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Economic Model Predictive Control: Theory, Formulations and Chemical Process Applications 1st ed. 2017 [Hardback]

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  • Formāts: Hardback, 292 pages, height x width: 235x155 mm, weight: 6547 g, 16 Illustrations, color; 79 Illustrations, black and white; XXIV, 292 p. 95 illus., 16 illus. in color., 1 Hardback
  • Sērija : Advances in Industrial Control
  • Izdošanas datums: 09-Aug-2016
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319411071
  • ISBN-13: 9783319411071
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Economic Model Predictive Control: Theory, Formulations and Chemical Process Applications 1st ed. 2017Palielināt
  • Formāts: Hardback, 292 pages, height x width: 235x155 mm, weight: 6547 g, 16 Illustrations, color; 79 Illustrations, black and white; XXIV, 292 p. 95 illus., 16 illus. in color., 1 Hardback
  • Sērija : Advances in Industrial Control
  • Izdošanas datums: 09-Aug-2016
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319411071
  • ISBN-13: 9783319411071
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783319411071.html
Keywords:
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.

Recenzijas

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)

1 Introduction
1(20)
1.1 Motivation
1(3)
1.2 Tracking Versus Economic Model Predictive Control: A High-Level Overview
4(2)
1.3 Chemical Processes and Time-Varying Operation
6(9)
1.3.1 Catalytic Oxidation of Ethylene
7(3)
1.3.2 Continuously-Stirred Tank Reactor with Second-Order Reaction
10(5)
1.4 Objectives and Organization of the Book
15(6)
References
17(4)
2 Background on Nonlinear Systems, Control, and Optimization
21(36)
2.1 Notation
21(1)
2.2 Stability of Nonlinear Systems
22(5)
2.2.1 Lyapunov's Direct Method
25(1)
2.2.2 LaSalle's Invariance Principle
26(1)
2.3 Stabilization of Nonlinear Systems
27(10)
2.3.1 Control Lyapunov Functions
27(2)
2.3.2 Stabilization of Nonlinear Sampled-Data Systems
29(5)
2.3.3 Tracking Model Predictive Control
34(2)
2.3.4 Tracking Lyapunov-Based MPC
36(1)
2.4 Brief Review of Nonlinear and Dynamic Optimization
37(20)
2.4.1 Notation
38(1)
2.4.2 Definitions and Optimality Conditions
39(3)
2.4.3 Nonlinear Optimization Solution Strategies
42(4)
2.4.4 Dynamic Optimization
46(7)
References
53(4)
3 Brief Overview of EMPC Methods and Some Preliminary Results
57(18)
3.1 Background on EMPC Methods
57(10)
3.1.1 Class of Nonlinear Systems
57(2)
3.1.2 EMPC Methods
59(8)
3.2 Application of EMPC to a Chemical Process Example
67(8)
References
71(4)
4 Lyapunov-Based EMPC: Closed-Loop Stability, Robustness, and Performance
75(60)
4.1 Introduction
75(1)
4.2 Lyapunov-Based EMPC Design and Implementation
76(9)
4.2.1 Class of Nonlinear Systems
76(1)
4.2.2 Stabilizability Assumption
76(1)
4.2.3 LEMPC Formulation
77(3)
4.2.4 Implementation Strategy
80(1)
4.2.5 Satisfying State Constraints
81(2)
4.2.6 Extensions and Variants of LEMPC
83(2)
4.3 Closed-Loop Stability and Robustness Under LEMPC
85(19)
4.3.1 Synchronous Measurement Sampling
85(6)
4.3.2 Asynchronous and Delayed Sampling
91(5)
4.3.3 Application to a Chemical Process Example
96(8)
4.4 Closed-Loop Performance Under LEMPC
104(8)
4.4.1 Stabilizability Assumption
104(1)
4.4.2 Formulation and Implementation of the LEMPC with a Terminal Equality Constraint
105(1)
4.4.3 Closed-Loop Performance and Stability Analysis
106(6)
4.5 LEMPC with a Time-Varying Stage Cost
112(20)
4.5.1 Class of Economic Costs and Stabilizability Assumption
112(1)
4.5.2 The Union of the Stability Regions
113(3)
4.5.3 Formulation of LEMPC with Time-Varying Economic Cost
116(2)
4.5.4 Implementation Strategy
118(1)
4.5.5 Stability Analysis
119(2)
4.5.6 Application to a Chemical Process Example
121(11)
4.6 Conclusions
132(3)
References
132(3)
5 State Estimation and EMPC
135(36)
5.1 Introduction
135(2)
5.1.1 System Description
136(1)
5.1.2 Stabilizability Assumption
136(1)
5.2 High-Gain Observer-Based EMPC Scheme
137(16)
5.2.1 State Estimation via High-Gain Observer
139(1)
5.2.2 High-Gain Observer-Based EMPC
140(2)
5.2.3 Closed-Loop Stability Analysis
142(4)
5.2.4 Application to a Chemical Process Example
146(7)
5.3 RMHE-Based EMPC Scheme
153(16)
5.3.1 Observability Assumptions
155(1)
5.3.2 Robust MHE
155(2)
5.3.3 RMHE-Based EMPC
157(3)
5.3.4 Stability Analysis
160(5)
5.3.5 Application to a Chemical Process Example
165(4)
5.4 Conclusions
169(2)
References
169(2)
6 Two-Layer EMPC Systems
171(62)
6.1 Introduction
171(3)
6.1.1 Notation
172(2)
6.2 Two-Layer Control and Optimization Framework
174(17)
6.2.1 Class of Systems
174(1)
6.2.2 Formulation and Implementation
175(10)
6.2.3 Application to a Chemical Process
185(6)
6.3 Unifying Dynamic Optimization with Time-Varying Economics and Control
191(17)
6.3.1 Stabilizability Assumption
192(1)
6.3.2 Two-Layer EMPC Scheme Addressing Time-Varying Economics
193(8)
6.3.3 Application to a Chemical Process Example
201(7)
6.4 Addressing Closed-Loop Performance
208(22)
6.4.1 Class of Systems
209(1)
6.4.2 Stabilizability Assumption
210(1)
6.4.3 Two-Layer EMPC Structure
211(9)
6.4.4 Application to Chemical Process Example
220(10)
6.5 Conclusions
230(3)
References
231(2)
7 EMPC Systems: Computational Efficiency and Real-Time Implementation
233(58)
7.1 Introduction
233(1)
7.2 Economic Model Predictive Control of Nonlinear Singularly Perturbed Systems
234(18)
7.2.1 Class of Nonlinear Singularly Perturbed Systems
234(1)
7.2.2 Two-Time-Scale Decomposition
235(2)
7.2.3 Stabilizability Assumption
237(1)
7.2.4 LEMPC of Nonlinear Singularly Perturbed Systems
238(11)
7.2.5 Application to a Chemical Process Example
249(3)
7.3 Distributed EMPC: Evaluation of Sequential and Iterative Architectures
252(10)
7.3.1 Centralized EMPC
254(1)
7.3.2 Sequential DEMPC
255(3)
7.3.3 Iterative DEMPC
258(3)
7.3.4 Evaluation of DEMPC Approaches
261(1)
7.4 Real-Time Economic Model Predictive Control of Nonlinear Process Systems
262(25)
7.4.1 Class of Systems
264(1)
7.4.2 Real-Time LEMPC Formulation
265(1)
7.4.3 Implementation Strategy
266(4)
7.4.4 Stability Analysis
270(5)
7.4.5 Application to a Chemical Process Network
275(12)
7.5 Conclusions
287(4)
References
288(3)
Index 291
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.