Variance-Constrained Multi-Objective Stochastic Control and Filtering.

Unifies existing and emerging concepts concerning multi-objective control and stochastic control with engineering-oriented phenomena Establishes a unified theoretical framework for control and filtering problems for a class of discrete-time nonlinear stochastic systems with consideration to performa...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Ma, Lifeng
Otros Autores: Wang, Zidong, Bo, Yuming
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken : Wiley, 2015.
Temas:
Automatic control > Mathematics.
Stochastic processes.
Commande automatique > Mathématiques.
Processus stochastiques.
Automatic control > Mathematics
Stochastic processes
Acceso en línea:Texto completo

MARC

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035 |a (OCoLC)908519976 
050 4 |a QA402.3 .V384 2015 
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049 |a UAMI 
100 1 |a Ma, Lifeng. 
245 1 0 |a Variance-Constrained Multi-Objective Stochastic Control and Filtering. 
260 |a Hoboken :  |b Wiley,  |c 2015. 
300 |a 1 online resource (320 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
588 0 |a Print version record. 
505 0 |a Cover; Title Page; Copyright; Contents; Preface; Series Preface; Acknowledgements; List of Abbreviations; List of Figures; Chapter 1 Introduction; 1.1 Analysis and Synthesis of Nonlinear Stochastic Systems; 1.1.1 Nonlinear Systems; 1.1.2 Stochastic Systems; 1.2 Multi-Objective Control and Filtering with Variance Constraints; 1.2.1 Covariance Control Theory; 1.2.2 Multiple Performance Requirements; 1.2.3 Design Techniques for Nonlinear Stochastic Systems with Variance Constraints; 1.2.4 A Special Case of Multi-Objective Design: Mixed H2/H∞ Control/Filtering; 1.3 Outline 
505 8 |a Chapter 2 Robust H∞ Control with Variance Constraints2.1 Problem Formulation; 2.2 Stability, H∞ Performance, and Variance Analysis; 2.2.1 Stability and H∞ Performance Analysis; 2.2.2 Variance Analysis; 2.3 Robust Controller Design; 2.4 Numerical Example; 2.5 Summary; Chapter 3 Robust Mixed H2/H∞ Filtering; 3.1 System Description and Problem Formulation; 3.2 Algebraic Characterizations for Robust H2/H∞ Filtering; 3.2.1 Robust H2 Filtering; 3.2.2 Robust H∞ Filtering; 3.3 Robust H2/H∞ Filter Design Techniques; 3.4 An Illustrative Example; 3.5 Summary 
505 8 |a Chapter 4 Robust Variance-Constrained Filtering with Missing Measurements4.1 Problem Formulation; 4.2 Stability and Variance Analysis; 4.3 Robust Filter Design; 4.4 Numerical Example; 4.5 Summary; Chapter 5 Robust Fault-Tolerant Control with Variance Constraints; 5.1 Problem Formulation; 5.2 Stability and Variance Analysis; 5.3 Robust Controller Design; 5.4 Numerical Example; 5.5 Summary; Chapter 6 Robust H2 Sliding Mode Control; 6.1 The System Model; 6.2 Robust H2 Sliding Mode Control; 6.2.1 Switching Surface; 6.2.2 Performances of the Sliding Motion; 6.2.3 Computational Algorithm. 
505 8 |a 6.3 Sliding Mode Controller6.4 Numerical Example; 6.5 Summary; Chapter 7 Variance-Constrained Dissipative Control with Degraded Measurements; 7.1 Problem Formulation; 7.2 Stability, Dissipativity, and Variance Analysis; 7.3 Observer-Based Controller Design; 7.3.1 Solvability of the Multi-Objective Control Problem; 7.3.2 Computational Algorithm; 7.4 Numerical Example; 7.5 Summary; Chapter 8 Variance-Constrained H∞ Control with Multiplicative Noises; 8.1 Problem Formulation; 8.2 Stability, H∞ Performance, and Variance Analysis; 8.2.1 Stability; 8.2.2 H∞ Performance; 8.2.3 Variance Analysis 
505 8 |a 8.3 Robust State Feedback Controller Design8.4 Numerical Example; 8.5 Summary; Chapter 9 Robust H∞ Control with Variance Constraints: the Finite-Horizon Case; 9.1 Problem Formulation; 9.2 Performance Analysis; 9.2.1 H∞ Performance; 9.2.2 Variance Analysis; 9.3 Robust Finite-Horizon Controller Design; 9.4 Numerical Example; 9.5 Summary; Chapter 10 Error Variance-Constrained H∞ Filtering with Degraded Measurements: The Finite-Horizon Case; 10.1 Problem Formulation; 10.2 Performance Analysis; 10.2.1 H∞ Performance Analysis; 10.2.2 System Covariance Analysis; 10.3 Robust Filter Design 
500 |a 10.4 Numerical Example. 
520 |a Unifies existing and emerging concepts concerning multi-objective control and stochastic control with engineering-oriented phenomena Establishes a unified theoretical framework for control and filtering problems for a class of discrete-time nonlinear stochastic systems with consideration to performance Includes case studies of several nonlinear stochastic systems Investigates the phenomena of incomplete information, including missing/degraded measurements, actuator failures and sensor saturations Considers both time-invariant systems and time-varying systems Exploits newly developed technique. 
504 |a Includes bibliographical references and index. 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
650 0 |a Automatic control  |x Mathematics. 
650 0 |a Stochastic processes. 
650 6 |a Commande automatique  |x Mathématiques. 
650 6 |a Processus stochastiques. 
650 7 |a Automatic control  |x Mathematics  |2 fast 
650 7 |a Stochastic processes  |2 fast 
700 1 |a Wang, Zidong. 
700 1 |a Bo, Yuming. 
758 |i has work:  |a Variance-constrained multi-objective stochastic control and filtering (Text)  |1 https://id.oclc.org/worldcat/entity/E39PCGwvd9DR6kfKxvmbX7CHhb  |4 https://id.oclc.org/worldcat/ontology/hasWork 
776 0 8 |i Print version:  |a Ma, Lifeng.  |t Variance-Constrained Multi-Objective Stochastic Control and Filtering.  |d Hoboken : Wiley, ©2015  |z 9781118929490 
856 4 0 |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1895765  |z Texto completo 
938 |a ProQuest Ebook Central  |b EBLB  |n EBL1895765 
938 |a Recorded Books, LLC  |b RECE  |n rbeEB00612236 
994 |a 92  |b IZTAP 

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