Nonlinear Dynamics

, Volume 77, Issue 3, pp 859–876 | Cite as

Local joint information based active fault tolerant control for reconfigurable manipulator

Original Paper


This paper is concerned with the active fault tolerant control problem for reconfigurable manipulator actuator based on local joint information. It is considered that the entire reconfigurable manipulator system consists of a couple of independent joint modules as subsystems, which are controlled using unified radial basis function neural network adaptive algorithm using local joint information when actuators are fault free. For the subsystem in actuator fault situation, fault detection is achieved through comparing the user defined threshold to the residual between actual velocity value and nonlinear velocity observation value. The unknown input state observer is exploited for fault identification. Based on the information aforementioned, a compensation term is added to the proposed control algorithm for switching to realize active fault tolerant control when subsystem in fault. The advantages of the presented scheme are that unlike the complex control structure in centralized control, this scheme possesses simple control structure, as well as could isolate and tolerant the fault in subsystem. Furthermore, it can be easily applied to different configurations without any parameters modification. It means that the local fault could not affect the joint in normal situation. In order to demonstrate the effectiveness of the proposed method, two different 2-DOF reconfigurable manipulators are employed for simulation.


Reconfigurable manipulators Local joint information  Fault detection and identification Active fault tolerant control Nonlinear velocity observer Radial basis function neural network 



The authors would like to thank the anonymous reviews, editors, Ph. D candidate Lei Liu at York University, Canada and Equipment Engineer Peng Lu of FAW CAR CO., Ltd. China for their valuable comments and constructed suggestions to improve the quality of this paper. This work is financially supported by the National Natural Science Foundation of China (61374051 and 60974010) and Scientific and Technological Development Plan Project in Jilin Province of China (20110705).


