000 | 03764nam a22005895i 4500 | ||
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001 | 978-981-99-3053-1 | ||
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005 | 20240207153625.0 | ||
007 | cr nn 008mamaa | ||
008 | 230626s2023 si | s |||| 0|eng d | ||
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_a9789819930531 _9978-981-99-3053-1 |
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_aZhai, Chao. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aControl and Optimization Methods for Complex System Resilience _h[electronic resource] / _cby Chao Zhai. |
250 | _a1st ed. 2023. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2023. |
|
300 |
_aXX, 206 p. 68 illus., 62 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aStudies in Systems, Decision and Control, _x2198-4190 ; _v478 |
|
500 | _aAcceso multiusuario | ||
505 | 0 | _aIntroduction to Complex System Resilience -- Optimal Control Approach to Identifying Cascading Failures -- Jacobian-free Newton-Krylov Method for Risk Identification -- Security Monitoring using Converse Lyapunov Function -- Online Gaussian Process Learning for Security Assessment -- Risk Identification of Cascading Process under Protection -- Model Predictive Approach to Preventing Cascading Process -- Robust Optimization Approach to Uncertain Cascading Process -- Cooperative Control Methods for Relieving System Stress -- Distributed Optimization Approach to System Protection -- Reinforcement Learning Approach to System Recovery -- Summary and Future Work. | |
520 | _aThis book provides a systematic framework to enhance the ability of complex dynamical systems in risk identification, security assessment, system protection, and recovery with the assistance of advanced control and optimization technologies. By treating external disturbances as control inputs, optimal control approach is employed to identify disruptive disturbances, and online security assessment is conducted with Gaussian process and converse Lyapunov function. Model predictive approach and distributed optimization strategy are adopted to protect the complex system against critical contingencies. Moreover, the reinforcement learning method ensures the efficient restoration of complex systems from severe disruptions. This book is meant to be read and studied by researchers and graduates. It offers unique insights and practical methodology into designing and analyzing complex dynamical systems for resilience elevation. | ||
541 |
_fUABC ; _cPerpetuidad |
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650 | 0 | _aControl engineering. | |
650 | 0 | _aRobotics. | |
650 | 0 | _aAutomation. | |
650 | 0 | _aCooperating objects (Computer systems). | |
650 | 1 | 4 | _aControl, Robotics, Automation. |
650 | 2 | 4 | _aCyber-Physical Systems. |
650 | 2 | 4 | _aControl and Systems Theory. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789819930524 |
776 | 0 | 8 |
_iPrinted edition: _z9789819930548 |
776 | 0 | 8 |
_iPrinted edition: _z9789819930555 |
830 | 0 |
_aStudies in Systems, Decision and Control, _x2198-4190 ; _v478 |
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_zLibro electrónico _uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-981-99-3053-1 |
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