RBFNN Mode Sliding Control for Robotic Manipulator

Abstract

A new control scheme is proposed for robotic manipulators in this paper, it consisted of two combined controllers aim at nominal model and practical plant of the robot system separately.The status feedback controller was designed for nominal model, the compensatory sliding mode controller is designed for practical plant.Radial Basic Function Neural Network(RBFNN) was used to adaptively learn the unknown bounds of system uncertainties.and the robust control focus compensate effectively to eliminate the network approximation error.The stability of the control system was ensured by Lyapunov method.The tracking error converges to zero.In order to make the control smoothed and bounded, a saturated function instead of the symbols function.The simulation studies verify the effectiveness of the proposed algorithm.