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E-raamat: Handbook of Model Predictive Control

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  • ISBN-13: 9783319774893
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  • Formaat: EPUB+DRM
  • Sari: Control Engineering
  • Ilmumisaeg: 01-Sep-2018
  • Kirjastus: Birkhauser Verlag AG
  • Keel: eng
  • ISBN-13: 9783319774893
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Recent developments in model-predictive control promise remarkable opportunities for designing multi-input, multi-output control systems and improving the control of single-input, single-output systems. This volume provides a definitive survey of the latest model-predictive control methods available to engineers and scientists today.

The initial set of chapters present various methods for managing uncertainty in systems, including stochastic model-predictive control. With the advent of affordable and fast computation, control engineers now need to think about using “computationally intensive controls,” so the second part of this book addresses the solution of optimization problems in “real” time for model-predictive control. The theory and applications of control theory often influence each other, so the last section of Handbook of Model Predictive Control rounds out the book with representative applications to automobiles, healthcare, robotics, and finance.

The chapters in this volume will be useful to working engineers, scientists, and mathematicians, as well as students and faculty interested in the progression of control theory. Future developments in MPC will no doubt build from concepts demonstrated in this book and anyone with an interest in MPC will find fruitful information and suggestions for additional reading.


Arvustused

This handbook is designed for a wide audience. It is an excellent reference for graduate students, researchers, and practitioners in the field of control systems and numerical optimization who want to understand the potential, challenges, and benefits of MPC and its applications. This handbook enables the reader to gain a panoramic viewpoint of MPC theory and practice as well as provides a state-of-the-art overview of new and exciting areas of application at the forefront of MPC research. (Gabriele Pannocchia, IEEE Control Systems Magazine, Vol. 40 (5), October, 2020)

Part I Theory
The Essentials of Model Predictive Control
3(26)
William S. Levine
1 Introduction
3(1)
2 Background
4(8)
2.1 Intuition
4(1)
2.2 History
5(7)
3 Basics of Model Predictive Control (MPC)
12(3)
4 Stability, of MPC
15(2)
5 Exogenotis Inputs
17(2)
6 Robustness
19(1)
7 Example
20(6)
7.1 Background
21(1)
7.2 Dynamics
21(2)
7.3 Delay
23(1)
7.4 Performance Measure
23(1)
7.5 Noise and Other Disturbances
24(1)
7.6 Problem
24(1)
7.7 Solution
24(1)
7.8 Results
25(1)
7.9 Discussion
26(1)
8 Conclusions
26(1)
References
26(3)
Dynamic Programming, Optimal Control and Model Predictive Control
29(24)
Lars Grune
1 Introduction
29(1)
2 Setting, Definitions and Notation
30(3)
3 Dynamic Programming
33(2)
4 Stabilizing MPC
35(5)
4.1 Terminal Conditions
36(1)
4.2 No Terminal Conditions
37(3)
5 Economic MPC
40(11)
5.1 Terminal Conditions
41(2)
5.2 No Terminal Conditions
43(8)
6 Conclusions
51(1)
References
51(2)
Set-Valued and Lyapunov Methods for MPC
53(22)
Rafal Goebel
Sasa V. Rakovic
1 Introduction
53(1)
2 Problem Statement and Assumptions
54(3)
2.1 Open Loop Optimal Control Problem
54(2)
2.2 Closed Loop Dynamics
56(1)
2.3 Standing Assumptions
56(1)
3 Properties of the Open Loop Optimal Control Problem
57(5)
3.1 Set-Valued Analysis Background
57(1)
3.2 Parametric Optimization Background
58(2)
3.3 Existence and Structure of Optimal Solutions
60(2)
4 Asymptotic Stability and Related Issues
62(6)
4.1 Strong Positive Invariance (a.k.a. Recursive Feasibility)
63(1)
