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_aQA402.5 _bM493 2017 EB |
|
| 100 | 1 | _aMeza, Gilberto Reynoso. | |
| 245 | 1 | 0 |
_aController tuning with evolutionary multiobjective optimization : _ba holistic multiobjective optimization design procedure _cGilberto Reynoso Meza, Xavier Blasco Ferragud, Javier Sanchis Saez, Juan Manuel Herrero Durá. |
| 264 | 1 |
_aCham, Switzerland _bSpringer _c[2017] |
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| 300 | _a1 recurso en línea | ||
| 336 |
_aTexto _btxt _2rdacontent |
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| 337 |
_aelectrónico _bc _2rdamedia |
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| 338 |
_arecurso electrónico _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 0 |
_aIntelligent systems, control and automation: Science and engineering _x2213-8986 _vvolume 85 |
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| 500 |
_aSpringerLink _bSpringer Engineering eBooks 2017 English+International |
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| 504 | _aIncluye referencias bibliográficas | ||
| 505 | 0 | _aPreface; 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. | |
| 505 | 8 | _a2.3.1 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. | |
| 505 | 8 | _a3.2.1 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. | |
| 505 | 8 | _a6 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. | |
| 505 | 8 | _a8.4 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. | |
| 520 | 3 | _aThis 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. | |
| 650 | 7 |
_aComputación evolutiva _2embne _0(OCoLC)fst00917338 _0 _9667195 |
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| 700 | 1 |
_aBlasco Ferragud, Xavier _q(Francesc Xavier), _d1966- |
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| 700 | 1 | _aHerrero Durá, Juan Manuel. | |
| 700 | 1 | _aSanchis Saez, Javier. | |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-41301-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 988 | _aEBOOK, asignarmaterias, EBSPRINGER_2017A | ||
| 998 |
_b02/2018 _dz _e- _zSI |
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| 999 |
_c94841 _d94841 _x1 |
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