  1. 1.
    Paredis, C.J.J., Brown, H.B., Khosla, P.K.: Rapidly deployable manipulator system. Rob. Autom. Syst. 21(3), 289–304 (1997)CrossRefGoogle Scholar
  2. 2.
    Matsumaru T.: Design and control of the modular robot system: TOMMS. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2125–2131, IEEE Press, New York, USA (1995)Google Scholar
  3. 3.
    Pisu, P., Serrani, A., You, S., Jalics, L.: Adaptive threshold based diagnostics for steer-by -wire systems. J. Dyn. Syst. Meas. Control Trans. ASME 128(2), 428–435 (2006)CrossRefGoogle Scholar
  4. 4.
    Bakule, L.: Decentralized control: an overview. Annu. Rev. Control 32(1), 87–98 (2008)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Bakule, L., Papik, M.: Decentralized control and communication. Annu. Rev. Control 36(1), 1–10 (2012)CrossRefGoogle Scholar
  6. 6.
    Tseng, C.S.: A novel approach to \(H\infty \) decentralized fuzzy-observer-based fuzzy control design for nonlinear interconnected systems. IEEE Trans. Fuzzy Syst. 16(5), 1337–1350 (2008)CrossRefGoogle Scholar
  7. 7.
    Yang, Z.J., Fukushima, Y., Qin, P.: Decentralized adaptive robust control of robot manipulators using disturbance observers. IEEE Trans. Control Syst. Technol. 20(5), 1357–1365 (2012)CrossRefGoogle Scholar
  8. 8.
    Keviczkya, T., Borrellib, F., Balas, G.J.: Decentralized receding horizon control for large scale dynamically decoupled systems. Automatica 42(12), 2105–2115 (2006)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Khan, U.A., Moura, J.M.F.: Distributing the Kalman filter for large-scale systems. IEEE Trans. Signal Process. 56(10), 4919–4935 (2008)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Liu, G., Abdul, S., Goldenberg, A.A.: Distributed control of modular and reconfigurable robot with torque sensing. Robotica 26(1), 75–84 (2008)CrossRefGoogle Scholar
  11. 11.
    Zhu, M., Li, Y.: Decentralized adaptive fuzzy sliding mode control for reconfigurable modular manipulators. Int. J. Robust Nonlinear Control 20(4), 472–488 (2010)Google Scholar
  12. 12.
    Zhao, B., Wang, Z., Qiao, Y., Liu, K., Li, Y.: A Combined Backstepping Terminal Sliding Mode Algorithm Based Decentralized Control Scheme for Reconfigurable Manipulators. Advances in Reconfigurable Mechanisms and Robots I, pp. 657–668. Springer, London (2012)Google Scholar
  13. 13.
    Brambilla, D., Capisani, L.M., Ferrara, A., Pisu, P.: Second order sliding mode observers for fault detection of robot manipulators. In: Proceedings of the 47th IEEE Conference on Decision and Control, pp. 2949–2954. Cancun, Mexico (2008)Google Scholar
  14. 14.
    Zolghadri, A.: Advanced model-based FDIR techniques for aerospace systems: today challenges and opportunities. Prog. Aerosp. Sci. 53, 18–29 (2012)CrossRefGoogle Scholar
  15. 15.
    Shin, J., Lee, J.: Fault detection and robust fault recovery control for robot manipulators with actuator failures. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 861–866, Detroit, USA (1999)Google Scholar
  16. 16.
    Brambilla, D., Capisani, L.M., Ferrara, A.: Fault detection for robot manipulators via second-order sliding modes. IEEE Trans. Ind. Electron. 55(11), 3954–3963 (2008)CrossRefGoogle Scholar
  17. 17.
    Caccavale, F., Cilibrizzi, P., Pierri, F., Villani, L.: Actuators fault diagnosis for robot manipulators with uncertain model. Control Eng. Pract. 17, 146–157 (2009)CrossRefGoogle Scholar
  18. 18.
    Yoo, S.J.: Actuator fault detection and adaptive accommodation control of flexible-joint robots. IET Control Theory Appl. 6(10), 1497–1507 (2012)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Li, X.J., Yang, G.H.: Adaptive fault detection and isolation approach for actuator stuck faults in closed-loop systems. Int. J. Control Autom. Syst. 10(4), 830–834 (2012)CrossRefGoogle Scholar
  20. 20.
    Hsiao, T., Weng, M.C.: A hierarchical multiple-model approach for detection and isolation of robotic actuator faults. Robot Autom. Syst. 60(2), 154–166 (2012)CrossRefGoogle Scholar
  21. 21.
    Chen, W., Saif, M.: Actuator fault diagnosis for uncertain linear systems using a high-order sliding-mode robust differentiator (HOSMRD). Int. J. Robust Nonlinear Control 18(4–5), 413–426 (2008)CrossRefMATHMathSciNetGoogle Scholar
  22. 22.
