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020 _a9783031275401
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050 4 _aTJ212-225
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082 0 4 _a629.8312
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082 0 4 _a003
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245 1 0 _aRecent Developments in Model-Based and Data-Driven Methods for Advanced Control and Diagnosis
_h[electronic resource] /
_cedited by Didier Theilliol, Józef Korbicz, Janusz Kacprzyk.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2023.
300 _aX, 365 p. 145 illus., 126 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Systems, Decision and Control,
_x2198-4190 ;
_v467
500 _aAcceso multiusuario
505 0 _aH_infinity Stochastic State-Multiplicative Uncertain Systems-Robust Luenberger Filters -- Anticipating the Loss of Unknown Input Observability for Sampled LPV Systems -- Luenberger Observer Design for Robust Estimation of Battery State of Charge with Application to Lithium-Titanate Oxide Cells -- Fault Detection and Diagnosis of PV Systems Using Kalman-Filter Algorithm Based on Multi-Zone Polynomial Regression -- Parity-Space and Multiple-Model Based Approaches to Measurement Fault Estimation -- Online Condition Monitoring of a Vacuum Process Based on Adaptive Notch Filters.
520 _aThe book consists of recent works on several axes either with a more theoretical nature or with a focus on applications, which will span a variety of up-to-date topics in the field of systems and control. The main market area of the contributions include: Advanced fault-tolerant control, control reconfiguration, health monitoring techniques for industrial systems, data-driven diagnosis methods, process supervision, diagnosis and control of discrete-event systems, maintenance and repair strategies, statistical methods for fault diagnosis, reliability and safety of industrial systems artificial intelligence methods for control and diagnosis, health-aware control design strategies, advanced control approaches, deep learning-based methods for control and diagnosis, reinforcement learning-based approaches for advanced control, diagnosis and prognosis techniques applied to industrial problems, Industry 4.0 as well as instrumentation and sensors. These works constitute advances in the aforementioned scientific fields and will be used by graduate as well as doctoral students along with established researchers to update themselves with the state of the art and recent advances in their respective fields. As the book includes several applicative studies with several multi-disciplinary contributions (deep learning, reinforcement learning, model-based/data-based control etc.), the book proves to be equally useful for the practitioners as well industrial professionals.
541 _fUABC ;
_cPerpetuidad
650 0 _aControl engineering.
650 0 _aComputational intelligence.
650 0 _aDynamics.
650 0 _aNonlinear theories.
650 1 4 _aControl and Systems Theory.
650 2 4 _aComputational Intelligence.
650 2 4 _aApplied Dynamical Systems.
700 1 _aTheilliol, Didier.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aKorbicz, Józef.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aKacprzyk, Janusz.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031275395
776 0 8 _iPrinted edition:
_z9783031275418
776 0 8 _iPrinted edition:
_z9783031275425
830 0 _aStudies in Systems, Decision and Control,
_x2198-4190 ;
_v467
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-27540-1
912 _aZDB-2-INR
912 _aZDB-2-SXIT
942 _cLIBRO_ELEC
999 _c261837
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