Estimation of Nonlinear Hybrid Systems Using Second-Order Q-Adaptive Self-switched Derivative-Free Estimators

  • Sayanti Chatterjee
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 626)


This paper introduces the adaptive versions of proposed self-switched estimators for a class of nonlinear hybrid systems. This proposed estimation scheme can eliminate the common disadvantage of conventional state estimators, that is the requirement of fairly accurate information about process noise covariances. To obtain a good compromise about computational complexity and estimation accuracy, a Q-adaptive (QA) state estimator based on derivative-free estimators like second-order CDKF and first-order CDKF has been proposed and employed in this work. The efficacy of the proposed estimators in comparison with QAEKF has been demonstrated through simulation studies on a benchmark problem, namely chemical stirred tank reactor (CSTR).


CDKF Q-adaptation Estimation Nonlinear hybrid system CSTR 


  1. 1.
    C.G. Cassandras, J. Lygeros, Stochastic Hybrid Systems (CRC Press, Taylor & Francis Group, LLC, 2007)Google Scholar
  2. 2.
    M. Buss, M. Glocker, M. Hardt, O. von Stryk, R. Bulirsch, G. Schmidt, Nonlinear hybrid dynamical systems: modeling, optimal control, and applications, in Modelling, Analysis, and Design of Hybrid Systems, ed. by E.G. Frehse, E. Schnieder (Springer, 2010)Google Scholar
  3. 3.
    A. Almagbile, J. Wang, W. Ding, Evaluating the performances of adaptive Kalman filter methods in GPS/ INS integration. J. Glob. Positioning Syst. 9(1), 33–40 (2010)CrossRefGoogle Scholar
  4. 4.
    A.H. Mohamed, K.P. Schwarz, Adaptive filtering for INS/GPS. J. Geodesy 73, 193–203 (1999)CrossRefGoogle Scholar
  5. 5.
    H.E. Soken, C. Hajiyev, A Novel Adaptive Unscented Kalman Filter For Pico Satellite Attitude Estimation. PHYSCON 2011, León (2011, September, 5)Google Scholar
  6. 6.
    K. Ito, K. Xiong, Gaussian filters for nonlinear filtering problems. IEEE Trans. Autom. Control 45(5), 910–927 (2000)MathSciNetCrossRefGoogle Scholar
  7. 7.
    T.S. Schei, A finite-difference method for linearization in nonlinear estimation. Automatica 33(11), 2053–2058 (1997)MathSciNetCrossRefGoogle Scholar
  8. 8.
    S. Chatterjee, S. Sadhu, T.K. Ghoshal, Fault detection and of non-linear hybrid system using self-switched sigma point filter bank. IET Control Theor. Appl. 9(7), 1093–1102 (2015)CrossRefGoogle Scholar
  9. 9.
    W. Wang, L. Li, D. Zhou, K. Liu, Robust state estimation and fault diagnosis for uncertain hybrid nonlinear systems. Nonlinear Anal. Hybrid Syst. 1(1), 2–15 (2007)MathSciNetCrossRefGoogle Scholar
  10. 10.
    S. Chatterjee, Improved fault detection and for nonlinear hybrid systems using self-switched CDKF. Selected for Presentation IEEE Indicon 2015 (New Delhi, India, 17–20 2015)Google Scholar
  11. 11.
    S. Chatterjee, S. Sadhu, T.K. Ghoshal, Improved estimation and fault detection method for a class of nonlinear hybrid systems using self switched sigma point filter. In 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC) (IEEE, 31 Jan 2014), pp. 578–582Google Scholar
  12. 12.
    S. Tafazoli, Hybrid system state tracking and fault detection using particle filters. IEEE Trans. Autom. Control 14(6), 1078–1087 (2006)Google Scholar
  13. 13.
    A. Mirzaee, K. Salahshoor, Fault diagnosis and accommodation of nonlinear systems based on multiple-model adaptive unscented Kalman filter and switched MPC and H-infinity loop-shaping controller. J. Process Control 22(3), 626–634 (2012)CrossRefGoogle Scholar
  14. 14.
    S. Chatterjee, S. Sadhu, T.K. Ghoshal, Improved estimation and fault detection scheme for a class of non-linear hybrid systems using time delayed adaptive CD state estimator. IET-Signal Process. 11(7), 771–779 (2017)CrossRefGoogle Scholar
  15. 15.
    S. Chatterjee, S. Sadhu, T.K. Ghoshal, Self-switched R-adaptive extended kalman filter based state estimation and mode determination for nonlinear hybrid systems. Computer, Communication, Control and Information Technology (Kolkata, India, 2014), pp. 1–6Google Scholar
  16. 16.
    F. Cadini, E. Zio, G. Peloni, Particle filtering for the detection of fault onset time in hybrid dynamic systems with autonomous transitions. IEEE Trans. Reliab. 61(1), 130–139 (2012)CrossRefGoogle Scholar
  17. 17.
    C. Andrieu, A. Doucet, E. Punskaya, Sequential Monte Carlo methods for optimal filtering, in Sequential Monte Carlo Methods in Practice (Springer, New York, 2001)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Sayanti Chatterjee
    • 1
  1. 1.Department of Electrical and Electronics EngineeringNarsimha Reddy Engineering CollegeHyderabadIndia

Personalised recommendations