Circuits, Systems, and Signal Processing

, Volume 33, Issue 6, pp 1971–1984 | Cite as

Neural Adaptive Compensation Control for a Class of MIMO Uncertain Nonlinear Systems with Actuator Failures

Short Paper


A neural adaptive compensation control scheme for a class of multi-input multi-ouput (MIMO) uncertain nonlinear systems with actuator failures is proposed based on prescribed performance bound (PPB) transient performance which characterizes the convergence rate and maximum overshoot of the tracking error. RBF neural networks are used to approximate the error of plant model, the control law proposed can guarantee the asymptotic output tracking and closed-loop signal bounds. The control scheme is applied to a twin otter aircraft longitudinal nonlinear dynamics model in the presence of unknown failures. Simulation results demonstrate the effectiveness of the proposed method.


Adaptive compensation control Neural networks MIMO nonlinear systems Actuator failure Prescribed performance bound 



This work is supported by the National Nature Science Foundation of China under Grants 61074063 and National University Foundational Research Funds of NUAA under grants NZ2012007, NS2013032.


  1. 1.
    P.C. Bechlioulis, A.G. Rovithakis, Adaptive control with guaranteed transient and steady state tracking error bounds for strict feedback systems. Automatica 45(2), 532–538 (2009) CrossRefMathSciNetGoogle Scholar
  2. 2.
    M. Bodson, J.E. Groszkiewicz, Multivariable adaptive algorithms for reconfigurable flight control. IEEE Trans. Control Syst. Technol. 5(2), 217–229 (1997) CrossRefGoogle Scholar
  3. 3.
    J.D. Boskovic, J.A. Jackson, R.K. Mehra et al., Multiple-model adaptive fault-tolerant control of a planetary lander. J. Guid. Control Dyn. 32(6), 1812–1826 (2009) CrossRefGoogle Scholar
  4. 4.
    M. Chen, C. Jiang, Q. Zong, Adaptive H control of a class of uncertain nonlinear systems based on RBF neural networks. J. Control Theory Appl. 20(1), 27–32 (2003) Google Scholar
  5. 5.
    M.L. Corradini, G. Orlando, Actuator failure identification and compensation through sliding modes. IEEE Trans. Control Syst. Technol. 15(1), 184–190 (2007) CrossRefGoogle Scholar
  6. 6.
    J. Jiang, Design of reconfigurable control systems using eigenstructure assignments. International Journal of Control, 395–410 (1994) Google Scholar
  7. 7.
    P. Li, G. Yang, Adaptive fuzzy control of unknown nonlinear systems with actuator failures for robust output tracking, in Proceedings of the 2005 American Control Conference (IEEE, New York, 2005), pp. 4862–4867 Google Scholar
  8. 8.
    D. Looze, J.L. Weiss, J. Eterno et al., An automatic redesign approach for restructurable control systems. IEEE Control Syst. Mag. 5(2), 16–22 (1985) CrossRefGoogle Scholar
  9. 9.
    S. Mohammad-Hoseini, M. Farrokhi, A.J. Koshkouei, Robust adaptive control of uncertain non-linear systems using neural networks. Int. J. Control 81(8), 1319–1330 (2008) CrossRefMATHMathSciNetGoogle Scholar
  10. 10.
    X. Qiu, S. Zhang, C. Liu, Backstepping adaptive compensation control for a class of MIMO nonlinear systems with actuator failures, in Proceedings of the 32nd Chinese Control Conference, Xian, China (2013), pp. 6088–6093 Google Scholar
  11. 11.
    W. Sun, H. Gao, Adaptive backstepping control for active suspension systems with hard constraints. IEEE/ASME Trans. Mechatron. 18(3), 3889–3895 (2013) MathSciNetGoogle Scholar
  12. 12.
    W. Sun, Z. Zhao, H. Gao, Saturated adaptive robust control for active suspension systems. IEEE Trans. Ind. Electron. 60(9), 1072–1079 (2013) Google Scholar
  13. 13.
    X. Tang, G. Tao, S.M. Joshi, Adaptive actuator failure compensation for parametric strict feedback systems and an aircraft application. Automatica 39(11), 1975–1982 (2003) CrossRefMathSciNetGoogle Scholar
  14. 14.
    X. Tang, G. Tao, S.M. Joshi, Virtual grouping based adaptive actuator failure compensation for MIMO nonlinear systems. IEEE Trans. Autom. Control 50(11), 1775–1780 (2005) CrossRefMathSciNetGoogle Scholar
  15. 15.
    X. Tang, G. Tao, S.M. Joshi, Adaptive actuator failure compensation for nonlinear MIMO systems with an aircraft control application. Automatica 43(11), 1869–1883 (2007) CrossRefMathSciNetGoogle Scholar
  16. 16.
    G. Tao, Adaptive control of systems with actuator failures, in Control and Decision Conference, Yantai, China (2008), pp. 53–54 Google Scholar
  17. 17.
    W. Wang, C. Wen, Adaptive actuator failure compensation control of uncertain nonlinear systems with guaranteed transient performance. Automatica 46 (12), 2082–2091 (2010) CrossRefMathSciNetGoogle Scholar
  18. 18.
    J. Yao, Z. Jiao, B. Yao et al., Nonlinear adaptive robust force control of hydraulic load simulator. Chin. J. Aeronaut. 25(5), 766–775 (2012) CrossRefGoogle Scholar
  19. 19.
    J. Yao, Z. Jiao, D. Ma, Adaptive robust control of DC motors with extended state observer. IEEE Trans. Ind. Electron. (2013) doi: 10.1109/TIE.2013.2281165 Google Scholar
  20. 20.
    J. Yao, Z. Jiao, D. Ma et al., High-accuracy tracking control of hydraulic rotary actuators with modeling uncertainties. IEEE/ASME Trans. Mechatron. (2013). doi: 10.1109/TMECH.2013.2252360 Google Scholar
  21. 21.
    Y. Zhang, J. Jiang, Bibliographical review on reconfigurable fault tolerant control systems. Annu. Rev. Control 32(2), 229–252 (2008) CrossRefGoogle Scholar
  22. 22.
    S. Zhang, C. Liu, S. Hu, Robust adaptive control for a class of nonlinear systems using backstepping based on RBF neural network. J. Syst. Eng. Electron. 32(3), 635–637 (2010) Google Scholar
  23. 23.
    S. Zhang, C. Liu, S. Hu, Adaptive fault-tolerant control for multi-input-multi-output minimum-phase systems with actuator failures. Control Theory Appl. 27(9), 1190–1194 (2010) Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Nanjing University of Aeronautics and AstronauticsNanjingChina

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