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CLC number: TP27; V24
On-line Access: 2017-07-31
Received: 2016-06-25
Revision Accepted: 2016-10-14
Crosschecked: 2022-04-22
Cited: 0
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Flight control for air-breathing hypersonic vehicles using linear quadratic regulator design based on stochastic robustness analysis
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Lin Cao, Shuo Tang, Dong Zhang. Flight control for air-breathing hypersonic vehicles using linear quadratic regulator design based on stochastic robustness analysis[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(7): 882-897.
@article{title="Flight control for air-breathing hypersonic vehicles using linear quadratic regulator design based on stochastic robustness analysis",
author="Lin Cao, Shuo Tang, Dong Zhang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="7",
pages="882-897",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601363"
}
%0 Journal Article
%T Flight control for air-breathing hypersonic vehicles using linear quadratic regulator design based on stochastic robustness analysis
%A Lin Cao
%A Shuo Tang
%A Dong Zhang
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 7
%P 882-897
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601363
TY - JOUR
T1 - Flight control for air-breathing hypersonic vehicles using linear quadratic regulator design based on stochastic robustness analysis
A1 - Lin Cao
A1 - Shuo Tang
A1 - Dong Zhang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 7
SP - 882
EP - 897
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601363
Abstract: The flight dynamics model of air-breathing hypersonic vehicles (AHVs) is highly nonlinear and multivariable coupling, and includes inertial uncertainties and external disturbances that require strong, robust, and high-accuracy controllers. In this paper, we propose a linear-quadratic regulator (LQR) design method based on stochastic robustness analysis for the longitudinal dynamics of AHVs. First, input/output feedback linearization is used to design LQRs. Second, subject to various system parameter uncertainties, system robustness is characterized by the probability of stability and desired performance. Then, the mapping relationship between system robustness and LQR parameters is established. Particularly, to maximize system robustness, a novel hybrid particle swarm optimization algorithm is proposed to search for the optimal LQR parameters. During the search iteration, a Chernoff bound algorithm is applied to determine the finite sample size of Monte Carlo evaluation with the given probability levels. Finally, simulation results show that the optimization algorithm can effectively find the optimal solution to the LQR parameters.
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