4.2 Strong Lyapunov Decrease (a.k.a. Cost Reduction)
64(1)
4.3 Strong Positive Invariance and Strong Asymptotic Stability
65(1)
4.4 Set-Valued Approach to Robustness of Asymptotic Stability
66(1)
4.5 Consistent Improvement
67(1)
5 Set-Valued Control Systems
68(4)
5.1 Weak Formulation of MPC
69(2)
5.2 Strong Formulation of MPC
71(1)
References
72(3)
Stochastic Model Predictive Control
75(24)
Ali Mesbah
Ilya V. Kolmanovsky
Stefano Di Cairano
1 Introduction
75(1)
2 Stochastic Optimal Control and MPC with Chance Constraints
76(2)
3 Scenario Tree-Based MPC
78(6)
3.1 Scenario-Tree Construction
79(2)
3.2 Scenario-Tree Stochastic Optimization Problem
81(1)
3.3 Extensions and Applications
82(2)
4 Polynomial Chaos-Based MPC
84(6)
4.1 System Model, Constraints, and Control Input Parameterization
84(1)
4.2 Generalized Polynomial Chaos for Uncertainty Propagation
85(3)
4.3 Moment-Based Surrogate for Joint Chance Constraint
88(1)
4.4 Sample-Free, Moment-Based SMPC Formulation
89(1)
4.5 Extensions
90(1)
5 Stochastic Tube MPC
90(5)
5.1 System Model, Disturbance Model and Constraints
90(1)
5.2 Tube MPC Design
91(2)
5.3 Theoretical Guarantees
93(1)
5.4 Mass-Spring-Damper Example
94(1)
5.5 Extensions
94(1)
References
95(4)
Moving Horizon Estimation
99(26)
Douglas A. Allan
James B. Rawlings
1 Introduction
99(4)
2 Systems of Interest
103(3)
3 MHE Setup
106(4)
4 Main Results
110(3)
5 Numerical Example
113(3)
6 Conclusions
116(6)
References
122(3)
Probing and Duality in Stochastic Model Predictive Control
125(20)
Martin A. Sehr
Robert R. Bitmead
1 Introduction
125(1)
2 Stochastic Optimal Control and Duality
126(2)
2.1 The State, the Information State, and the Bayesian Filter
126(1)
2.2 Stochastic Optimal Control and the Information State
127(1)
2.3 Duality and the Source of Intractability
128(1)
3 Stochastic MPC and Deterministic MPC
128(1)
4 Stochastic Reconstructibility and Its Dependence on Control
129(4)
4.1 Linear Regression and the Cramer-Rao Lower Bound
130(1)
4.2 Conditional Entropy Measure of Reconstructibility
131(2)
5 Three Examples of Dualized Stochastic Control
133(6)
5.1 Internet Congestion Control in TCP/IP
133(1)
5.2 Equalization in Cellular Wireless
134(3)
5.3 Experiment Design in Linear Regression for MPC
137(2)
6 Tractable Compromise Dualized Stochastic MPC Algorithms
139(3)
6.1 Non-dual Approaches
140(1)
6.2 Dual Optimal POMDPs
141(1)
7 Conclusion
142(1)
References
143(2)
Economic Model Predictive Control: Some Design Tools and Analysis Techniques
145(24)
David Angeli
Matthias A. Muller
1 Model-Based Control and Optimization
145(3)
2 Formulation of Economic Model Predictive Control
148(3)
3 Properties of Economic MPC
151(10)
3.1 Recursive Feasibility
151(2)
3.2 Asymptotic Average Cost
153(3)
3.3 Stability of Economic MPC
156(4)
3.4 EMPC Without Terminal Ingredients
160(1)
4 EMPC with Constraints on Average
161(1)
5 Robust Economic Model Predictive Control
162(2)
6 Conclusions
164(1)
References
165(4)
Nonlinear Predictive Control for Trajectory Tracking and Path Following: An Introduction and Perspective
169(30)
Janine Matschek
Tobias Bathge
Timm Faulwasser
Rolf Findeisen
1 Introduction and Motivation
170(3)
2 Setpoint Stabilization, Trajectory Tracking, Path Following, and Economic Objectives
173(4)
2.1 Setpoint Stabilization
173(1)
2.2 Trajectory Tracking
174(1)
2.3 Path Following
175(2)
2.4 Economic Objectives
177(1)
3 A Brief Review of MPC for Setpoint Stabilization
177(4)
3.1 Comments on Convergence and Stability
179(1)
3.2 Setpoint Stabilization of a Lightweight Robot
180(1)
4 Model Predictive Control for Trajectory Tracking
181(2)
4.1 Convergence and Stability of Tracking NMPC
182(1)
4.2 Trajectory-Tracking Control of a Lightweight Robot
183(1)
5 Model Predictive Control for Path Following
183(9)
5.1 Convergence and Stability of Output Path-Following NMPC
185(1)
5.2 Path-Following Control of a Lightweight Robot
186(5)
