|
|
论文:2019,Vol:37,Issue(5):952-961 |
|
|
引用本文： |
|
|
呼卫军, 马一鸣, 周军. 基于相空间递归网络的鲁里叶系统稳定度量[J]. 西北工业大学学报 |
|
|
HU Weijun, MA Yiming, ZHOU Jun. Research of Mapping Element Space Method for Uncertain Lurie System Stability Diagnosis[J]. Northwestern polytechnical university |
|
|
|
|
|
|
|
| 基于相空间递归网络的鲁里叶系统稳定度量 |
|
| 呼卫军, 马一鸣, 周军 |
|
| 西北工业大学 航天学院, 陕西 西安 710072 |
| 摘要: |
| 针对考虑故障、不确定等特性的飞行控制系统稳定性的分析问题，提出了一种基于混沌时间序列数据分析的鲁里叶系统稳定度预测度量方法。首先采用鲁棒控制理论中的小增益理论和线性矩阵不等式，分析了导致鲁里叶非线性系统失稳的多种诱因，在理论上证明了鲁里叶系统在不确定和故障情况下的稳定条件。为了量化分析复杂鲁里叶非线性系统的稳定性，基于相空间重构理论将包含连续离散特性的非线性鲁里叶系统等价转换为近似时间离散方程，将其映射至低维的基元空间中，通过在核函数中引入q高斯函数，更好地增强了神经网络的泛化能力，实现了基于基元迁移特性的稳定性分析方法，适用于多种故障与不确定条件，并可给出相关的量化稳定范数。最终的仿真表明了所提出的方法可有效解决多种因素下的飞行控制系统稳定性分析判定。 |
| 关键词:
飞行控制
lurie系统
相空间理论
自递归神经网络
稳定性度量
|
|
| Research of Mapping Element Space Method for Uncertain Lurie System Stability Diagnosis |
|
| HU Weijun, MA Yiming, ZHOU Jun |
|
| School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China |
| Abstract: |
| A new diagnosis method of Lurie system stability by using Chaotic time series data was proposed in order to solve stability analysis of flight control system with fault and uncertain. Firstly, the reasons for the instability of the nonlinear system of Ruri leaf was analyzed by using the small gain theory and linear matrix inequalities in the robust control theory. And the stability conditions of Rui leaf system under the condition of uncertainty and failure were proved theoretically. In order to quantify the stability of nonlinear systems in complex Ruri leaves, based on the theory of phase space reconstruction, the nonlinear Ruri system with continuous discrete characteristics was converted into an approximate time discrete equation, mapping it to a low dimensional primitive space, by introducing Q Gauss function into Kernel function, the generalization ability of neural networks are enhanced, realizing the stability analysis method based on the characteristic of primitive migration, which is suitable for various fault and uncertainty conditions, and the relative quantized stability norm can be given. The simulation shows that the present method can effectively solve the stability analysis and determination of flight control system under various factors. |
| Key words:
flight control
Lurie system
mapping element space
self recurrent neural network
stability analysis
|
|
| 收稿日期: 2018-09-10
修回日期:
|
| DOI: 10.1051/jnwpu/20193750952 |
| 基金项目: 国家自然科学基金（61473226）资助 |
| 通讯作者:
Email： |
| 作者简介: 呼卫军(1979-),西北工业大学副教授,主要从事飞行器制导控制、故障诊断以及仿真技术研究。
|
|
| 相关功能 |
|
|
|
|
| 作者相关文章 |
|
| 呼卫军 在本刊中的所有文章 |
| 马一鸣 在本刊中的所有文章 |
| 周军 在本刊中的所有文章 |
|
|
|
|
|
|
|
|
参考文献: |
|
|
[1] LI H, GAO Y, SHI P, et al. Observer-Based Fault Detection for Nonlinear Systems with Sensor Fault Andlimited Communication Capacity[J]. IEEE Trans on Automatic Control, 2016, 61(9):2745-2751
