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Sliding Mode Fault Diagnosis with Vision in the Loop for Robot Manipulators

  • Antonella Ferrara
  • Gian Paolo IncremonaEmail author
  • Bianca Sangiovanni
Chapter
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Part of the Studies in Systems, Decision and Control book series (SSDC, volume 270)

Abstract

This chapter is devoted to the problem of Fault Diagnosis (FD) for industrial robotic manipulators within the framework of sliding mode control theory. According to this control concept, a set of unknown input higher order sliding mode observers are designed to detect, isolate and identify multiple actuators faults and corruptions. More specifically, the whole FD architecture is based on the inverse dynamics-based feedback linearized robotic MIMO system, which is equivalent to a set of linearized decoupled SISO systems, affected by uncertain terms. The FD process includes a residual generation, followed by a decision making through the evaluation of the achieved residuals. The advantages of the sliding mode approach are the good performance in terms of stability and robustness, as well as satisfactory estimate of the occurring faults. Furthermore, in order to extend the FD strategy to multiple sensor and actuator faults, a low cost vision servoing architecture is used in the scheme, allowing one to design a fault tolerant control strategy in case of sensor faults. The effectiveness of the proposed FD architecture has been carried out in simulation on a realistic simulator as well as experimentally on a COMAU SMART3-S2 anthropomorphic robot manipulator.

