Adaptive finite-time tracking control of robot manipulators with multiple uncertainties based on a low-cost neural approximator

Abstract

Benefiting from a newly designed switching function in terminal sliding manifold and novel uncertainty handling solutions, this article presents a low-cost neuroadaptive control scheme that can not only achieve the finite time tracking control of robot system with multiple uncertainties also circumvent the possible singularity. Specifically, for the kinematics parameter uncertainties involved, the proposed terminal sliding mode observer can ensure the actual position of end-effector be accurately estimated within a finite time. And then, a neural approximator is designed to handle the non-parameterizable lumped dynamics uncertainty, and a new low-cost neural adaptive mechanism is constructed to reduce the computational burden. Furthermore, it is proved that all closed-loop signals are bounded and the tracking error converges to an arbitrarily small adjustable neighborhood of the origin within a finite time. The comparison simulation example also confirms the effectiveness and superiority of the proposed control scheme.