  To achieve high-accuracy manipulation in the presence of unknown
disturbances, we propose two novel efficient and robust motion control schemes
for high-dimensional robot manipulators. Both controllers incorporate an
unknown system dynamics estimator (USDE) to estimate disturbances without
requiring acceleration signals and the inverse of inertia matrix. Then, based
on the USDE framework, an adaptive-gain controller and a super-twisting sliding
mode controller are designed to speed up the convergence of tracking errors and
strengthen anti-perturbation ability. The former aims to enhance feedback
portions through error-driven control gains, while the latter exploits
finite-time convergence of discontinuous switching terms. We analyze the
boundedness of control signals and the stability of the closed-loop system in
theory, and conduct real hardware experiments on a robot manipulator with seven
degrees of freedom (DoF). Experimental results verify the effectiveness and
improved performance of the proposed controllers, and also show the feasibility
of implementation on high-dimensional robots.