5.3 Extensions of Path Following
191(1)
6 Economic MPC
192(2)
6.1 Convergence and Stability of Economic MPC
193(1)
7 Conclusions and Perspectives
194(1)
References
195(4)
Hybrid Model Predictive Control
199(22)
Ricardo G. Sanfelice
1 Summary
199(1)
2 Hybrid Model Predictive Control
200(15)
2.1 Discrete-Time MPC for Discrete-Time Systems with Discontinuous Right-Hand Sides
201(2)
2.2 Discrete-Time MPC for Discrete-Time Systems with Mixed States
203(1)
2.3 Discrete-Time MPC for Discrete-Time Systems Using Memory and Logic Variables
204(4)
2.4 Periodic Continuous-Discrete MPC for Continuous-Time Systems
208(3)
2.5 Periodic Continuous-Time MPC for Continuous-Time Systems Combined with Local Static State-Feedback Controllers
211(1)
2.6 Periodic Discrete-Time MPC for Continuous-Time Linear Systems with Impulses
212(3)
3 Towards MPC for Hybrid Dynamical Systems
215(3)
4 Further Reading
218(1)
References
218(3)
Model Predictive Control of Polynomial Systems
221(18)
Eranda Harinath
Lucas C. Foguth
Joel A. Paulson
Richard D. Braatz
1 Introduction
221(1)
2 Model Predictive Control of Discrete-Time Polynomial Systems
222(2)
3 Polynomial Optimization Methods
224(3)
3.1 Sum-of-Squares Decomposition
225(1)
3.2 Dual Approach via SOS Decomposition
225(2)
4 Fast Solution Methods for Polynomial MPC
227(3)
4.1 Convex MPC for a Subclass of Polynomial Systems
227(1)
4.2 Explicit MPC Using Algebraic Geometry Methods
228(2)
5 Taylor Series Approximations for Non-polynomial Systems
230(3)
5.1 Taylor's Theorem
230(1)
5.2 Example
231(2)
6 Outlook for Future Research
233(2)
References
235(4)
Distributed MPC for Large-Scale Systems
239(20)
Marcello Farina
Riccardo Scattolini
1 Introduction and Motivations
239(2)
2 Model and Control Problem Decomposition
241(6)
2.1 Model Decomposition
241(3)
2.2 Partition Properties and Control
244(1)
2.3 MPC Problem Separability
245(2)
3 Decentralized MPC
247(1)
4 Distributed MPC
248(7)
4.1 Cooperating DMPC
248(2)
4.2 Non-cooperating Robustness-Based DMPC
250(2)
4.3 Distributed Control of Independent Systems
252(1)
4.4 Distributed Optimization
253(2)
5 Extensions and Applications
255(1)
6 Conclusions and Future Perspectives
256(1)
References
256(3)
Scalable MPC Design
259(28)
Marcello Farina
Giancarlo Ferrari-Trecate
Colin Jones
Stefano Riverso
Melanie Zeilinger
1 Introduction and Motivations
259(1)
2 Scalable and Plug-and-Play Design
260(3)
3 Concepts Enabling Scalable Design for Constrained Systems
263(5)
3.1 Tube-Based Small-Gain Conditions for Networks
263(3)
3.2 Distributed Invariance
266(2)
4 Scalable Design of MPC
268(6)
4.1 PnP-MPC Based on Robustness Against Coupling
268(3)
4.2 PnP-MPC Based on Distributed Invariance
271(3)
5 Generalizations and Related Approaches
274(2)
6 Applications
276(4)
6.1 Frequency Control in Power Networks
276(2)
6.2 Electric Vehicle Charging in Smart Grids
278(2)
7 Conclusions and Perspectives
280(1)
References
281(6)
Part II Computations
Efficient Convex Optimization for Linear MPC
287(18)
Stephen J. Wright
1 Introduction
287(1)
2 Formulating and Solving LQR
288(1)
3 Convex Quadratic Programming
289(3)
4 Linear MPC Formulations and Interior-Point Implementation
292(5)
4.1 Linear MPC Formulations
292(2)
4.2 KKT Conditions and Efficient Interior-Point Implementation
294(3)
5 Parametrized Convex Quadratic Programming
297(5)
5.1 Enumeration
298(1)
5.2 Active-Set Strategy
299(3)
6 Software
302(1)
References
302(3)
Implicit Non-convex Model Predictive Control
305(30)
Sebastien Gros
1 Introduction
305(2)
2 Parametric Nonlinear Programming
307(1)
3 Solution Approaches to Nonlinear Programming
308(3)
3.1 SQP
309(1)
3.2 Interior-Point Methods
310(1)
4 Discretization
311(4)
4.1 Single Shooting Methods
312(1)
4.2 Multiple Shooting Methods
313(1)
4.3 Direct Collocation Methods
314(1)
5 Predictors & Path-Following
315(10)
5.1 Parametric Embedding
317(2)
5.2 Path Following Methods
319(2)
