Zhihua Zhang 
Observer Design for Control and Fault Diagnosis of Boolean Networks [PDF ebook] 

Sokongan

Boolean control networks (BCNs) are a kind of parameter-free model, which can be used to approximate the qualitative behavior of biological systems. After converting into a model similar to the standard discrete-time state-space model, control-theoretic problems of BCNs can be studied. In control theory, state observers can provide state estimation for any other applications. Reconstructibility condition is necessary for the existence of state observers. In this thesis explicit and recursive methods have been developed for reconstructibility analysis. Then, an approach to design Luenberger-like observer has been proposed, which works in a two-step process (i.e. predict and update). If a BCN is reconstructible, then an accurate state estimate can be provided by the observer no later than the minimal reconstructibility index. For a wide range of applications the approach has been extended to enable design of unknown input observer, distributed observers and reduced-order observer. The performance of the observers has been evaluated thoroughly. Furthermore, methods for output tracking control and fault diagnosis of BCNs have been developed. Finally, the developed schemes are tested with numerical examples.

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Jadual kandungan

Introduction.- Matrix expression of Boolean control networks.- Reconstructibility analysis.- Observer design.- Model-based output tracking control.- Model-based Fault diagnosis.- Conclusion.- Kurzfassung in deutscher Sprache (extended summary in German).

Mengenai Pengarang

Zhihua Zhang received the B.Sc. degree and M.Sc. degree in electrical engineering from the TU Kaiserslautern, Germany, respectively, in 2013 and 2015. In 2021 he finished Ph.D. in the area of Boolean control networks.

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Bahasa Inggeris ● Format PDF ● Halaman-halaman 169 ● ISBN 9783658359294 ● Saiz fail 2.5 MB ● Penerbit Springer Fachmedien Wiesbaden ● Bandar raya Wiesbaden ● Negara DE ● Diterbitkan 2021 ● Muat turun 24 bulan ● Mata wang EUR ● ID 8238978 ● Salin perlindungan Social DRM

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