Return to home page
Searching: Muskingum library catalog
We are currently experiencing delivery delays for items requested from other institutions while transitioning to a new statewide delivery service. Please contact your library with questions or advice about alternative resources. Thank you for your patience!
  Previous Record Previous Item Next Item Next Record
  Reviews, Summaries, etc...
EBOOK
Author Meza, Gilberto Reynoso.
Title Controller tuning with evolutionary multiobjective optimization : a holistic multiobjective optimization design procedure / Gilberto Reynoso Meza, Xavier Blasco Ferragud, Javier Sanchis Saez, Juan Manuel Herrero Dura.
Imprint Cham, Switzerland : Springer, [2017]

LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
View online
LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
View online
Author Meza, Gilberto Reynoso.
Series Intelligent systems, control and automation: Science and engineering, 2213-8986 ; volume 85
International series on intelligent systems, control and automation--science and engineering ; v. 85.
Subject Multidisciplinary design optimization.
Programmable controllers.
Evolutionary computation.
Alt Name Blasco Ferragud, Xavier (Francesc Xavier), 1966-
Sanchis Saez, Javier.
Herrero DurĂ¡, Juan Manuel.
Description 1 online resource.
Bibliography Note Includes bibliographical references.
Summary This book is devoted to Multiobjective Optimization Design (MOOD) procedures for controller tuning applications, by means of Evolutionary Multiobjective Optimization (EMO). It presents developments in tools, procedures and guidelines to facilitate this process, covering the three fundamental steps in the procedure: problem definition, optimization and decision-making. The book is divided into four parts. The first part, Fundamentals, focuses on the necessary theoretical background and provides specific tools for practitioners. The second part, Basics, examines a range of basic examples regarding the MOOD procedure for controller tuning, while the third part, Benchmarking, demonstrates how the MOOD procedure can be employed in several control engineering problems. The fourth part, Applications, is dedicated to implementing the MOOD procedure for controller tuning in real processes.
Contents Preface; Acknowledgements; Contents; Acronyms; Part I Fundamentals; 1 Motivation: Multiobjective Thinking in Controller Tuning; 1.1 Controller Tuning as a Multiobjective Optimization Problem: A Simple Example; 1.2 Conclusions on This Chapter; References; 2 Background on Multiobjective Optimization for Controller Tuning; 2.1 Definitions; 2.2 Multiobjective Optimization Design (MOOD) Procedure; 2.2.1 Multiobjective Problem (MOP) Definition; 2.2.2 Evolutionary Multiobjective Optimization (EMO); 2.2.3 MultiCriteria Decision Making (MCDM); 2.3 Related Work in Controller Tuning.
2.3.1 Basic Design Objectives in Frequency Domain2.3.2 Basic Design Objectives in Time Domain; 2.3.3 PI-PID Controller Design Concept; 2.3.4 Fuzzy Controller Design Concept; 2.3.5 State Space Feedback Controller Design Concept; 2.3.6 Predictive Control Design Concept; 2.4 Conclusions on This Chapter; References; 3 Tools for the Multiobjective Optimization Design Procedure; 3.1 EMO Process; 3.1.1 Evolutionary Technique; 3.1.2 A MOEA with Convergence Capabilities: MODE; 3.1.3 An MODE with Diversity Features: sp-MODE; 3.1.4 An sp-MODE with Pertinency Features: sp-MODE-II; 3.2 MCDM Stage.
3.2.1 Preferences in MCDM Stage Using Utility Functions3.2.2 Level Diagrams for Pareto Front Analysis; 3.2.3 Level Diagrams for Design Concepts Comparison ; 3.3 Conclusions of This Chapter; References; Part II Basics; 4 Controller Tuning for Univariable Processes; 4.1 Introduction; 4.2 Model Description; 4.3 The MOOD Approach; 4.4 Performance of Some Available Tuning Rules; 4.5 Conclusions; References; 5 Controller Tuning for Multivariable Processes; 5.1 Introduction; 5.2 Model Description and Control Problem; 5.3 The MOOD Approach; 5.4 Control Tests; 5.5 Conclusions; References.
6 Comparing Control Structures from a Multiobjective Perspective6.1 Introduction; 6.2 Model and Controllers Description; 6.3 The MOOD Approach; 6.3.1 Two Objectives Approach; 6.3.2 Three Objectives Approach; 6.4 Conclusions; References; Part III Benchmarking; 7 The ACC'1990 Control Benchmark: A Two-Mass-Spring System; 7.1 Introduction; 7.2 Benchmark Setup: ACC Control Problem; 7.3 The MOOD Approach; 7.4 Control Tests; 7.5 Conclusions; References; 8 The ABB'2008 Control Benchmark: A Flexible Manipulator; 8.1 Introduction; 8.2 Benchmark Setup: The ABB Control Problem; 8.3 The MOOD Approach.
8.4 Control Tests8.5 Conclusions; References; 9 The 2012 IFAC Control Benchmark: An Industrial Boiler Process; 9.1 Introduction; 9.2 Benchmark Setup: Boiler Control Problem; 9.3 The MOOD Approach; 9.4 Control Tests; 9.5 Conclusions; References; Part IV Applications; 10 Multiobjective Optimization Design Procedure for Controller Tuning of a Peltier Cell Process; 10.1 Introduction; 10.2 Process Description; 10.3 The MOOD Approach; 10.4 Control Tests; 10.5 Conclusions; References; 11 Multiobjective Optimization Design Procedure for Controller Tuning of a TRMS Process; 11.1 Introduction.
ISBN 9783319413013 (electronic bk.)
3319413015 (electronic bk.)
9783319412993
331941299X
ISBN/ISSN 10.1007/978-3-319-41301-3
OCLC # 962325710
Additional Format Print version: Controller tuning with evolutionary multiobjective optimization. Cham, Switzerland : Springer, 2016, 2017 9783319412993 331941299X (OCoLC)951760868



If you experience difficulty accessing or navigating this content, please contact the OPAL Support Team