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E-raamat: Metaheuristic Optimization for the Design of Automatic Control Laws [Wiley Online]

  • Formaat: 144 pages
  • Sari: Focus Series
  • Ilmumisaeg: 27-Aug-2013
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1118796357
  • ISBN-13: 9781118796351
Teised raamatud teemal:
  • Wiley Online
  • Hind: 174,45 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Metaheuristic Optimization for the Design of Automatic Control LawsSuurem pilt
  • Formaat: 144 pages
  • Sari: Focus Series
  • Ilmumisaeg: 27-Aug-2013
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1118796357
  • ISBN-13: 9781118796351
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118796351

The classic approach in Automatic Control relies on the use of simplified models of the systems and reformulations of the specifications. In this framework, the control law can be computed using deterministic algorithms. However, this approach fails when the system is too complex for its model to be sufficiently simplified, when the designer has many constraints to take into account, or when the goal is not only to design a control but also to optimize it. This book presents a new trend in Automatic Control with the use of metaheuristic algorithms. These kinds of algorithm can optimize any criterion and constraint, and therefore do not need such simplifications and reformulations.
The first chapter outlines the author’s main motivations for the approach which he proposes, and presents the advantages which it offers. In Chapter 2, he deals with the problem of system identification. The third and fourth chapters are the core of the book where the design and optimization of control law, using the metaheuristic method (particle swarm optimization), is given. The proposed approach is presented along with real-life experiments, proving the efficiency of the methodology. Finally, in Chapter 5, the author proposes solving the problem of predictive control of hybrid systems.

Contents

1. Introduction and Motivations.
2. Symbolic Regression.
3. PID Design Using Particle Swarm Optimization.
4. Tuning and Optimization of H-infinity Control Laws.
5. Predictive Control of Hybrid Systems.

About the Authors

Guillaume Sandou is Professor in the Automatic Department of Supélec, in Gif Sur Yvette, France. He has had 12 books, 8 journal papers and 1 patent published, and has written papers for 32 international conferences.His main research interests include modeling, optimization and control of industrial systems; optimization and metaheuristics for Automatic Control; and constrained control.

Preface ix
Chapter 1 Introduction and Motivations
1(6)
1.1 Introduction: automatic control and optimization
1(2)
1.2 Motivations to use metaheuristic algorithms
3(2)
1.3 Organization of the book
5(2)
Chapter 2 Symbolic Regression
7(20)
2.1 Identification problematic and brief state of the art
7(3)
2.2 Problem statement and modeling
10(3)
2.2.1 Problem statement
10(1)
2.2.2 Problem modeling
10(3)
2.3 Ant colony optimization
13(5)
2.3.1 Ant colony social behavior
13(1)
2.3.2 Ant colony optimization
14(2)
2.3.3 Ant colony for the identification of nonlinear functions with unknown structure
16(2)
2.4 Numerical results
18(4)
2.4.1 Parameter settings
18(1)
2.4.2 Experimental results
19(3)
2.5 Discussion
22(1)
2.5.1 Considering real variables
22(1)
2.5.2 Local minima
22(1)
2.5.3 Identification of nonlinear dynamical systems
23(1)
2.6 A note on genetic algorithms for symbolic regression
23(2)
2.7 Conclusions
25(2)
Chapter 3 PID Design Using Particle Swarm Optimization
27(24)
3.1 Introduction
27(2)
3.2 Controller tuning: a hard optimization problem
29(6)
3.2.1 Problem framework
29(1)
3.2.2 Expressions of time domain specifications
30(2)
3.2.3 Expressions of frequency domain specifications
32(3)
3.2.4 Analysis of the optimization problem
35(1)
3.3 Particle swarm optimization implementation
35(2)
3.4 PID tuning optimization
37(6)
3.4.1 Case study: magnetic levitation
37(2)
3.4.2 Time response optimization
39(2)
3.4.3 Time response optimization with penalization on the control input
41(1)
3.4.4 Time response optimization with penalization on the control input and constraint on module margin
42(1)
3.5 PID multiobjective optimization
43(5)
3.6 Conclusions
48(3)
Chapter 4 Tuning and Optimization of H∞ Control Laws
51(38)
4.1 Introduction
51(3)
4.2 H∞ synthesis
54(6)
4.2.1 Full-order H∞ synthesis
54(3)
4.2.2 Tuning the filters as an optimization problem
57(1)
4.2.3 Reduced-order H∞ synthesis
58(2)
4.3 Application to the control of a pendulum in the cart
60(17)
4.3.1 Case study
60(4)
4.3.2 H∞ synthesis schemes
64(2)
4.3.3 Optimization of the parameters of the filters
66(4)
4.3.4 Reduced-order H∞ synthesis: one DOF case
70(1)
4.3.5 Reduced-order H∞ synthesis: three DOF case
71(5)
4.3.6 Conclusions
76(1)
4.4 Static output feedback design
77(5)
4.5 Industrial examples
82(5)
4.5.1 Mold level control in continuous casting
83(1)
4.5.2 Linear parameter varying control of a missile
83(3)
4.5.3 Internal combustion engine air path control
86(1)
4.5.4 Inertial line-of-sight stabilization
86(1)
4.6 Conclusions
87(2)
Chapter 5 Predictive Control of Hybrid Systems
89(22)
5.1 Problematic
89(3)
5.2 Predictive control of power systems
92(4)
5.2.1 Open-loop control and unit commitment
92(2)
5.2.2 Closed-loop control
94(2)
5.3 Optimization procedure
96(11)
5.3.1 Classical optimization methods for unit commitment
96(1)
5.3.2 General synopsis of the optimization procedure
97(1)
5.3.3 Ant colony optimization for the unit commitment
98(2)
5.3.4 Computation of real variables
100(1)
5.3.5 Feasibility criterion
101(1)
5.3.6 Knowledge-based genetic algorithm
102(5)
5.4 Simulation results
107(1)
5.4.1 Real-time updating of produced powers
107(1)
5.4.2 Case study
107(1)
5.5 Conclusions and discussions
108(3)
Conclusion 111(4)
Bibliography 115(12)
Index 127
Guillaume SANDOU is Associate Professor, SUPELEC Systems Sciences (E3S), Control Department, Gif sur Yvette, France.
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