Neural PD control with second-order sliding mode compensation for robot manipulators

Abstract

Both neural network and sliding mode technique can compensate the steady-state error of proportional-derivative (PD) control. The tracking error of PD control with sliding mode is asymptotically stable, but the chattering is big. PD control with neural networks is smooth, but it is not asymptotically stable. PD control combining both neural networks and sliding mode cannot reduce chattering, because the sliding mode control (SMC) is always applied. In this paper, neural control and SMC are connected serially: first a dead-zone neural PD control assures that the tracking error is bounded, then super-twisting second-order sliding-mode is used to guarantee finite time convergence of the sliding mode PD control.