Metaheuristic optimization for the design of automatic control laws / Guillaume Sandou.

Format:

Book

Author/Creator:

Sandou, Guillaume.

Series:

Focus series in automation & control.

Focus automation and control series, 2051-2481

Language:

English

Subjects (All):

Mathematical optimization.

Heuristic algorithms.

Physical Description:

1 online resource (140 p.)

Edition:

1st ed.

Place of Publication:

Hoboken, NJ : ISTE Ltd/John Wiley and Sons Inc, 2013.

Language Note:

English

Summary:

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

Contents:

""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 ""

""3.4.1. Case study: magnetic levitation ""

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781118796511

1118796519

9781118796351

1118796357

9781118796481

1118796489

OCLC:

861529063