5.3 Real-Time Dilemma: Should We Converge the Solutions?
321(2)
5.4 Shifting
323(1)
5.5 Convergence of Path-Following Methods
324(1)
6 Sensitivities & Hessian Approximation
325(2)
7 Structures
327(2)
8 Summary
329(1)
References
330(5)
Convexification and Real-Time Optimization for MPC with Aerospace Applications
335(24)
Yuanqi Mao
Daniel Dueri
Michael Szmuk
Behget Acikmese
1 Introduction
335(2)
2 Convexification
337(15)
2.1 Lossless Convexification of Control Constraints
338(7)
2.2 Successive Convexification
345(7)
3 Real-Time Computation
352(3)
4 Concluding Remarks
355(1)
References
356(3)
Explicit (Offline) Optimization for MPC
359(28)
Nikolaos A. Diangelakis
Richard Oberdieck
Efstratios N. Pistikopoulos
1 Introduction
359(4)
1.1 From State-Space Models to Multi-Parametric Programming
359(4)
1.2 When Discrete Elements Occur
363(1)
2 Multi-Parametric Linear and Quadratic Programming: An Overview
363(10)
2.1 Theoretical Properties
364(3)
2.2 Degeneracy
367(2)
2.3 Solution Algorithms for mp-LP and mp-QP Problems
369(4)
3 Multi-Parametric Mixed-Integer Linear and Quadratic Programming: An Overview
373(6)
3.1 Theoretical Properties
373(2)
3.2 Solution Algorithms
375(2)
3.3 The Decomposition Algorithm
377(2)
4 Discussion and Concluding Remarks
379(3)
4.1 Size of Multi-Parametric Programming Problem and Offline Computational Effort
379(1)
4.2 Size of the Solution and Online Computational Effort
380(1)
4.3 Other Developments in Explicit MPC
381(1)
References
382(5)
Real-Time Implementation of Explicit Model Predictive Control
387(26)
Michal Kvasnica
Colin N. Jones
Ivan Pejcic
Juraj Holaza
Milan Korda
Peter Bakarac
1 Simplification of MPC Feedback Laws
387(4)
1.1 Preliminaries
387(2)
1.2 Complexity of Explicit MPC
389(1)
1.3 Problem Statement and Main Results
390(1)
2 Piecewise Affine Explicit MPC Controllers of Reduced Complexity
391(9)
2.1 Clipping-Based Explicit MPC
391(3)
2.2 Regionless Explicit MPC
394(3)
2.3 Piecewise Affine Approximation of Explicit MPC
397(3)
3 Approximation of MPC Feedback Laws for Nonlinear Systems
400(10)
3.1 Problem Setup
400(1)
3.2 A QP-Based MPC Controller
401(1)
3.3 Stability Verification
402(3)
3.4 Closed-Loop Performance
405(1)
3.5 Parameter Tuning
406(2)
3.6 Numerical Example
408(2)
References
410(3)
Robust Optimization for MPC
413(32)
Boris Houska
Mario E. Villanueva
1 Introduction
413(1)
2 Problem Formulation
414(4)
2.1 Inf-Sup Feedback Model Predictive Control
415(1)
2.2 Set-Based Robust Model Predictive Control
416(2)
2.3 Numerical Challenges
418(1)
3 Convex Approximations for Robust MPC
418(5)
3.1 Ellipsoidal Approximation Using LMIs
419(2)
3.2 Affine Disturbance Feedback
421(2)
4 Generic Methods for Robust MPC
423(5)
4.1 Inf-Sup Dynamic Programming
424(2)
4.2 Scenario-Tree MPC
426(1)
4.3 Tube MPC
427(1)
5 Numerical Methods for Tube MPC
428(5)
5.1 Feedback Parametrization
428(1)
5.2 Affine Set-Parametrizations
429(2)
5.3 Tube MPC Parametrization
431
5.4 Tube MPC Via Min-Max Differential Inequalities
411(22)
6 Numerical Aspects: Modern Set-Valued Computing
433(6)
6.1 Factorable Functions
433(2)
6.2 Set Arithmetics
435(2)
6.3 Set-Valued Integrators
437(2)
7 Conclusions
439(1)
References
440(5)
Scenario Optimization for MPC
445(20)
Marco C. Campi
Simone Garatti
Maria Prandini
1 Introduction
445(1)
2 Stochastic MPC and the Use of the Scenario Approach
