Recently, robotic manipulators have been playing an increasingly critical part in scientific
research and industrial applications. However, modeling of robotic manipulators is …

In this article, a robot skills learning framework is developed, which considers both motion
modeling and execution. In order to enable the robot to learn skills from demonstrations, a …

The controller design for the robotic manipulator faces different challenges such as the
system's nonlinearities and the uncertainties of the parameters. Furthermore, the tracking of …

This paper proposes a finite-time trajectory tracking controller for a space manipulator under
model uncertainty, external disturbance, and actuator saturation. The dynamics of space …

In this paper, a neural network learning adaptive robust controller (NNLARC) is synthesized
for an industrial linear motor stage to achieve good tracking performance and excellent …

In this paper, an adaptive fuzzy-neural-network (FNN) control scheme based on a radial
basis function (RBF) neural network (NN) is proposed to enhance the performance of a …

In this paper, an adaptive neural network force tracking impedance control scheme based
on a nonlinear observer is proposed to control robotic system with uncertainties and external …

This paper proposes an adaptive fuzzy-neural inference system (ANFIS)-based control
approach for a six degrees of freedom (6-DoF) robotic manipulator. Its main objective is to …

Aimed at the nonlinearity and uncertainty of the manipulator system, a RBF (radial basis
function) neural network-based fuzzy sliding-mode control method was proposed in this …

Two novel nonlinearly activated recurrent neural networks (RNNs) with finite-time
convergence [called finite-time RNNs (FTRNNs)] are proposed and analyzed to solve …