Adaptive fast sliding neural control for robot manipulator

Robotic manipulators are open to external disturbances and actuation failures during performing a task such as trajectory tracking. In this paper, we present a modifed controller consisting of a global fast sliding surface combined with an adaptive neural network which is called adaptive fast sliding neural control (AFSNC) for a robotic manipulator to precise stable trajectory tracking performance under the external disturbances. The adaptive term is employed to reduce uncertainties due to unmodeled dynamics. Tracking error asymptotically converges to zero according to the Lyapunov stability theorem. Numerical examples have been carried on a planar two-links manipulator to verify the control approach efficiency. The experimental results show that the proposed control approach performs satisfactory trajectory tracking and tracks the desired trajectory in less time with reduced chattering effect compared to the other methods.