A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search
Volume 7 Issue 2
Mar.  2020

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(2): 517-526
Hamed Kazemi and Alireza Yazdizadeh, "Optimal State Estimation and Fault Diagnosis for a Class of Nonlinear Systems," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 517-526, Mar. 2020. doi: 10.1109/JAS.2020.1003051
Citation: Hamed Kazemi and Alireza Yazdizadeh, "Optimal State Estimation and Fault Diagnosis for a Class of Nonlinear Systems," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 517-526, Mar. 2020. doi: 10.1109/JAS.2020.1003051
shu

Optimal State Estimation and Fault Diagnosis for a Class of Nonlinear Systems

doi: 10.1109/JAS.2020.1003051

  • The authors are with the Department of Electrical Engineering, Shahid Beheshti University, Tehran 1983963113, Iran

More Information
    Abstract
  • This study proposes a scheme for state estimation and, consequently, fault diagnosis in nonlinear systems. Initially, an optimal nonlinear observer is designed for nonlinear systems subject to an actuator or plant fault. By utilizing Lyapunov's direct method, the observer is proved to be optimal with respect to a performance function, including the magnitude of the observer gain and the convergence time. The observer gain is obtained by using approximation of Hamilton-Jacobi-Bellman (HJB) equation. The approximation is determined via an online trained neural network (NN). Next a class of affine nonlinear systems is considered which is subject to unknown disturbances in addition to fault signals. In this case, for each fault the original system is transformed to a new form in which the proposed optimal observer can be applied for state estimation and fault detection and isolation (FDI). Simulation results of a single-link flexible joint robot (SLFJR) electric drive system show the effectiveness of the proposed methodology.

     

