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山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (5): 7-14.doi: 10.6040/j.issn.1672-3961.0.2017.173

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基于新型ESF的一类非线性系统故障滤波方法

李炜1,2,王可宏1,2*,曹慧超1,2   

  1. 1. 兰州理工大学电气工程与信息工程学院, 甘肃 兰州 730050;2. 甘肃省工业过程先进控制重点实验室, 甘肃 兰州 730050
  • 收稿日期:2017-02-10 出版日期:2017-10-20 发布日期:2017-02-10
  • 通讯作者: 王可宏(1990— ),男,甘肃靖远人,硕士,主要研究方向为故障诊断与容错控制.E-mail:lzhwkh@163.com E-mail:liwei@lut.cn
  • 作者简介:李炜(1963— ),女,陕西西安人,教授,博士生导师,硕士,主要研究方向为故障诊断与容错控制.E-mail:liwei@lut.cn
  • 基金资助:
    国家自然科学基金资助项目(61364011,6146330);甘肃省青年科技基金资助项目(1610RJYA013)

A fault filtering method based on an improved extended state filter for nonlinear system

LI Wei1,2, WANG Kehong1,2*, CAO Huichao1,2   

  1. 1.College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, Gansu, China;
    2. Key Lab of Advanced Control for Industrial Process in Gansu Province, Lanzhou 730050, Gansu, China
  • Received:2017-02-10 Online:2017-10-20 Published:2017-02-10

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摘要： 针对传统非线性系统故障诊断方法中存在的线性化误差、滤波发散等问题,对一类含有测量噪声及执行器时变故障的非线性系统研究一类新的故障诊断方法。通过将系统的非线性动态及故障扩张为一新的状态,构造一种可用于故障诊断的扩张状态滤波器,并在此基础上推证出增广系统渐进稳定的充分条件,给出具有鲁棒H∞性能的故障诊断滤波器设计方法,同时借助于原系统中已知的非线性动态,对滤波后的新扩张状态进行故障分离,实现对故障的滤波及诊断。用该方法对具有测量噪声的典型非线性系统Van der pol振荡器,在分别发生恒值和时变故障情形下进行仿真研究。结果表明,所提方法能较好地解决含噪非线性系统的滤波及故障诊断问题。

关键词: 故障分离, 扩张状态滤波器, 故障诊断, 测量噪声, 非线性系统

Abstract: Aiming at the problem of the linearization error and filtering divergence for traditional fault diagnosis method of nonlinear system, a new fault diagnosis method was studied for a class nonlinear system with the measurement noise and actuator fault of time-varying. A new extended state including the nonlinear dynamic and fault of the system was constructed, and a class of the filter containing expansion stateis was constructed for fault diagnosis. The sufficient conditions of the augmented system were presented, meanwhile, the design method of the fault diagnosis filter with the robust H∞ performance was given. And the filter and diagnosis of the fault could be implemented by the fault isolation for the new extended state, among the known nonlinear dynamic of the original system. Based on the Van der pol oscillator that was typical nonlinear system with measurement noise, the simulation study for the constant value and time-varying failure condition was performed. The results showed that the proposed method could better solve noise filtering and the fault diagnosis problem of nonlinear systems.

Key words: extended state filter, measurement noise, fault isolation, nonlinear system, fault diagnosis

中图分类号: 

  • U226.8+1
[1] 张翼, 季海波, 王刚. 含有动态输入不确定性的随机非线性系统的鲁棒控制[J]. 中国科学技术大学学报, 2010, 40(8): 854-862. ZHANG Yi, JI Haibo, WANG Gang. Robust control of stochastic nonlinear systems with input dynamic uncertainties[J]. Journal of University of Science and Technology of China, 2010, 40(8): 854-862.
[2] KALMAN R E. A new approach to linear filtering and prediction problems[J]. Journal of Basic Engineering, 1960, 82(1): 35-45.
[3] CHETOUANI Y. Using the Kalman filtering for the fault detection and isolation(FDI)in the nonlinear dynamic processes[J]. International Journal of Chemical Reactor Engineering, 2008, 6(1): 226-237.
[4] JULIER S J, UHLMANN J K, DURRANT-WHYTE H F. A new approach for filtering nonlinear systems[C] //Proceedings of American Control Conference.Washington: IEEE, 1995: 1628-1632.
[5] GHESMAT M, KHALKHALI A. Design of a new nonlinear model predictive fault tolerant control system using multi-sensor data fusion technique based on UKF algorithm[J]. The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 2015, 34(4): 1286-1301.
[6] MIRZAEE A, SALAHSHOOR K. Fault diagnosis and accommodation of nonlinear systems based on multiple-model adaptive unscented kalman filter and switched MPC and hinfinity loop-shaping controller[J]. Journal of Process Control, 2012, 22(3): 626-634.
[7] LIU Y, SUN D, CUI Y. Distributed fault detection for discrete-time nonlinear systems: An innovation-based approach[C] //Chinese Control Conference of 2011. Yantai, China: IEEE, 2011:4194-4199.
[8] ALKOV I, WEIDEMANN D. Fault detection with unscented kalman filter applied to nonlinear differential-algebraic systems[C] //Methods and Models in Automation and Robotics. Miedzyzdroje, Poland: IEEE, 2013: 166-171.
[9] GORDON N J, SALMOND D J, SMITH A F M. Novel approach to nonlinear/non-gaussian bayesian state estimation[J]. Radar & Signal Processing IEEE Proceedings F, 2002, 140(2):107-113.
[10] YIN S, ZHU X. Intelligent particle filter and its application to fault detection of nonlinear system[J]. Industrial Electronics, IEEE Transactions on, 2015, 62(6): 3852-3861.
[11] LI P, KADIRKAMANATHAN V. Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systems[J]. IEEE Transactions on Systems, Man and Cybernetics: Part C: Applications and Reviews, 2001, 31(3): 337-343.
[12] RIGATOS G G. Particle filtering for state estimation in nonlinear industrial systems[J]. Instrumentation and Measurement, 2009, 58(11): 3885-3900.
[13] 高志强. 自抗扰控制思想探究[J]. 控制理论与应用, 2013, 30(12): 1498-1510. GAO Zhiqiang. On the foundation of active disturbance rejection control[J]. Control Theory & Applications, 2013, 30(12): 1498-1510.
[14] GERMAIN G, JACQUES B, DENIS A. Robust stabilization of discrete-time linear systems with norm 2 bounded time varying uncertainty[J].Systems and Control Letters, 1994, 22(5):327-333.
[15] ZHOU Benchuan, CHENG Xianghong. Robust UKF algorithm in SINS initial alignment[J].Journal of Southest University(English edition), 2011, 27(1):56-60.
[16] JUTLEY J J, ACEVES A B. Spontaneous pt-symmetry breaking in nonlinearly coupled van der pol oscillators[J]. Studies in Applied Mathematics, 2016, 137(2):256-270.
[17] PARSHIN D V, UFIMTSEVA I V, CHEREVKO A A, et al. Differential properties of van der pol-duffing mathematical model of cerebrovascular haemodynamics based on clinical measurements[C] //All-Russian Conference on Nonlinear Waves:Theory and New Applications. Beijing: Institute of Computational Mathematics of Scientific, 2016, 722(1): 12-30.
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