Metaheuristic Optimization for the Design of Automatic Control Laws.
Title:
Metaheuristic Optimization for the Design of Automatic Control Laws.
Author:
Sandou, Guillaume.
ISBN:
9781118796481
Personal Author:
Edition:
1st ed.
Physical Description:
1 online resource (140 pages)
Series:
FOCUS Series
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 -- 3.4.2. Time response optimization -- 3.4.3. Time response optimization with penalization on the control input -- 3.4.4. Time response optimization with penalization on the control input and constraint on module margin -- 3.5. PID multiobjective optimization -- 3.6. Conclusions -- Chapter 4. Tuning And Optimization Of H∞ Control Laws -- 4.1. Introduction -- 4.2. H∞ synthesis -- 4.2.1. Full-order H∞ synthesis.
4.2.2. Tuning the filters as an optimization problem -- 4.2.3. Reduced-order H∞ synthesis -- 4.3. Application to the control of a pendulum in the cart -- 4.3.1. Case study -- 4.3.2. H∞ synthesis schemes -- 4.3.3. Optimization of the parameters of the filters -- 4.3.4. Reduced-order H∞ synthesis: one DOF case -- 4.3.5. Reduced-order H∞ synthesis: three DOF case -- 4.3.6. Conclusions -- 4.4. Static output feedback design -- 4.5. Industrial examples -- 4.5.1. Mold level control in continuous casting -- 4.5.2. Linear parameter varying control of a missile -- 4.5.3. Internal combustion engine air path control -- 4.5.4. Inertial line-of-sight stabilization -- 4.6. Conclusions -- Chapter 5. Predictive Control Of Hybrid Systems -- 5.1. Problematic -- 5.2. Predictive control of power systems -- 5.2.1. Open-loop control and unit commitment -- 5.2.2. Closed-loop control -- 5.3. Optimization procedure -- 5.3.1. Classical optimization methods for unit commitment -- 5.3.2. General synopsis of the optimization procedure -- 5.3.3. Ant colony optimization for the unit commitment -- 5.3.4. Computation of real variables -- 5.3.5. Feasibility criterion -- 5.3.6. Knowledge-based genetic algorithm -- 5.4. Simulation results -- 5.4.1. Real-time updating of produced powers -- 5.4.2. Case study -- 5.5. Conclusions and discussions -- Conclusion -- Bibliography -- Index.
Abstract:
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.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Genre:
Electronic Access:
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