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Table of 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.
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.