Intelligent controller for hybrid force and position control of robot manipulators using RBF neural network

  • Komal Rani
  • Naveen KumarEmail author


In this paper, an intelligent controller is developed for hybrid force and position control of robot manipulators in the presence of external disturbances and the model uncertainties. The proposed controller consists of a model based controller and neural network based model free controller with an adaptive bound part. A non linear function of model dynamics is identified by employing a radial basis function neural network. The role of adaptive bound part is to estimate the bounds on model disturbances, friction term and neural network reconstruction error. The Lyapunov function candidate is used to prove the stability of the proposed controller and to show that the errors are asymptotically convergent. Finally numerical simulation results are presented for two link robot manipulator to show excellent performance of the proposed controller in comparison to other control schemes such as model based computed torque control and neural network based model free controller.


Hybrid force and position control Constrained robot manipulator Disturbances Neural network Asymptotically stable 


  1. 1.
    Raibert MH, Craig JJ (1981) Hybrid position/force control of manipulators. ASME J Dyn Syst Meas Control 103:126–133CrossRefGoogle Scholar
  2. 2.
    Lozano R, Brogliato B (1992) Adaptive hybrid position/force control of redundant manipulators. IEEE Trans Autom Control 37(10):1501–1505CrossRefzbMATHGoogle Scholar
  3. 3.
    Yoshikawa T, Sudou A (1993) Dynamic hybrid position/force control of 5 robot manipulators: online-estimation of unknown constraint. IEEE Trans Robot Autom 9(2):220–226CrossRefGoogle Scholar
  4. 4.
    Kwan CM (1996) Robust adaptive force/motion control of constrained robots. IEE Proc Control Theory Appl 143(1):103–109CrossRefzbMATHGoogle Scholar
  5. 5.
    Kouya DN, Saad M, Lamarche L (2002) Backstepping adaptive hybrid force/position control for robotic manipulators. In: Proceedings of the American control conference, pp 4595–4600Google Scholar
  6. 6.
    De Queiroz MS, Jun H, Dawson DM (1997) Adaptive position/force control of robot manipulators without velocity measurements: theory and experimentation. IEEE Trans Syst Man Cybern Part B Cybern 27(5):796–809CrossRefGoogle Scholar
  7. 7.
    Cheah CC, Zhao Y, Slotine JJE (2006) Adaptive Jacobian motion and force control for constrained robots with uncertainties. In: Proceedings of the international conference on robotics and automation, pp 2226–2231Google Scholar
  8. 8.
    Filaretov VF, Zuev AV (2008) Adaptive force/position control of robot manipulators. In: Proceedings of the 2008 IEEE/ASME international conference on advanced intelligent mechatronics, pp 96–101Google Scholar
  9. 9.
    Kouya DN (2008) Adaptive hybrid force-position control of a robotic manipulator. In: 2nd European computing confrence, pp 323–331Google Scholar
  10. 10.
    Roy J, Whitcomb LL (2002) Adaptive force control of position/velocity controlled robots: theory and experiment. IEEE Trans Robot Autom 18(2):121–137CrossRefGoogle Scholar
  11. 11.
    Pliego-Jimenez J, Arteaga-Perez MA (2015) Adaptive position/force control for robot manipulators in contact with a rigid surface with uncertain parameters. Eur J Control 22:1–12MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Lewis FL, Jagannathan S, Yesildirek A (1999) Neural network control of robot manipulators and nonlinear systems. Taylor and Francis, BrightonGoogle Scholar
  13. 13.
    Lewis FL, Yesildirek A, Liu K (1996) Multilayer neural net robot controller with quaranted tracking performance. IEEE Trans Neural Netw 7(2):388–399CrossRefGoogle Scholar
  14. 14.
    Fanaei A, Farrokhi M (2006) Robust adaptive neuro-fuzzy controller for hybrid position/force control of robot manipulators in contact with unknown environment. J Intell Fuzzy Syst 17(2):125–144zbMATHGoogle Scholar
  15. 15.
    Liu H, Wang L, Wang F (2007) Fuzzy force control of constrained robot manipulators based on impedance model in an unknown environment. Front Mech Eng China 2(2):168–174CrossRefGoogle Scholar
  16. 16.
    Karayiannidis Y, Rovithakis K, Doulgeri Z (2007) Force/position tracking for a robotic manipulator in compliant contact with a surface using neuro-adaptive control. Automatica 43(7):1281–1288MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Bechlioulis CP, Doulgeri Z, Rovithakis GA (2010) Neuroadaptive force/position control with prescribed performance and guaranteed contact maintenance. IEEE Trans Neural Network 21(12):1857–1868CrossRefGoogle Scholar
  18. 18.
    Kumar N, Panwar V, Sukavanam N, Sharma SP, Borm JH (2011) Neural network based hybrid force/position control for robot manipulators. Int J Precis Eng Manuf 12(3):419–426CrossRefGoogle Scholar
  19. 19.
    Singh HP, Sukavanam N (2013) Stability analysis of robust adaptive hybrid position/force controller for robot manipulators using neural network with uncertainties. Neural Comput Appl 22(7–8):1745–1755CrossRefGoogle Scholar
  20. 20.
    Mahjoub S, Mnif F, Derbel N, Hamerlain M (2014) Radial-basis-functions neural network sliding mode control for underactuated mechanical systems. Int J Dyn Control 2(4):533–541CrossRefGoogle Scholar
  21. 21.
    Li Y, Wang G, Dong B, Zhao B (2015) Hybrid positionforce control for constrained reconfigurable manipulators based on adaptive neural network. Adv Mech Eng 7(9):1–10Google Scholar
  22. 22.
    Lee CH, Wang WC (2016) Robust adaptive position and force controller design of robot manipulator using fuzzy neural networks. Nonlinear Dyn 85(1):343–354MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Ghajar MH, Keshmiri M, Bahrami J (2018) Neural-network-based robust hybrid force/position controller for a constrained robot manipulator with uncertainties. Trans Inst Meas Control 40(5):1625–1636CrossRefGoogle Scholar
  24. 24.
    De Oliveira OC, De Medeiros Martins A, De Araujo AD (2017) A dual-mode control with a RBF network. J Control Autom Electr Syst 28(2):180–188CrossRefGoogle Scholar
  25. 25.
    Rani K, Kumar N (2018) Design of intelligent hybrid force and position control of robot manipulators. In: Proceedings of the 6th international conference on smart computing and communications, pp. 42–49Google Scholar
  26. 26.
    Slotine JJE, Li W (1991) Applied nonlinear control. Prentice-Hall, Englewood CliffszbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of MathematicsNational Institute of Technology KurukshetraKurukshetraIndia
  2. 2.Department of MathematicsBabu Anant Ram Janta College KaulKaithalIndia

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