Neural Networks-based Adaptive State Feedback Control of Robot Manipulators


  • Ghania Debbache Electrical Engineering Institute, Oum El-Bouaghi University, 04000 Oum El-Bouaghi, Algeria
  • Abdelhak Bennia Electronic Departement, Constantine University 25000 Constantine, Algeria
  • Noureddine Goléa Electrical Engineering Institute, Oum El-Bouaghi University, 04000 Oum El-Bouaghi, Algeria


Robot manipulator, neural networks, adaptive control, stability


This paper proposes an adaptive control suitable for motion control of robot manipulators with structured and unstructured uncertainties. In order to design an adaptive robust controller, with the ability to compensate these uncertainties, we use neural networks (NN) that have the capability to approximate any nonlinear function over a compact space. In the proposed control scheme, we need not derive the linear formulation of robot dynamic equation and tune the parameters. To reduce the NNs complexity, we consider the properties of robot dynamics and the decomposition of the uncertainties terms. The proposed controller is robust against uncertainties and external disturbance. The validity of the control scheme is demonstrated by computer simulations on a two-link robot manipulator.


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