In this paper, we aim to improve the tracking performance of the
manipulator joint system under the presence of the uncertainties, such
as modelling error, friction, and external disturbance. Firstly, the
nonsingular fast terminal sliding mode control is developed to
guarantees a finite-time convergence and to solve the singular issue of
the terminal sliding mode control. Secondly, in view of the established
system model, an adaptive sliding mode controller (SMC) based on radial
basis function neural network (RBFNN) and sliding mode variable
structure control theory is designed for the tracking of the bi-joint
manipulator and six-degree of freedom parallel robot. Finally, the
results show that our method improves the robustness of the adaptive
RBFNN controller further, weakens the chattering phenomenon, reduces
error, and has an excellent control performance.