Keywords

Fault diagnosis Robot manipulators Sliding mode observers Uncertain systems 

References

  1. 1.
    Alwi, H., Edwards, C., Tan, C.P.: Fault Detection and Fault-Tolerant Control Using Sliding Modes. Advances in Industrial Control. Springer, Berlin (2011)Google Scholar
  2. 2.
    Bartolini, G., Caputo, W., Cecchi, M., Ferrara, A., Fridman, L.: Vibration damping in elastic robotic structures via sliding modes. J. Robot. Syst. 14(9), 675–696 (1998a)CrossRefGoogle Scholar
  3. 3.
    Bartolini, G., Ferrara, A., Levant, A., Usai, E.: On Second Order Sliding Mode Controllers. Lecture Notes in Control and Information, pp. 329–350. Springer, London (1999)zbMATHGoogle Scholar
  4. 4.
    Bartolini, G., Ferrara, A., Usai, E.: Adaptive reduction of the control effort in chattering-free sliding-mode control of uncertain nonlinear systems. Appl. Math. Comput. Sci. 8(1), 51–71 (1998b)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Bartolini, G., Ferrara, A., Usai, E.: Chattering avoidance by second-order sliding mode control. IEEE Trans. Autom. Control 43(2), 241–246 (1998c)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Brambilla, D., Capisani, L., Ferrara, A., Pisu, P.: Fault detection for robot manipulators via second-order sliding modes. IEEE Trans. Ind. Electron. 55(11), 3954–3963 (2008)CrossRefGoogle Scholar
  7. 7.
    Capisani, L.M., Ferrara, A., Ferreira de Loza, A., Fridman, L.M.: Manipulator fault diagnosis via higher order sliding-mode observers. IEEE Trans. Ind. Electron. 59(10), 3979–3986 (2012)CrossRefGoogle Scholar
  8. 8.
    Capisani, L.M., Ferrara, A., Magnani, L.: Design and experimental validation of a second-order sliding-mode motion controller for robot manipulators. Int. J. Control 82(2), 365–377 (2009)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Capisani, L.M., Ferrara, A., Pisu, P.: Sliding mode observers for vision-based fault detection, isolation and identification in robot manipulators. In: Proceedings American Control Conference, pp. 4540–4545. Baltimore, Maryland, USA (2010)Google Scholar
  10. 10.
    Chiang, L., Braatz, R., Russell, E.: Fault Detection and Diagnosis in Industrial Systems. Advanced Textbooks in Control and Signal Processing. Springer, London (2001)CrossRefGoogle Scholar
  11. 11.
    Davila, J., Fridman, L., Poznyak, A.: Observation and identification of mechanical systems via second order sliding modes. Int. J. Control 79(10), 1251–1262 (2006)MathSciNetCrossRefGoogle Scholar
  12. 12.
    De Luca, A., Mattone, R.: An adapt-and-detect actuator FDI scheme for robot manipulators. In: Proceedings International Conference on Robotics and Automation, vol. 5, pp. 4975–4980. Barcelona, Spain (2004)Google Scholar
  13. 13.
    De Luca, A., Mattone, R.: An identification scheme for robot actuator faults. In: Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1127–1131. Alberta, Canada (2005)Google Scholar
  14. 14.
    Esna Ashari, A., Nikoukhah, R., Campbell, S.: Active robust fault detection in closed-loop systems: Quadratic optimization approach. IEEE Trans. Autom. Control 57(10), 2532–2544 (2012)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Ferrara, A., Incremona, G.P.: Design of an integral suboptimal second-order sliding mode controller for the robust motion control of robot manipulators. IEEE Trans. Control Syst. Technol. 23(6), 2316–2325 (2015)CrossRefGoogle Scholar
  16. 16.
    Halder, B.: Robust Nonlinear Fault Detection and Isolation of Robotic System: A Novel Nonlinear Analytic Redundancy Method. VDM Verlag, Germany (2009)Google Scholar
  17. 17.
    Incremona, G.P., Ferrara, A., Magni, L.: MPC for robot manipulators with integral sliding modes generation. IEEE/ASME Trans. Mechatron. 22(3), 1299–1307 (2017)CrossRefGoogle Scholar
  18. 18.
    Incremona, G.P., Saccon, A., Ferrara, A., Nijmeijer, H.: Trajectory tracking of mechanical systems with unilateral constraints: Experimental results of a recently introduced hybrid pd feedback controller. In: Proceedings 54th IEEE Conference on Decision and Control, pp. 920–925. Osaka, Japan (2015)Google Scholar
  19. 19.
    Isermann, R.: Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance. Springer, Berlin (2006)Google Scholar
  20. 20.
    Natke, H., Cempel, C.: Model-Aided Diagnosis of Mechanical Systems. Fundamentals, Detection, Localization, Assessment. Springer, London (2011)zbMATHGoogle Scholar
  21. 21.
    Papadimitropoulos, A., Rovithakis, G.A., Parisini, T.: Fault detection in mechanical systems with friction phenomena: an online neural approximation approach. IEEE Trans. Neural Netw. Learn. Syst. 18(4), 1067–1082 (2007)Google Scholar
  22. 22.
    Rigatos, G.G.: Fault Diagnosis in Robotic and Industrial Systems, 1st edn. iConcept Press Ltd., Hong Kong (2012)Google Scholar
  23. 23.
    Rohmer, E., Singh, S.P.N., Freese, M.: V-REP: a versatile and scalable robot simulation framework. In: Proceedings of the International Conference on Intelligent Robots and Systems (IROS) (2013).Google Scholar
  24. 24.
    Sangiovanni B., Rendiniello A., Incremona G. P., Ferrara A., Piastra M.: Deep reinforcement learning for collision avoidance of robotic manipulators. In: Proceedings of European Control Conference, pp. 2063–2068 (2018)Google Scholar
  25. 25.
    Ferrara, A., Incremona, G.P., Sangiovanni, B.: Integral sliding mode based switched structure control scheme for robot manipulators. In: 15th International Workshop on Variable Structure Systems, pp. 168–173 (2018)Google Scholar
  26. 26.
    Sangiovanni B., Incremona G.P., Ferrara A., Piastra M.: Deep reinforcement learning based self-configuring integral sliding mode control scheme for robot manipulators. In: IEEE Conference on Decision and Control, pp. 5969–5974 (2018)Google Scholar
  27. 27.
    Scott, J.K., Findeisen, R., Braatz, R.D., Raimondo, D.M.: Input design for guaranteed fault diagnosis using zonotopes. Automatica 50(6), 1580–1589 (2014)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Siciliano, B., Khatib, O. (eds.): The Handbook of Robotics. Springer, Berlin (2008)zbMATHGoogle Scholar
  29. 29.
    Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G.: Robotics-Modelling Planning and Control, 3rd edn. Springer, London, (2009)Google Scholar
  30. 30.
    Simandl, M., Puncochar, I.: Active fault detection and control: unified formulation and optimal design. Automatica 45(9), 2052–2059 (2009)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Spurgeon, S.K.: Sliding mode observers: a survey. Int. J. Syst. Sci. 39(8), 751–764 (2008)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Utkin, V.I.: Sliding Modes in Optimization and Control Problems. Springer, New York (1992)CrossRefGoogle Scholar
  33. 33.
    Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S.N.: A review of process fault detection and diagnosis: part i: quantitative model-based methods. Comput. Chem. Eng. 27(3), 293–311 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Antonella Ferrara
    • 1
  • Gian Paolo Incremona
    • 2
    Email author
  • Bianca Sangiovanni
    • 1
  1. 1.Dipartimento di Ingegneria Industriale e dell’InformazioneUniversity of PaviaPaviaItaly
  2. 2.Dipartimento di ElettronicaInformazione e Bioingegneria, Politecnico di MilanoMilanItaly

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