Adaptive Nonsingular Fast Terminal Sliding mode Control of Robotic Manipulator Based Neural Network Approach

The paper addresses an adaptive robust position control for tracking control of a manipulator under the presence of the uncertainties, such as variant payload, modeling error, friction, and external disturbance. The proposed control uses radial basis function neural networks (RBFNN)s to approximate and cancel the uncertainties. The nonsingular fast terminal sliding mode control (NFTSMC) of the proposed control is developed to guarantees a finite-time convergence and to solve the singular issue of the terminal sliding mode control. Moreover, the learning laws are derived from the Lyapunov approach to ensure the stability and robustness of the whole system. The proposed control is compared with other controllers through both simulations and experiments on a 3-DOF manipulator to exhibit its efficiency with the variant payload and the uncertainties.