446(2)
3 Fundamentals of Scenario Optimization
448(3)
4 The Scenario Approach for Solving Stochastic MPC
451(5)
5 Numerical Example
456(4)
6 Extensions and Future Work
460(1)
References
461(4)
Nonlinear Programming Formulations for Nonlinear and Economic Model Predictive Control
465(28)
Mingzhao Yu
Devin W. Griffith
Lorenz T. Biegler
1 Introduction
465(2)
1.1 NLP Strategies for NMPC
466(1)
2 Properties of the NLP Subproblem
467(3)
2.1 NMPC Problem Reformulation
469(1)
3 Nominal and ISS Stability of NMPC
470(2)
4 Economic NMPC with Objective Regularization
472(9)
4.1 Regularization of Non-convex Economic Stage Costs
474(1)
4.2 Economic NMPC with Regularization of Reduced States
475(6)
5 Economic MPC with a Stabilizing Constraint
481(1)
6 Case Studies
482(5)
6.1 Nonlinear CSTR
482(2)
6.2 Large-Scale Distillation System
484(3)
7 Conclusions
487(1)
References
487(6)
Part III Applications
Automotive Applications of Model Predictive Control
493(36)
Stefano Di Cairano
Ilya V. Kolmanovsky
1 Model Predictive Control in Automotive Applications
493(5)
1.1 A Brief History
494(1)
1.2 Opportunities and Challenges
495(3)
1.3
Chapter Overview
498(1)
2 MPC for Powertrain Control, Vehicle Dynamics, and Energy Management
498(13)
2.1 Powertrain Control
498(6)
2.2 Control of Vehicle Dynamics
504(4)
2.3 Energy Management in Hybrid Vehicles
508(3)
2.4 Other Applications
511(1)
3 MPC Design Process in Automotive Applications
511(7)
3.1 Prediction Model
512(3)
3.2 Horizon and Constraints
515(1)
3.3 Cost Function, Terminal Set and Soft Constraints
516(2)
4 Computations and Numerical Algorithms
518(5)
4.1 Explicit MPC
519(2)
4.2 Online MPC
521(1)
4.3 Nonlinear MPC
522(1)
5 Conclusions and Future Perspectives
523(1)
References
523(6)
Applications of MPC in the Area of Health Care
529(22)
G.C. Goodwin
A.M. Medioli
K. Murray
R. Sykes
C. Stephen
1 Introduction
529(1)
2 Is MPC Relevant to Health Problems?
530(1)
3 Special Characteristics of Control Problems in the Area of Health
530(2)
3.1 Safety
531(1)
3.2 Background Knowledge
531(1)
3.3 Models
531(1)
3.4 Population Versus Personalised Models
532(1)
4 Specific Examples Where MPC Has Been Used in the Area of Health
532(11)
4.1 Ambulance Scheduling
532(2)
4.2 Joint Movement
534(1)
4.3 Type 1 Diabetes-Treatment
535(2)
4.4 Anaesthesia
537(1)
4.5 HIV
538(2)
4.6 Cancer
540(2)
4.7 Inflammation
542(1)
5 Appraisal
543(1)
6 Conclusion
544(1)
References
545(6)
Model Predictive Control for Power Electronics Applications
551(30)
Daniel E. Quevedo
Ricardo P. Aguilera
Tobias Geyer
1 Introduction
551(2)
2 Basic Concepts
553(5)
2.1 System Constraints
553(1)
2.2 Cost Function
554(2)
2.3 Moving Horizon Optimization
556(1)
2.4 Design Parameters
557(1)
3 Linear Quadratic MPC for Converters with a Modulator
558(3)
4 Linear Quadratic Finite Control Set MPC
561(9)
4.1 Closed-Form Solution
562(2)
4.2 Design for Stability and Performance
564(2)
4.3 Example: Reference Tracking
566(4)
5 An Efficient Algorithm for Finite-Control Set MPC
570(7)
5.1 Modified Sphere Decoding Algorithm
571(3)
5.2 Simulation Study of FCS-MPC
574(3)
6 Conclusions
577(1)
References
578(3)
Learning-Based Fast Nonlinear Model Predictive Control for Custom-Made 3D Printed Ground and Aerial Robots
581(26)
Mohit Mehndiratta
Erkan Kayacan
Siddharth Patel
Erdal Kayacan
Girish Chowdhary
1 Introduction
581(2)