[2] SERDIO F, LUGHOFER E, ZAVOIANU A C, et al. Improved Fault Detection Employing Hybrid Memetic Fuzzy Modeling and Adaptive Filters[J]. Applied Soft Computing, 2017, 51:60-82
[3] 胡志坤,孙岩,姜斌,等. 一种基于最优未知输入观测器的故障诊断方法[J]. 自动化学报,2013,39(8):1225-1230 HU Zhikun, SUN Yan, JIANG Bin, et al. An Optimal Unknown Input Observer Based Fault Diagnosis Method for the Drive Train System of Wind Turbine[J]. Acta Automatica Sinica, 2013, 39(8):1225-1230(in Chinese)
[4] ZOLGHADRI A. The Challenge of Advanced Model-Based Fdir for Real-World Flight-Critical Applications[J]. Engineering Applications of Artificial Intelligence, 2018, 68:249-259
[5] JUNE Chongvisal, NIKOLAS Tekles, ENRIC Xargay, et al. Talleur, Alex Kirlikk, Naira H-ovakimyan,Loss-of-Control Prediction and Prevention for NASA's Transport Class Model[C]//AIAA Guidance, Navigation, and Control Conference, National Harbor,Maryland, 2014
[6] SMITH C, JIN Y. Evolutionary Multi-Objective Generation of Recurrent Neural Network Ensembles for Time Series Prediction[J]. Neuro computing, 2014, 143:302-311
[7] Chen T T, Lee S J. A Weighted Ls-Svm Based Learning System for Time Series Forecasting[J]. Information Sciences, 2015, 299:99-116
[8] 胡瑜, 陈涛. 基于CC算法的混沌吸引子的相空间重构技术[J]. 电子测量与仪器学报, 2012, 5(10):425-430 HU Yu, CHEN Tao. Phase-Space Reconstruction Technology of Chaotic Attractor Based on CC Method[J]. J Electron Meas Instrum, 2012, 5(10):425-430(in Chinese)
[9] 郭小萍, 袁杰, 李元. 基于特征空间k最近邻的批次过程监视[J]. 自动化学报, 2014, 40(1):135-142 GUO Xiaoping, YUAN Jie, LI Yuan. Feature Space k Nearest Neighbor Based Batch Process Monitoring[J]. Acta Automatica Sinica, 2014, 40(1):135-142(in Chinese)
[10] DENG F, GUO S, ZHOU R, et al. Sensor Multifault Diagnosis with Improved Support Vector Machines[J]. IEEE Trans on Automation Science and Engineering, 2017, 14(2):1053-1063
[11] 韩敏,许美玲. 一种基于误差补偿的多元混沌时间序列混合预测模型[J]. 物理学报, 2013, 62(12):120510 HAN Min, XU Meiling. A Hybrid Prediction Model of Multivariate Chaotic Time Series Based on Error Correction[J]. Acta Phys Sin, 2013, 62(12):120510(in Chinese)
[12] MUANGKOTE N, SUNAT K, CHIEWCHANWATTANA S. An Improved Grey Wolf Optimizer for Training Q-Gaussian Radial Basis Functional-Link Nets[C]//2014 International Computer Science and Engineering Conference, 2014:209-214
[13] 李娟,王宇平. 基于样本密度和分类误差率的增量学习矢量量化算法研究[J]. 自动化学报, 2015, 41(6):1187-1200 LI Juan, WANG Yuping. An Incremental Learning Vector Quantization Algorithm Based on Pattern Density and Classification Error Ratio[J]. Acta Automatica Sinica, 2015, 41(6):1187-1200(in Chinese)
[14] 张景祥,王士同,邓赵红,等. 融合异构特征的子空间迁移学习算法[J]. 自动化学报,2014,40(2):236-246 ZHANG Jingxiang, WANG Shitong, DENG Zhaohong, et al. A Subspace Transfer Learning Algorithm Integrating Heterogeneous Features[J]. Acta Automatica Sinica, 2014, 40(2):236-246(in Chinese)
[15] AHOOKHOSH M, GHADERI S. On Efficiency of Nonmonotone Armijo-Type Line Searches[J]. Applied Mathematical Modelling, 2017, 43:170-190 |
|
|
|
|
|
|
|
