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El. knyga: Controller Tuning with Evolutionary Multiobjective Optimization: A Holistic Multiobjective Optimization Design Procedure

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Controller Tuning with Evolutionary Multiobjective Optimization: A Holistic Multiobjective Optimization Design ProcedureDidesnis paveikslėlis
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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.

Part I Fundamentals.- Tutorial on Multiobjective Optimization Design procedure.- Review on MOOD procedure for controller tuning.- Tools for the MOOD procedure.- Part II Basics.- Controller tuning for SISO processes.- Controller tuning for MIMO processes.- Controller tuning in multidisciplinary optimization.- Part III Benchmarking.- The ACC"1992 Control Benchmark: a Two-mass-spring system.- The ABB"2008 Control Benchmark: a Flexible Manipulator.- The 2012 IFAC Control Benchmark: a Boiler Process.- Part IV Applications.- MOOD procedure for a to-be-defined process.- MOOD procedure for a Peltier process.- MOOD procedure for a Twin RotorMIMO System.
Part I Fundamentals
1 Motivation: Multiobjective Thinking in Controller Tuning
3(20)
1.1 Controller Tuning as a Multiobjective Optimization Problem: A Simple Example
3(17)
1.2 Conclusions on This
Chapter
20(3)
References
21(2)
2 Background on Multiobjective Optimization for Controller Tuning
23(36)
2.1 Definitions
23(4)
2.2 Multiobjective Optimization Design (MOOD) Procedure
27(14)
2.2.1 Multiobjective Problem (MOP) Definition
28(1)
2.2.2 Evolutionary Multiobjective Optimization (EMO)
29(8)
2.2.3 MultiCriteria Decision Making (MCDM)
37(4)
2.3 Related Work in Controller Tuning
41(9)
2.3.1 Basic Design Objectives in Frequency Domain
41(1)
2.3.2 Basic Design Objectives in Time Domain
42(2)
2.3.3 PI-PID Controller Design Concept
44(3)
2.3.4 Fuzzy Controller Design Concept
47(1)
2.3.5 State Space Feedback Controller Design Concept
48(1)
2.3.6 Predictive Control Design Concept
49(1)
2.4 Conclusions on This
Chapter
50(9)
References
51(8)
3 Tools for the Multiobjective Optimization Design Procedure
59(32)
3.1 EMO Process
59(16)
3.1.1 Evolutionary Technique
60(2)
3.1.2 A MOEA with Convergence Capabilities: MODE
62(1)
3.1.3 An MODE with Diversity Features: sp-MODE
63(6)
3.1.4 An sp-MODE with Pertinency Features: sp-MODE-II
69(6)
3.2 MCDM Stage
75(12)
3.2.1 Preferences in MCDM Stage Using Utility Functions
76(3)
3.2.2 Level Diagrams for Pareto Front Analysis
79(3)
3.2.3 Level Diagrams for Design Concepts Comparison
82(5)
3.3 Conclusions of This
Chapter
87(4)
References
88(3)
Part II Basics
4 Controller Tuning for Univariable Processes
91(16)
4.1 Introduction
91(1)
4.2 Model Description
92(1)
4.3 The MOOD Approach
92(10)
4.4 Performance of Some Available Tuning Rules
102(2)
4.5 Conclusions
104(3)
References
105(2)
5 Controller Tuning for Multivariable Processes
107(16)
5.1 Introduction
107(1)
5.2 Model Description and Control Problem
108(1)
5.3 The MOOD Approach
109(8)
5.4 Control Tests
117(4)
5.5 Conclusions
121(2)
References
121(2)
6 Comparing Control Structures from a Multiobjective Perspective
123(24)
6.1 Introduction
123(1)
6.2 Model and Controllers Description
124(2)
6.3 The MOOD Approach
126(17)
6.3.1 Two Objectives Approach
126(5)
6.3.2 Three Objectives Approach
131(12)
6.4 Conclusions
143(4)
References
143(4)
Part III Benchmarking
7 The ACC'1990 Control Benchmark: A Two-Mass-Spring System
147(12)
7.1 Introduction
147(1)
7.2 Benchmark Setup: ACC Control Problem
148(1)
7.3 The MOOD Approach
149(5)
7.4 Control Tests
154(2)
7.5 Conclusions
156(3)
References
156(3)
8 The ABB'2008 Control Benchmark: A Flexible Manipulator
159(14)
8.1 Introduction
159(1)
8.2 Benchmark Setup: The ABB Control Problem
159(4)
8.3 The MOOD Approach
163(5)
8.4 Control Tests
168(3)
8.5 Conclusions
171(2)
References
172(1)
9 The 2012 IFAC Control Benchmark: An Industrial Boiler Process
173(14)
9.1 Introduction
173(1)
9.2 Benchmark Setup: Boiler Control Problem
174(2)
9.3 The MOOD Approach
176(4)
9.4 Control Tests
180(2)
9.5 Conclusions
182(5)
References
182(5)
Part IV Applications
10 Multiobjective Optimization Design Procedure for Controller Tuning of a Peltier Cell Process
187(14)
10.1 Introduction
187(1)
10.2 Process Description
188(1)
10.3 The MOOD Approach
189(8)
10.4 Control Tests
197(1)
10.5 Conclusions
198(3)
References
199(2)
11 Multiobjective Optimization Design Procedure for Controller Tuning of a TRMS Process
201(14)
11.1 Introduction
201(1)
11.2 Process Description
201(2)
11.3 The MOOD Approach for Design Concepts Comparison
203(5)
11.4 The MOOD Approach for Controller Tuning
208(5)
11.5 Control Tests
213(1)
11.6 Conclusions
213(2)
References
213(2)
12 Multiobjective Optimization Design Procedure for an Aircraft's Flight Control System
215(12)
12.1 Introduction
215(1)
12.2 Process Description
216(3)
12.3 The MOOD Approach
219(3)
12.4 Controllers Performance in a Real Flight Mission
222(5)
12.5 Conclusions
227(1)
References 227
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