    • FullText(HTML)
  • loading
    • Recommended by Associate Editor Changhua Hu
    References(32)
  • [1]
    Y. Yang, S. X. Ding, and L. Li, "Parameterization of nonlinear observer-based fault detection systems, " IEEE Trans. Autom. Control, vol. 61, pp. 3687-3692, 2016. doi: 10.1109/TAC.2016.2532381
    [2]
    Y. Ma, B. Li, G. Li, J. Zhang, and H. A. Chen, "Nonlinear observer approach of SOC estimation based on hysteresis model for lithium-ion battery, " IEEE/CAA J. Autom. Sinica, vol. 4, pp. 195-204, 2017. doi: 10.1109/JAS.2017.7510502
    [3]
    S. Delshad, A. Johansson, M. Darouach, and T. Gustafsson, "Robust state estimation and unknown inputs reconstruction for a class of nonlinear systems: multiobjective approach, " Automatica, vol. 64, pp. 1-7, 2016. doi: 10.1016/j.automatica.2015.10.051
    [4]
    W. H. Zhang, Z. H. Wang, T. Raissi, Y. Wang, and Y. Shen, "A state augmentation approach to interval fault estimation for descriptor systems, " European J. Control, vol. 51, pp. 19-29, 2020. doi: 10.1016/j.ejcon.2019.06.006
    [5]
    R. N. Ravindranathan and L. Behera, "Robust adaptive gain higher order sliding mode observer based control-constrained nonlinear model observer predictive control for spacecraft formation flying, " IEEE/CAA J. Autom. Sinica, pp. 1-15, 2016. https://ieeexplore.ieee.org/document/7783979/
    [6]
    Y. F. Li, L. L. Zhang, C. C. Hua, Y. Zhang, and X. P. Guan, "Output feedback control for stochastic nonlinear time delay systems using dynamic gain technique, " J. Franklin Institute, vol. 355, no. 3, pp. 1073-1087, 2018. doi: 10.1016/j.jfranklin.2018.01.001
    [7]
    A. Benabdallah, T. Kharrat, and J. C. Vivalda, "On practical observers for nonlinear uncertain systems, " Syst. Control Lett., vol. 57, pp. 371-377, 2008. doi: 10.1016/j.sysconle.2007.10.007
    [8]
    R. Ortega, A. Bobtsov, A. Pyrkin, and S. Aranovskiy, "A parameter estimation approach to state observation of nonlinear systems, " Syst. Control Lett., vol. 85, pp. 84-94, 2015. doi: 10.1016/j.sysconle.2015.09.008
    [9]
    M. Hou and P. C. Muller "Design of observers for linear systems with unknown inputs, " IEEE Trans. Autom. Control, vol. 37, no. 6, pp. 871-875, 1992. doi: 10.1109/9.256351
    [10]
    J. Chen and R. Patton, Robust Model-Based Fault Diagnosis for Dynamic Systems. Springer: Berlin, 2009.
    [11]
    Z. Gao, C. Cecati, and S. X. Ding, "A survey of fault diagnosis and fault-tolerant techniques Part I: fault diagnosis with model-based and signal-based approaches, " IEEE Trans. Industrial Electronics, vol. 62, pp. 3757-3767, 2015. doi: 10.1109/TIE.2015.2417501
    [12]
    D. Chen, H. Yang, and H. Tao, "Iterative learning fault diagnosis algorithm for non-uniform sampling hybrid system, " IEEE/CAA J. Autom. Sinica, vol. 1, pp. 1-9, 2017. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zdhxb-ywb201703014
    [13]
    X. G. Yan and C. Edwards, "Nonlinear robust fault reconstruction and estimation using a sliding mode observer, " Automatica, vol. 43, pp. 1605-1614, 2007. doi: 10.1016/j.automatica.2007.02.008
    [14]
    Z. Wang, Y. Shen, and X. Zhang, "Attitude sensor fault diagnosis based on Kalman filter of discrete-time descriptor system, " J. Systems Engineering and Electronics, vol. 23, pp. 914-920, 2012. doi: 10.1109/JSEE.2012.00112
    [15]
    A. Xu and Q. Zhang, "Residual generation for fault diagnosis in linear time-varying systems, " IEEE Trans. Autom. Control, vol. 49 no. 5, pp. 767-772, 2004. doi: 10.1109/TAC.2004.825983
    [16]
    R. Isermann, "Model-based fault detection and diagnosis-status and applications, " Annu. Rev. Control, vol. 29, pp. 71-75, 2005. doi: 10.1016/j.arcontrol.2004.12.002
    [17]
    H. Kazemi and A. Yazdizadeh, "Fault reconstruction in a class of nonlinear systems using inversion-based filter, " Nonlinear Dynamics, vol. 85, no.3, pp. 1805-1814, 2016. doi: 10.1007/s11071-016-2796-z
    [18]
    H. M. Qian, P. Yu, and G. Y. Yang, "Reduced-order observer-based fault estimation and fault-tolerant control for a class of discrete Lipschitz nonlinear systems, " Optimal Control Applications and Methods, vol. 37, no. 6, pp. 1236-1262, 2016. doi: 10.1002/oca.2235
    [19]
    C. De Persis and A. Isidori, "A geometric approach to nonlinear fault detection and isolation, " IEEE Trans. Autom. Control, vol. 46 no. 6, pp. 853-865, 2001. http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_823bd9a496a270c87beccd326c4565e2
