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