CLC number: O34
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2009-07-09
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Lin-cong CHEN, Rong-hua HUAN, Wei-qiu ZHU. Feedback maximization of reliability of MDOF quasi integrable-Hamiltonian systems under combined harmonic and white noise excitations[J]. Journal of Zhejiang University Science A, 2009, 10(9): 1245-1251.
@article{title="Feedback maximization of reliability of MDOF quasi integrable-Hamiltonian systems under combined harmonic and white noise excitations",
author="Lin-cong CHEN, Rong-hua HUAN, Wei-qiu ZHU",
journal="Journal of Zhejiang University Science A",
volume="10",
number="9",
pages="1245-1251",
year="2009",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0820316"
}
%0 Journal Article
%T Feedback maximization of reliability of MDOF quasi integrable-Hamiltonian systems under combined harmonic and white noise excitations
%A Lin-cong CHEN
%A Rong-hua HUAN
%A Wei-qiu ZHU
%J Journal of Zhejiang University SCIENCE A
%V 10
%N 9
%P 1245-1251
%@ 1673-565X
%D 2009
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0820316
TY - JOUR
T1 - Feedback maximization of reliability of MDOF quasi integrable-Hamiltonian systems under combined harmonic and white noise excitations
A1 - Lin-cong CHEN
A1 - Rong-hua HUAN
A1 - Wei-qiu ZHU
J0 - Journal of Zhejiang University Science A
VL - 10
IS - 9
SP - 1245
EP - 1251
%@ 1673-565X
Y1 - 2009
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0820316
Abstract: We studied the feedback maximization of reliability of multi-degree-of-freedom (MDOF) quasi integrable-Hamiltonian systems under combined harmonic and white noise excitations. First, the partially averaged Itô equations are derived by using the stochastic averaging method for quasi integrable-Hamiltonian systems under combined harmonic and white noise excitations. Then, the dynamical programming equation and its boundary and final time conditions for the control problems of maximizing the reliability is established from the partially averaged equations by using the dynamical programming principle. The nonlinear stochastic optimal control for maximizing the reliability is determined from the dynamical programming equation and control constrains. The reliability function of optimally controlled systems is obtained by solving the final dynamical programming equation. Finally, the application of the proposed procedure and effectiveness of the control strategy are illustrated by using an example.
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