2 Receding Horizon Control and Estimation Methods
583(3)
2.1 Nonlinear Model Predictive Control
583(1)
2.2 Nonlinear Moving Horizon Estimation
584(2)
3 Real-Time Applications
586(17)
3.1 Ultra-Compact Field Robot
586(6)
3.2 Tilt-Rotor Tricopter UAV
592(11)
4 Conclusion
603(1)
References
604(3)
Applications of MPC to Building HVAC Systems
607(18)
Nishith R. Patel
James B. Rawlings
1 Introduction to Building HVAC Systems
607(2)
2 Problem Statement
609(2)
2.1 MPC
610(1)
3 Challenges and Opportunities
611(3)
3.1 Modeling
611(1)
3.2 Load Forecasting
612(1)
3.3 Discrete Decisions
613(1)
3.4 Large-Scale Applications
613(1)
3.5 Demand Charges
614(1)
4 Decomposition
614(2)
4.1 High-Level
614(1)
4.2 Low-Level Airside
615(1)
4.3 Low-Level Waterside
615(1)
4.4 Feedback
616(1)
5 Example
616(2)
6 Stanford University Campus
618(2)
6.1 SESI Project
618(1)
6.2 Control System
619(1)
6.3 Performance
620(1)
7 Outlook
620(2)
References
622(3)
Toward Multi-Layered MPC for Complex Electric Energy Systems
625(40)
Marija Ilic
Rupamathi Jaddivada
Xia Miao
Nipun Popli
1 Introduction
625(1)
2 Temporal and Spatial Complexities in the Changing Electric Power Industry
626(2)
3 Load Characterization: The Main Cause of Inter-Temporal Dependencies and Spatial Interdependencies
628(5)
3.1 Multi-Temporal Load Decomposition
631(1)
3.2 Inflexible Load Modeling
631(2)
4 Hierarchical Control in Today's Electric Power Systems
633(5)
4.1 Main Objectives of Hierarchical Control
633(2)
4.2 General Formulation of Main Objectives
635(1)
4.3 Unified Modeling Framework
636(1)
4.4 Assumptions and Limitations Rooted in Today's Hierarchical Control
637(1)
5 Need for Interactive Multi-Layered MPC in Changing Industry
638(2)
5.1 Temporal Aspect
639(1)
5.2 Spatial Aspect
639(1)
6 Temporal Lifting for Decision Making with Multi-Rate Disturbances
640(4)
6.1 Nested Temporal Lifting
641(3)
7 Spatial Lifting for Multi-Agent Decision Making
644(4)
7.1 Nested Spatial Lifting
645(3)
8 Digital Implementation
648(2)
9 Framework for Implementing Interactive Multi-Spatial Multi-Temporal MPC: DyMonDS
650(2)
10 Application of the DyMonDS Framework: One Day in a Lifetime of Two Bus Power System
652(8)
10.1 Example 1: MPC for Utilizing Heterogeneous Generation Resources
652(1)
10.2 Example 2: MPC Spatial and Temporal Lifting in Microgrids to Support Efficient Participation of Flexible Demand
653(2)
10.3 Example 3: The Role of MPC in Reducing the Need for Fast Storage While Enabling Stable Feedback Response
655(3)
10.4 Example 4: The Role of MPC Spatial Lifting in Normal Operation Automatic Generation Control (AGC)
658(2)
11 Conclusions
660(1)
References
660(5)
Applications of MPC to Finance
665(22)
James A. Primbs
1 Introduction
665(3)
1.1 Portfolio Optimization
665(1)
1.2 Dynamic Option Hedging
666(1)
1.3 Organization of
Chapter
667(1)
2 Modeling of Account Value Dynamics
668(3)
2.1 Stock Price Dynamics
670(1)
2.2 Control Structure of Trading Algorithms
671(1)
3 Portfolio Optimization Problems
671(6)
3.1 MPC Formulations
673(4)
4 MPC in Dynamic Option Hedging
677(6)
4.1 European Call Option Hedging
678(1)
4.2 Option Replication as a Control Problem
679(1)
4.3 MPC Option Hedging Formulations
680(2)
4.4 Additional Considerations in Option Hedging
682(1)
5 Conclusions
683(1)
References
683(4)
Index 687
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