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Neuro-fuzzy control of a robotic manipulator

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Języki publikacji
EN
Abstrakty
EN
In this paper, to solve the problem of control of a robotic manipulator’s movement with holonomical constraints, an intelligent control system was used. This system is understood as a hybrid controller, being a combination of fuzzy logic and an artificial neural network. The purpose of the neuro-fuzzy system is the approximation of the nonlinearity of the robotic manipulator’s dynamic to generate a compensatory control. The control system is designed in such a way as to permit modification of its properties under different operating conditions of the two-link manipulator.
Rocznik
Strony
575--584
Opis fizyczny
Bibliogr. 10 poz., rys., wykr.
Twórcy
autor
  • Department of Applied Mechanics and Robotics Faculty of Mechanical Engineering and Aeronautics Rzeszow University of Technology al. Powstańców Warszawy 12, 35-959 Rzeszów, POLAND
  • Department of Applied Mechanics and Robotics Faculty of Mechanical Engineering and Aeronautics Rzeszow University of Technology al. Powstańców Warszawy 12, 35-959 Rzeszów, POLAND
autor
  • Department of Applied Mechanics and Robotics Faculty of Mechanical Engineering and Aeronautics Rzeszow University of Technology al. Powstańców Warszawy 12, 35-959 Rzeszów, POLAND
Bibliografia
  • [1] Chen S. and Billings A. (1996): Neural networks for nonlinear dynamic system modeling and identification. - Int. J. Control, vol.56, No.2, pp.319-346.
  • [2] Giergiel J., Hendzel Z. and Zylski W. (2002): Modelling and Control of Wheeled Mobile Robots. - Warsaw: PWN.
  • [3] Gierlak P. (2012): Hybrid Position/Force Control of the SCORBOT-ER 4pc Manipulator with Neural Compensation of Nonlinearities. - Springer-Verlag Berlin Heidelberg, In: L. Rutkowski et al. (Eds.): ICAISC 2012, Part II, LNCS 7268, pp.433-441.
  • [4] Gierlak P. (2013): Application of adaptive hybrid position - force control of manipulator in robotised machining. - Modelowanie Inżynierskie, vol.46, No.15, pp.28-34.
  • [5] Gierlak P. (2014): Hybrid position/force control in robotised machining. - Solid State Phenomena, Trans. Tech. Publications, vol.210, pp.192-199.
  • [6] Hendzel Z. and Muszyńska M. (2012): Adaptive fuzzy control of a wheeled mobile robot. - Int. J. of Applied Mechanics and Engineering, vol.17, No.3, pp.827-835.
  • [7] Hornik K., Stinchcombe M. and White H. (1989): Multilayer feedforward networks are universal approximators. - Neural Networks, vol.2, pp.359-366.
  • [8] Kumar N., Panwar V., Sukavanam N., Sharma S.P. and Borm J.-H. (2011): Neural network based hybrid force/position control for robot manipulators. - Int. J. Precis. Eng. Manuf., vol.12, No.3, pp.419-426.
  • [9] Lewis F.L., Jagannathan S. and Yesildirek A. (1999): Control of Robot Manipulators and Nonlinear Systems. - London: Taylor & Francis.
  • [10] Muszyńska M. (2012): Neuro-fuzzy control of a wheeled mobile robot. - Doctoral Dissertation.
Typ dokumentu
Bibliografia
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