
	4.5.3 Formulation of LEMPC with Time-Varying Economic Cost	137
	4.5.4 Implementation Strategy	139
	4.5.5 Stability Analysis	140
	4.5.6 Application to a Chemical Process Example	142
	4.6 Conclusions	153