Skip to main content
SpringerLink
Log in

Intelligent nonlinear observer design for a class of nonlinear discrete-time flexible joint robot

  • Original Research Paper
  • Published:
Intelligent Service Robotics Aims and scope Submit manuscript

Abstract

In this paper, a nonlinear intelligent observer design is applied for a class of nonlinear discrete-time flexible joint robot (DFJR) dynamic system based on artificial neural network (ANN). The DFJR system has a relatively complex nonlinear dynamic and internal states’ estimation of it poses a challenging robotic problem. Multilayer perceptron (MLP) is an important class of feed-forward ANNs that maps set of inputs onto a set of suitable outputs. The ANN under online learning is one of the artificial intelligence methods. Therefore, the MLP neural nonlinear observer is trained online and it is robust in the presence of external and internal uncertainties. The learning method of the intelligent observer is a simple back propagation (BP) algorithm and, furthermore, the learning method of estimation of the link positions and the velocities is BP-developed algorithm. Simulation results show promising performance of the proposed observer in the presence of measurement noise and parameters uncertainties.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Tudor L, Moise A (2013) Automatic expert system for fuzzy control of robot trajectory in joint space. In: IEEE Int. Conf. on Mechatronics and Automation, pp 1057–1062

  2. Park YJ, Chung WK (2013) Unified external torque-sensing algorithm for flexible-joint robot based on Kalman filter. In: 10th Int Conference on Ubiquitous Robots and Ambient Intelligence, pp 78–79

  3. Park YJ, Chung WK (2013) External torque-sensing algorithm for flexible-joint robot based on Kalman filter. Electron Lett 49(14):877–878

    Article  Google Scholar 

  4. Li Xiao-Jian, Yang Guang-Hong (2014) Robust fault detection and isolation for a class of uncertain single output non-linear systems. Control Theory Appl 8(7):462–470

    Article  MathSciNet  Google Scholar 

  5. MDeshan, XuWenfu, Chao Xu, Zonggao Mu (2013) Modeling and simulation study of flexible space robot for capturing large flexible spacecraft. In: 32nd Chinese Control Conference (CCC), pp 5837–5842

  6. Spong MW, Khorasani K, Kokotovic PV (1987) An integral manifold approach to the feedback control of flexible joint robots. IEEE J Rob Autom Soc 3(4):291–300

  7. Lin L-C, Yuan K (1990) Control of flexible joint robots via external linearization approach. J Rob Syst 7(1):1–22

  8. Machida K, Nishida H, Akita K (1998) Precise telerobotic system for space experiment on ETSVII

  9. Chen W, Yu Y, Zhao X, Zhao L, Sun Q (2011) Position control of a 2DOF underactuated planar flexible manipulator. In: Int. Conf. on Mechatronics and Automation, pp 464–469

  10. Shang J (2012) Design of a multitasking robotic platform with flexible arms and articulated head for Minimally invasive surgery. In: IEEE Int. Conf. on Intelligent Robots and Systems, pp 1988–1993

  11. Xue G, Ren X (2013) Discrete-time sliding mode control coupled with asynchronous sensor fusion for rigid-link flexiblejoint manipulators. In: IEEE Int. Conf. Control and Automation, pp 238–243

  12. Shibata K, Goto K (2013) Emergence of flexible prediction-based discrete decision making and continuous motion generation through actor-Q-learning. In: IEEE Int. Conf. on Development and Learning and Epigenetic Robotics, pp 1–6

  13. Nakamura T, Saga N, Kawamura T (2003) Development of a soft manipulator using a smart flexible joint for safe contact with humans. In: IEEE Int. Conf. Adv. Intell. Mechatron, vol 1. pp 441–446

  14. Kaminaga H, Ono J, Nakashima Y (2009) Development of backdrivable hydraulic joint mechanism for knee joint of humanoid robots. In: IEEE Int. Conf. on Robotics and Automation, pp 1577–1582

  15. Albu-Schaffer A, Hirzinger G (2000) State feedback controller for flexible joint robots: a globally stable approach implemented on DLRs light-weight robots. IEEE Int Conf 2:1087–1093

    Google Scholar 

  16. Spong MW (1989) On the force control problem for flexible joint manipulators. IEEE Trans Autom Control 34(1):107–111

    Article  MATH  MathSciNet  Google Scholar 

  17. Ge SS, Woon LC (1998) Adaptive neural network control of flexible joint manipulators in constrained motion. Trans Inst Meas Control 20(1):37–46

    Article  Google Scholar 

  18. Spong MW (1987) Modeling and control of elastic joint robots. J Dyn Syst Meas Control 109:310–319

    Article  MATH  Google Scholar 

  19. Chang YZ, Daniel RW (1992) On the adaptive control of flexible joint robots. Automatica 28(5):969–974

    Article  MATH  MathSciNet  Google Scholar 

  20. Spong M, Khorasani K, Kokotovic PV (1987) An integral manifold approach to feedback control of flexible joint robots. IEEE J Robot Autom 3(4):291–301

