04391nam 22007095 450 991025424790332120220406235306.03-319-28847-410.1007/978-3-319-28847-5(CKB)3710000000577065(EBL)4354095(SSID)ssj0001606890(PQKBManifestationID)16317683(PQKBTitleCode)TC0001606890(PQKBWorkID)14895618(PQKB)11613875(DE-He213)978-3-319-28847-5(MiAaPQ)EBC4354095(PPN)191701955(EXLCZ)99371000000057706520160119d2016 u| 0engur|n|---|||||txtccrAnalysis and design of Markov jump systems with complex transition probabilities[electronic resource] /by Lixian Zhang, Ting Yang, Peng Shi, Yanzheng Zhu1st ed. 2016.Cham :Springer International Publishing :Imprint: Springer,2016.1 online resource (268 p.)Studies in Systems, Decision and Control,2198-4182 ;54Description based upon print version of record.3-319-28846-6 Includes bibliographical references and index.Introduction -- Part I Partially Unknown TPs -- Part II Piecewise Homogeneous TPs.-Part III Memory TPs.The book addresses the control issues such as stability analysis, control synthesis and filter design of Markov jump systems with the above three types of TPs, and thus is mainly divided into three parts. Part I studies the Markov jump systems with partially unknown TPs. Different methodologies with different conservatism for the basic stability and stabilization problems are developed and compared. Then the problems of state estimation, the control of systems with time-varying delays, the case involved with both partially unknown TPs and uncertain TPs in a composite way are also tackled. Part II deals with the Markov jump systems with piecewise homogeneous TPs. Methodologies that can effectively handle control problems in the scenario are developed, including the one coping with the asynchronous switching phenomenon between the currently activated system mode and the controller/filter to be designed. Part III focuses on the Markov jump systems with memory TPs. The concept of σ-mean square stability is proposed such that the stability problem can be solved via a finite number of conditions. The systems involved with nonlinear dynamics (described via the Takagi-Sugeno fuzzy model) are also investigated. Numerical and practical examples are given to verify the effectiveness of the obtained theoretical results. Finally, some perspectives and future works are presented to conclude the book.Studies in Systems, Decision and Control,2198-4182 ;54Control engineeringComputational complexitySystem theoryStatistical physicsControl and Systems Theoryhttps://scigraph.springernature.com/ontologies/product-market-codes/T19010Complexityhttps://scigraph.springernature.com/ontologies/product-market-codes/T11022Systems Theory, Controlhttps://scigraph.springernature.com/ontologies/product-market-codes/M13070Applications of Nonlinear Dynamics and Chaos Theoryhttps://scigraph.springernature.com/ontologies/product-market-codes/P33020Control engineering.Computational complexity.System theory.Statistical physics.Control and Systems Theory.Complexity.Systems Theory, Control.Applications of Nonlinear Dynamics and Chaos Theory.620Zhang Lixianauthttp://id.loc.gov/vocabulary/relators/aut1060647Yang Tingauthttp://id.loc.gov/vocabulary/relators/autShi Pengauthttp://id.loc.gov/vocabulary/relators/autZhu Yanzhengauthttp://id.loc.gov/vocabulary/relators/autBOOK9910254247903321Analysis and Design of Markov Jump Systems with Complex Transition Probabilities2514742UNINA