Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-24T08:12:45.100Z Has data issue: false hasContentIssue false
  • English
  • Français

Robust and simple intelligent observer-based fault estimation and reconstruction for a class of non-linear systems: HIRM aircraft

Published online by Cambridge University Press:  23 March 2016

M.H. Khooban ,
M. Siahi  and
M.-R. Soltanpour
M.H. Khooban*
Affiliation:
Young Researchers and Elite Club, Garmsar Branch, Islamic Azad University, GarmsarIran
M. Siahi
Affiliation:
Department of Electrical Engineering, Garmsar Branch, Islamic Azad University, SemnanIran
M.-R. Soltanpour
Affiliation:
Department of Electrical Engineering, Aeronautical University of Science and Technology, Tehran, Iran
*
[email protected], [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper introduces an observer strategy, namely a Sliding Mode Observer (SMO), to realise the fault detection and estimation of general uncertain non-linear systems. The use of a non-linear observer is considered for monitoring the states of a high incidence research model (HIRM) aircraft system. For a special class of Lipschitz non-linear system, a fault reconstruction scheme is presented where the reconstructed signal can approximate the fault signal to any accuracy. The proposed method is based only on the available plant input-output information and can be calculated online. Moreover, the globally asymptotic stability of the closed-loop system is mathematically proved. Finally, an HIRM aircraft system example is given to illustrate the efficiency of the proposed approach.