    Article  Google Scholar 

  21. Khorasani K (1992) Adaptive control of flexible-joint robots. IEEE Trans Robot Autom 8(2):250–267

    Article  Google Scholar 

  22. Ghanes M, De Leon J, Barbot J (2013) Observer design for nonlinear systems under unknown time-varying delays. IEEE Trans Autom Control 58(6):1529–1534

    Article  Google Scholar 

  23. Wu J (2014) Nonlinear disturbance observer-based dynamic surface control for trajectory tracking of pneumatic muscle system. IEEE Trans Control Syst Tech 22(2):440–455

    Article  Google Scholar 

  24. Tobias R, Allgower F (2007) Observers with impulsive dynamical behavior for linear and nonlinear continuous-time systems. In: 46th IEEE Conf. on Decision and Control, pp 4287–4292

  25. Farza M, MSaad M, Fall ML, Pigeon E, Gehan O (2014) Continuous-discrete time observers for a class of MIMO nonlinear systems. IEEE Trans Autom Control 59(4):1060–1065

  26. Prasov AA, Khalil HK (2013) A nonlinear high-gain observer for systems with measurement noise in a feedback control framework. IEEE Trans Autom Control 58(3):569–580

    Article  MathSciNet  Google Scholar 

  27. Alanis Alma Y, Sanchez Edgar N, Loukianov Alexander G, Perez Marco A (2011) Real-time recurrent neural state estimation. IEEE Trans Neural Netw 22(3):497–505

  28. Ghasemi Reza (2013) Designing observer based variable structure controller for large scale nonlinear systems. IAES Int J Artif Intel 2(3):125–135

    Google Scholar 

  29. Jankovic M (1995) Observer based control for elastic joint robots. IEEE Trans Robot Autom 11(4):618–623

    Article  Google Scholar 

  30. Nicosia S, Tomei P, Tornambe A (1988) A nonlinear observer for elastic robots. IEEE Trans Robot Autom 4(1):45–52

    Article  Google Scholar 

  31. Nicosia S, Tomei P, Tornambe A (1989) An approximate observer for a class of nonlinear systems. Syst Control Lett 12(1):43–51

    Article  MathSciNet  Google Scholar 

  32. Tomei P (1990) An observer for flexible joint robots. IEEE Trans Autom Control 35(6):739–743

    Article  MATH  MathSciNet  Google Scholar 

  33. Yun JN, Su Jian-Bo (2014) Design of a disturbance observer for a two- link manipulator with flexible joints. IEEE Trans Control Syst Technol 22(2):809–815

    Article  Google Scholar 

  34. Kim YH, Lewis FL (1998) High-level feedback control with neural networks. World Scientific, Singapore

    MATH  Google Scholar 

  35. Jar V, Khm G, Em H (2013) Adaptive observer design based on scaling and neural networks, Latin America transactions. IEEE (Revista IEEE America Latina) 11(4):989–994

    Google Scholar 

  36. Sanchez EN, Ricalde LJ (2003) Trajectory tracking via adaptiverecurrent control with input saturation. In: International Joint Conference on Neural Networks

  37. Talebi HA, Abdollahi F, Patel RV, Khorasani K (2010) Neural Network-based state estimation of nonlinear systems. In: Application to fault detection and isolation, vol 395, Springer

  38. Resendiz J, Yu W, Fridman L (2008) Two-stage neural observer for mechanical systems. IEEE Trans Circuits Syst II Express Brief 55(10):1076–1080

    Article  Google Scholar 

  39. Kazantzis N, Kravaris C (1999) Time-discretization of nonlinear control systems via Taylor methods. Comput Chem Eng 23:763–784

    Article  Google Scholar 

  40. Luenberger DG (1964) Observing the state of a linear systems. IEEE Trans Mil Electron 8(2):7480

    Article  Google Scholar 

  41. Slotine J-JE, Li W (1991) Applied nonlinear control. Prentice Hall, New Jersey, p 352

Download references

Author information

Authors and Affiliations

  1. Department of Control Engineering, Damavand Branch, Islamic Azad University, Damavand, Iran

    Mohammad Reza Rahimi Khoygani & Ahmad Reza Vali

  2. Department of Electrical Engineering, University of Qom, Qom, Iran

    Reza Ghasemi

Authors
  1. Mohammad Reza Rahimi Khoygani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reza Ghasemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmad Reza Vali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Reza Rahimi Khoygani.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khoygani, M.R.R., Ghasemi, R. & Vali, A.R. Intelligent nonlinear observer design for a class of nonlinear discrete-time flexible joint robot. Intel Serv Robotics 8, 45–56 (2015). https://doi.org/10.1007/s11370-014-0162-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11370-014-0162-x

Keywords

Search

Navigation