    Christensen, A.L., O’Grady, R., Birattari, M., Dorigo, M.: Fault detection in autonomous robots based on fault injection and learning. Auton. Robot 24, 49–67 (2008)CrossRefGoogle Scholar
  23. 23.
    Datta, A., Patel, S., Mavroidis, C., Antoniadis, I., Krishnasamy, J., Hosek, M.: Fault diagnostics of industrial robots using support vector machines and discrete wavelet transforms. In: Proceedings of 2006 ASME International Mechanical Engineering Congress and Exposition, pp. 1–7, Chicago, USA (2006)Google Scholar
  24. 24.
    Silva, J.C.D., Saxena, A., Balaban, E., Goebel, K.: A knowledge-based system approach for sensor fault modeling, detection and mitigation. Expert Sys. Appl. 39(12), 10977–10989 (2012)CrossRefGoogle Scholar
  25. 25.
    Zhang, Y., Ma, C.: Decentralized fault diagnosis using multiblock kernel independent component analysis. Chem. Eng. Res. Des. 90(5), 667–676 (2012)CrossRefGoogle Scholar
  26. 26.
    Amoozgar, M.H., Chamseddine, A., Zhang, Y.: Experimental test of a two-stage kalman filter for actuator fault detection and diagnosis of an unmanned quadrotor helicopter. J. Intell. Robot Syst. Theor. Appl. 70(1–4), 107–117 (2013)Google Scholar
  27. 27.
    Eski, I., Erkaya, S., Savas, S., Yildirim, S.: Fault detection on robot manipulators using artificial neural networks. Robot Comput. Integr. Manuf. 27(1), 115–123 (2011)CrossRefGoogle Scholar
  28. 28.
    Milena, P., Rapaic, M.R., Jelicic, Z.D., Pisano, A.: On-line adaptive clustering for process monitoring and fault detection. Expert Sys. Appl. 39(11), 10226–10235 (2012)CrossRefGoogle Scholar
  29. 29.
    Abdul, S., Liu, G.: Decentralised fault tolerance and fault detection of modular and reconfigurable robots with joint torque sensing. In: 2008 IEEE International Conference on Robotics and Automation, pp. 3520–3526, Pasadena, USA (2008)Google Scholar
  30. 30.
    Zhu, M.C., Li, Y.C., Jiang, R.H.,: Decentralized fault tolerant control for reconfigurable modular robots. Control Decis. 24(8):1247–1251, 1256 (2009)Google Scholar
  31. 31.
    Hu, Q., Xiao, B.: Fault-tolerant sliding mode attitude control for flexible spacecraft under loss of actuator effectiveness. Nonlinear Dyn. 64, 13–23 (2011)CrossRefMATHMathSciNetGoogle Scholar
  32. 32.
    Jiang, J., Yu, X.: Fault-tolerant control systems: a comparative study between active and passive approaches. Annu. Rev. Control 36(1), 60–72 (2012)CrossRefGoogle Scholar
  33. 33.
    Liu, G., Wang, D., Li, Y.: Active fault tolerant control with actuation reconfiguration. IEEE Trans. Aerosp. Electron. Syst. 40(3), 1110–1117 (2004)CrossRefMathSciNetGoogle Scholar
  34. 34.
    Marcello, B., Paolo, C., Nicola, M., Silvio, S.: Active fault tolerant control of nonlinear systems: the cart-pole example. Int. J. Appl. Math. Comput. Sci. 21(3), 441–455 (2011)MATHMathSciNetGoogle Scholar
  35. 35.
    Henrik, N.H.: A model-based approach to fault-tolerant control. Int. J. Appl. Math. Comput. Sci. 22(1), 67–86 (2012)MATHMathSciNetGoogle Scholar
  36. 36.
    Ma, H.J., Yang, G.H.: Detection and adaptive accommodation for actuator faults of a class of non-linear systems. IET Control Theory Appl. 6(14), 2292–2307 (2012) Google Scholar
  37. 37.
    Zhao, B., Li, Y., Liu, K.: Effectiveness factor integrated decentralized fault tolerant control scheme for reconfigurable manipulators. J. Tsinghua Univ. Sci. Technol. 52(9):1218–1222, 1229 (2012)Google Scholar
  38. 38.
    Li, Y., Lu, P., Zhao, B.: Backstepping time delay decentralized fault-tolerant control for reconfigurable manipulators. Control Decis. 27(3), 446–450 (2012)MATHMathSciNetGoogle Scholar
  39. 39.
    Luca, A.D., Mattone, R.: An identification Scheme for robot actuator faults. In: 2005 IEEE/RSJ International Conference on intelligent robots and systems 1–4: 2613–2617 (2005)Google Scholar
  40. 40.
    Bessa, W., Dutra, M., Kreuzer, E.: Sliding mode control with adaptive fuzzy dead-zone compensation of an electro-hydraulic servo-system. J. Intell. Robot. Syst. 58(1), 3–16 (2010)CrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Control EngineeringChangchun University of TechnologyChangchunChina
  2. 2.Department of Control Science and TechnologyJilin UniversityChangchunChina

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