Keywords

Sliding mode observerNon-linear systemsState estimationFault reconstructionHIRM Aircraft
Type
Research Article
Information
The Aeronautical Journal , Volume 120 , Issue 1225 , March 2016 , pp. 457 - 472
Copyright
Copyright © Royal Aeronautical Society 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1.Chen, J. and Patton, R.J.Robust Model-Based Fault Diagnosis for Dynamic Systems¸ 1999, Kluwer, Boston, Massachusetts, US.Google Scholar
2.Khooban, M.H., Siahi, M. and Soltanpour, M.R.Model-based fault detection and estimation in robotic wheelchair using sliding mode observer, Research J of Applied Sciences, Engineering and Technology, 2012, 4, (13), pp 2009–2016.Google Scholar
3.Kalman, R.E.On a new approach to filtering and prediction problems. ASME J Basic Engineering, 1976, 24, pp 705–718.Google Scholar
4.Gevers, M. and Bastin, G.A Stable Adaptive Observer for a Class of Nonlinear Second-Order Systems, in Analysis and Optimization of Systems, Bensoussan and Lions (Eds), 1986, Springer-Verlag, New York, New York, US, pp 143–155.Google Scholar
5.Hammouri, J.P. and Othman, H.A simple observer for nonlinear systems, IEEE Transactions on Automatic Control, 1992, 37, (6), pp 875–880.Google Scholar
6.Utkin, V.I.Sliding Modes in Control Optimization, 1992, Springer-Verlag, Berlin, Germany.Google Scholar
7.Walcott, B.L. and Zak, S.H. Observation of dynamical systems in the presence of bounded nonlinearities/uncertainties, Proceedings of the 25th IEEE Conference on Decision and Control, 1986, Athens, Greece, pp 961–966.Google Scholar
8.Edwards, C. and Spurgeon, S.K.On the development of discontinuous observers, Int J of Control, 1994, 59, (5), pp 1211–1229.CrossRefGoogle Scholar
9.Thein, M.-W.L. and Misawa, E.A. Comparison of the sliding observer to several state estimators using a rotational inverted pendulum, Proceedings of the 34th Conference on Decision and Control, 1995, New Orleans, Louisiana, US, pp 3385–3390.Google Scholar
10.Frank, P.M.Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results, Automatica, 1990, 26, (3), pp 459–474.Google Scholar
11.Magni, J.F. and Mouyon, P.On residual generation by observer and parity space approaches, IEEE Transactions on Automatic Control, 1994, 39, (2), p 447.CrossRefGoogle Scholar
12.Edwards, C. and Spurgeon, S.K.Sliding Mode Control: Theory and Applications, 1998, CRC Press, UK.Google Scholar
13.Walcott, B.L. and Zak, S.H.Combined observer-controller synthesis for uncertain dynamical systems with applications, IEEE Transactions on Systems, Man and Cybernetics, 1988, 18, (1), pp 88–104.Google Scholar
14.Edwards, C., Spurgeon, S.K. and Patton, R.J.Sliding mode observers for fault detection and isolation, Automatica, 2000, 36, (4), pp 541–553.Google Scholar
15.Edwards, C. and Spurgeon, S.On the development of discontinuous observers, Int J of Control, 1994, 59, (5), pp 1211–1229.Google Scholar
16.Tan, C.P. and Edwards, C.An LMI approach for designing sliding mode observers, Int J of Control, 2001, 74, (16), pp 1559–1568.Google Scholar
17.Xiang, J., Su, H. and Chu, J. On the design of Walcott-Zak sliding mode observer, Proceedings of the 2005 American Control Conference, 2005, pp 2451–2456.Google Scholar
18.Lin, Z., Xia, Y., Shi, P. and Wu, H.Robust sliding mode control for uncertain linear discrete systems independent of time-delay, Int J of Innovative Computing, Information and Control, 2011, 7, (2), pp 869–880.Google Scholar
19.Shi, P., Xia, Y., Liu, G.P. and Rees, D.On designing of sliding-mode control for stochastic jump systems, IEEE Transactions on Automatic Control, 2006, 51, (1), pp 97–103.Google Scholar
20.Chang, Y.H., Wu, C.I., Lin, H.W., Chen, H.C. and Chang, C.W.Fractional order integral sliding-mode flux observer for direct field-oriented induction machines, Int J of Innovative Computing, Information and Control, 2012, 8, 7(A), pp 4851–4868.Google Scholar
21.Khan, Q., Bhatti, A.I., Iqbal, M. and Ahmed, Q.Dynamic integral sliding mode control for SISO uncertain nonlinear systems, Int J of Innovative Computing, Information and Control, 2012, 8, 7(A), pp 4621–4633.Google Scholar
22.Wu, L., Shiand, P. and Gao, H.State estimation and sliding-mode control of Markovian jump singular systems, IEEE Transactions on Automatic Control, 2010, 55, (5), pp 1213–1219.Google Scholar
23.Soltanpour, M.R., Zolfaghari, B., Soltani, M. and Khooban, M.H.Fuzzy sliding mode control design for a class of nonlinear systems with structured and unstructured uncertainties, Int J of Innovative Computing, Information and Control, 2013, 9, (7), pp 2713–2726.Google Scholar
24.Mokhtar, S., Khooban, M.H. and Niknam, T.A robust and simple optimal type II fuzzy sliding mode control strategy for a class of nonlinear chaotic systems. J of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 2014, 27, (4), pp 1849–1859.Google Scholar
25.Soltanpour, M.R., Khooban, M.H. and Soltani, M.Robust fuzzy sliding mode control for tracking the robot manipulator in joint space and in presence of uncertainties, Robotica, 2014, 32, (03), pp 433–446.Google Scholar
26.Soltanpour, M.R. and Khooban, M.H.A particle swarm optimization approach for fuzzy sliding mode control for tracking the robot manipulator, Nonlinear Dynamics, 2013, 74, (1-2), pp 467–478.Google Scholar
27.Soltanpour, M. R., Otadolajam, P. and Khooban, M. H.Robust control strategy for electrically driven robot manipulators: adaptive fuzzy sliding mode, IET Science, Measurement & Technology, 2014, 9, (3), pp 322–334.Google Scholar
28.Jiang, B., Shi, P. and Mao, Z.Sliding mode observer-based fault estimation for nonlinear networked control systems, Circuits, Systems, and Signal Processing, 2011, 30, (1), pp 1–16.Google Scholar
29.Qiu, J., Ren, M., Niu, Y., Zhao, Y. and Guo, Y.Fault estimation for nonlinear dynamic systems, Circuits, Systems, and Signal Processing, 2012, 31, (2), pp 555–564.Google Scholar
30.Yan, X. and Edwards, C.Nonlinear robust fault reconstruction and estimation using a sliding mode observer, Automatica, 2007, 43, (9), pp 1605–1614.Google Scholar
31.Veluvolu, K.C. and Soh, Y.C.Fault reconstruction and state estimation with sliding mode observers for Lipschitz non-linear systems, IET Control Theory & Applications, 2011, 5, (11), pp 1255–1263.Google Scholar
32.Yan, X.G. and Edwards, C.Fault estimation for single output nonlinear systems using an adaptive sliding mode estimator, IET Control Theory & Applications, 2008, 2, (10), pp 841–850.Google Scholar
33.Magni, J.F., Bennani, S. and Terlouw, J.Robust Flight Control: A Design Challenge, Lecture Notes in Control and Information Sciences, 224, 1997, Springer-Verlag, Berlin, Germany.Google Scholar
34.Huzmezan, M. Theory and Aerospace Applications of Constrained Model Based Predictive Control, 1998, PhD thesis, Department of Engineering, University of Cambridge, UK.Google Scholar
35.Yan, X. and Edwards, C. Robust sliding mode observer-based actuator fault detection and isolation for a class of nonlinear systems, 44th IEEE Conference on Decision and Control, and the European Control Conference 2005, 12-15 December 2005, Seville, Spain.Google Scholar