    [20]
    M. A. Massoumnia, G. C. Verghese, and A. S. Willsky, "Failure detection and identification, " IEEE Trans. Autom. Control, vol. 34, pp. 316-321, 1989. doi: 10.1109/9.16422
    [21]
    M. Du and P. Mhaskar, "Isolation and handling of sensor faults in nonlinear systems, " Automatica, vol. 50 no. 4 pp. 1066-1074, 2014. doi: 10.1016/j.automatica.2014.02.017
    [22]
    D. M. Adhyaru, "State observer design for nonlinear systems using neural network, " Appl. Soft Comp., vol. 12, pp. 2530-2537, 2012. doi: 10.1016/j.asoc.2012.02.017
    [23]
    F. Zhu and F. Cen, "Full-order observer-based actuator fault detection and reduced-order observer-based fault reconstruction for a class of uncertain nonlinear systems, " J. Process Control, vol. 20, pp. 1141-1149, 2010. doi: 10.1016/j.jprocont.2010.06.021
    [24]
    R. Sharma and M. Aldeen, "Fault and disturbance reconstruction in non-linear systems using a network of interconnected sliding mode observers, " IET Control Theory Appl., vol. 5, no. 6, pp.751-763, 2011. doi: 10.1049/iet-cta.2009.0592
    [25]
    J. V. Gorpl, M. Defoort, and M. Djemai, "Fault detection based on higher-order sliding mode observer for a class of switched linear systems, " IET Control Theory Appl., vol. 9 no. 15, pp. 2249-2256, 2015. doi: 10.1049/iet-cta.2014.1004
    [26]
    T. Cheng, F. L. Lewis, and M. Abu-Khalaf, "Fixed final time constrained optimal control of nonlinear systems using neural network HJB approach, " IEEE Trans. Neural Network, vol. 18, no. 6, pp. 1725-1737, 2007. doi: 10.1109/TNN.2007.905848
    [27]
    S. E. Lyshevski, "Optimal control of nonlinear continuous-time systems: design of bounded controllers via generalized nonquadratic functionals, " in Proc. American Control Conf., pp. 205-209, 1998. http://www.gbv.de/dms/ilmenau/toc/318160692.PDF
    [28]
    M. Gopal, Modern Control System Theory. New Age International Publishers, 1993.
    [29]
    W. E. Sandberg, "Notes on uniform approximation of time-varying systems on finite time intervals, " IEEE Trans. Circuits Syst. I. Fundam. Theory, vol. 45, no. 8, 863-865, 1998. doi: 10.1109/81.704826
    [30]
    R. Beard, G. Saridis, and J. Wen, "Galerkin approximations of the generalized Hamilton Jacobi Bellman equation, " Automatica, vol. 33, pp. 2159-2177, 1997. doi: 10.1016/S0005-1098(97)00128-3
    [31]
    T. Cheng and F. L. Lewis, "Fixed-final time constrained optimal control of nonlinearsystems using neural network HJB approach, " in Proc. 45th IEEE Conf. Decision and Control, San Diego, USA, pp. 3016-3021, 2006. https://www.infona.pl/resource/bwmeta1.element.ieee-art-000004177174
    [32]
    M. Abu-Khalaf, J. Huang, and F. L. Lewis, Nonlinear H2/H∞ Constrained Feedback Control: A Practical Design Approach using Neural Networks. Springer, 2006.
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)  / Tables(1)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (1227) PDF downloads(75) Cited by()

    Highlights

    • This study proposes an optimal nonlinear observer for state estimation and fault diagnosis of nonlinear systems subject to an actuator or plant fault.
    • By utilizing Lyapunov's direct method, the observer is proved to be optimal with respect to a performance function, including the magnitude of the observer gain and the convergence time. The observer gain is obtained by using approximation of Hamilton-Jacobi-Bellman (HJB) equation. The approximation is determined via an online trained neural network (NN).
    • A class of affine nonlinear systems is considered which is subject to unknown disturbances in addition to fault signals. In this case, we transform the system to a form in which there is a subsystem that includes the fault signal and omits disturbances. Then, by utilizing the proposed observer, the subsystem states and corresponding fault signal can be estimated and diagnosed, respectively.
    • The transformation is done via an existing coordinate transformation based on the concept of observability co-distribution in differential geometry under appropriate technical hypotheses. For each fault of the original system, the fault detection procedure can be repeated. Thus, a bank of FDI observers is introduced for this class of systems.
    • Simulation results of a single-link flexible joint robot (SLFJR) electric drive system show the effectiveness of the proposed methodology.

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return