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Metaheuristic optimization for the design of automatic control laws /

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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Sandou, Guillaume
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken, NJ : ISTE Ltd/John Wiley and Sons Inc, 2013.
Colección:Focus series in automation & control.
Temas:
Acceso en línea:Texto completo

MARC

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245 1 0 |a Metaheuristic optimization for the design of automatic control laws /  |c Guillaume Sandou. 
264 1 |a Hoboken, NJ :  |b ISTE Ltd/John Wiley and Sons Inc,  |c 2013. 
300 |a 1 online resource (140 pages) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
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490 1 |a Focus automation and control series,  |x 2051-2481 
504 |a Includes bibliographical references and index. 
588 0 |a Print version record. 
520 |a 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 cr. 
505 0 |a Cover -- Title Page -- Contents -- Preface -- Chapter 1. Introduction And Motivations -- 1.1. Introduction: automatic control and optimization -- 1.2. Motivations to use metaheuristic algorithms -- 1.3. Organization of the book -- Chapter 2. Symbolic Regression 
505 8 |a 2.1. Identification problematic and brief state of the art 2.2. Problem statement and modeling -- 2.2.1. Problem statement -- 2.2.2. Problem modeling -- 2.3. Ant colony optimization -- 2.3.1. Ant colony social behavior -- 2.3.2. Ant colony optimization 
505 8 |a 2.3.3. Ant colony for the identification of nonlinear functions with unknown structure 2.4. Numerical results -- 2.4.1. Parameter settings -- 2.4.2. Experimental results -- 2.5. Discussion -- 2.5.1. Considering real variables -- 2.5.2. Local minima 
505 8 |a 2.5.3. Identification of nonlinear dynamical systems 2.6. A note on genetic algorithms for symbolic regression -- 2.7. Conclusions -- Chapter 3. Pid Design Using Particle Swarm Optimization -- 3.1. Introduction -- 3.2. Controller tuning: a hard optimization problem 
505 8 |a 3.2.1. Problem framework 3.2.2. Expressions of time domain specifications -- 3.2.3. Expressions of frequency domain specifications -- 3.2.4. Analysis of the optimization problem -- 3.3. Particle swarm optimization implementation -- 3.4. PID tuning optimization 
546 |a English. 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
650 0 |a Mathematical optimization. 
650 0 |a Heuristic algorithms. 
650 6 |a Optimisation mathématique. 
650 6 |a Algorithmes heuristiques. 
650 7 |a Heuristic algorithms  |2 fast 
650 7 |a Mathematical optimization  |2 fast 
758 |i has work:  |a Metaheuristic Optimization for the Design of Automatic Control Laws [electronic resource] (Text)  |1 https://id.oclc.org/worldcat/entity/E39PCYRMDmbPx7ktfQ8dty7Cgq  |4 https://id.oclc.org/worldcat/ontology/hasWork 
776 0 8 |i Print version:  |a Sandou, Guillaume.  |t Metaheuristic optimization for the design of automatic control laws.  |d Hoboken, NJ : ISTE Ltd/John Wiley and Sons Inc, 2013  |h x, 128 pages  |k Focus automation and control series  |x 2051-2481  |z 9781848215900  |w (DLC) 17775309 
830 0 |a Focus series in automation & control. 
856 4 0 |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1376955  |z Texto completo 
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