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Controller Tuning with Evolutionary Multiobjective Optimization : A Holistic Multiobjective Optimization Design Procedure

By: Reynoso Meza, Gilberto.
Contributor(s): Blasco Ferragud, Xavier | Sanchis Saez, Javier | Herrero Durá, Juan Manuel.
Material type: TextTextSeries: eBooks on Demand.Intelligent Systems, Control and Automation: Science and Engineering: Publisher: Cham : Springer International Publishing, 2016Description: 1 online resource (228 p.).ISBN: 9783319413013.Subject(s): Programmable controllers--Handbooks, manuals, etcGenre/Form: Electronic books.Additional physical formats: Print version:: Controller Tuning with Evolutionary Multiobjective Optimization : A Holistic Multiobjective Optimization Design ProcedureDDC classification: 620 Online resources: Click here to view this ebook.
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 Domain -- 2.3.2 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 Functions -- 3.2.2 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 Perspective -- 6.1 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 Tests -- 8.5 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
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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 Domain -- 2.3.2 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 Functions -- 3.2.2 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 Perspective -- 6.1 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 Tests -- 8.5 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

Description based upon print version of record.

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