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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Full text (MCPHS users only) |
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| Main Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hoboken, NJ :
ISTE Ltd/John Wiley and Sons Inc,
2013
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| Series: | Focus series in automation & control.
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| Subjects: | |
| Local Note: | ProQuest Ebook Central |
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| 100 | 1 | |a Sandou, Guillaume. | |
| 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 | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Focus automation and control series, |x 2051-2481 | |
| 504 | |a Includes bibliographical references and index. | ||
| 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 | |
| 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. | ||
| 546 | |a English. | ||
| 588 | 0 | |a Print version record. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Heuristic algorithms. | |
| 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. | |
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