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CLC number: TP27; V24

On-line Access: 2017-07-31

Received: 2016-06-25

Revision Accepted: 2016-10-14

Crosschecked: 2017-07-12

Cited: 0

Clicked: 4069

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Lin Cao

http://orcid.org/0000-0002-8943-9479

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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.7 P.882-897

10.1631/FITEE.1601363


Flight control for air-breathing hypersonic vehicles using linear quadratic regulator design based on stochastic robustness analysis


Author(s):  Lin Cao, Shuo Tang, Dong Zhang

Affiliation(s):  College of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China; more

Corresponding email(s):   zhangdong@nwpu.edu.cn

Key Words:  Air-breathing hypersonic vehicles (AHVs), Stochastic robustness analysis, Linear-quadratic regulator (LQR), Particle swarm optimization (PSO), Improved hybrid PSO algorithm


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(3): 882-897.

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author="Lin Cao, Shuo Tang, Dong Zhang",
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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.

基于随机鲁棒性分析的吸气式高超声速飞行器线性二次调节器设计

概要:吸气式高超声速飞行器(air-breathing hypersonic vehicle, AHV)的飞行动力学模型具有高度非线性与多变量耦合等特性,且受到内部不确定性与外部干扰的综合影响,因此需要具有强鲁棒性与高精度的控制器。本文介绍了一种改进的基于随机鲁棒性分析的线性二次调节器(linear-quadratic regulator, LQR)设计方法,用于AHV的纵向飞行控制器设计。首先,应用输入输出反馈线性化技术设计LQR控制器。其次,基于系统参数的不确定性,将系统鲁棒性表征为满足稳定性与设计指标要求的概率,并构建系统鲁棒性与LQR参数之间的映射关系。为了实现系统鲁棒性最大化的目标,提出一种全新的混合粒子群优化算法对LQR的控制参数进行寻优计算。在优化迭代的过程中,使用切诺夫边界理论确定蒙特卡洛估计的随机样本量。最后,仿真结果表明该优化算法可以高效地获取满足设计要求的LQR控制参数最优解。

关键词:吸气式高超声速飞行器;随机鲁棒性分析;二次线性调节器;粒子群优化;改进混合粒子群算法

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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