Abstract
In this paper, a novel neural network based dynamic surface integral nonsingular fast terminal sliding mode control (DS-INFTSMC) is developed. The robotic manipulators driven by permanent magnet synchronous motors are considered as a whole system to be controlled. The proposed DS-INFTSMC can provide fast dynamic performance and high tracking accuracy. To compensate lumped disturbance including unknown uncertainties and external disturbance, an extended state observer is developed. The adaptive radial basis function neural network estimates the modeling errors. DS-INFTSMC combined with the dynamic surface is proposed to obtain better robust performance and dynamic performance. Finally, Lyapunov theory is used to prove the practical stable of the whole system and finite time convergence, and simulation results verify effectiveness of the proposed method.
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