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Record Nr. |
UNINA9910350290103321 |
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Autore |
Peng Yongbo |
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Titolo |
Stochastic Optimal Control of Structures / / by Yongbo Peng, Jie Li |
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Pubbl/distr/stampa |
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Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (XII, 322 p. 170 illus., 86 illus. in color.) |
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Disciplina |
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Soggetti |
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Vibration |
Dynamical systems |
Dynamics |
Control engineering |
Mechanics |
Mechanics, Applied |
Calculus of variations |
Probabilities |
Vibration, Dynamical Systems, Control |
Control and Systems Theory |
Solid Mechanics |
Calculus of Variations and Optimal Control; Optimization |
Probability Theory and Stochastic Processes |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Preface -- Introduction -- Theoretical essentials -- PDEM based stochastic optimal control -- Probabilistic criteria of stochastic optimal control -- Generalized optimal control policy -- Stochastic optimal control of nonlinear structures -- Stochastic optimal control of wind-induced comfortability -- Stochastic optimal semi-active control of structures -- Shaking table test of controlled structures -- References -- Appendix A: Mapping from excitation vector to co-state vector -- Appendix B: Statistical linearization based LQG control -- Appendix C: Riccati matrix difference equation and discrete dynamic programming -- Index. |
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Sommario/riassunto |
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This book proposes, for the first time, a basic formulation for structural control that takes into account the stochastic dynamics induced by engineering excitations in the nature of non-stationary and non-Gaussian processes. Further, it establishes the theory of and methods for stochastic optimal control of randomly-excited engineering structures in the context of probability density evolution methods, such as physically-based stochastic optimal (PSO) control. By logically integrating randomness into control gain, the book helps readers design elegant control systems, mitigate risks in civil engineering structures, and avoid the dilemmas posed by the methods predominantly applied in current practice, such as deterministic control and classical linear quadratic Gaussian (LQG) control associated with nominal white noises. |
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