Abstract
A neural learning-based finite-time control policy is presented for a robotic manipulator with unknown backlash-like hysteresis and system uncertainties. Adaptive neural networks are adopted to deal with unknown robotic dynamics. In order to eliminate the effect of unknown backlash-like hysteresis, a robust adaptive term is designed in the backstepping design process. A neural network-based finite-time controller is designed by introducing a fractional order term, which guarantees the finite-time convergence of both neural networks and adaptive terms, and this type of convergence improves control accuracy to a certain extent. With the Lyapunov stability theory, the proposed scheme can be proved to make the errors be semiglobally finite-time stable (SGFTS). The effectiveness of the proposed control is shown by simulation and experimental results.
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