In recent years, numerous attempts have been made to integrate sliding mode control (SMC)
and neural networks (NN) in order to leverage the advantages of both methods while …

In this paper, a synergistic combination of neural networks with sliding mode control (SMC)
methodology is proposed. As a result, the chattering is eliminated and error performance of …

A neural-network-based terminal sliding-mode control (SMC) scheme is proposed for robotic
manipulators including actuator dynamics. The proposed terminal SMC (TSMC) alleviates …

This paper introduces a new control strategy for robot manipulators, specifically designed to
tackle the challenges associated with traditional model-based sliding mode (SM) controller …

Sliding mode control (SMC) is a well-known control method that has been widely studied
and applied for more than 50 years since its inception in the late 1950s [1],[2]. While its …

Highly nonlinear, highly coupled, and time-varying robotic manipulators suffer from
structured and unstructured uncertainties. Sliding-mode control (SMC) is effective in …

Unpredictable disturbances and chattering are the major challenges of the robot
manipulator control. In recent years, trajectory-tracking-based controllers have been …

This paper presents an indirect adaptive neural network sliding mode Control (IANSMC)
technique and a neural network sliding mode control (NNSMC) for underactuated robot …

In order to solve the trajectory tracking problem for robotic manipulators with dynamic
uncertainty, external disturbance and input saturation, a novel second-order sliding mode …

In this article, the issue of adaptive neural fixed-time tracking control for uncertain robotic
manipulators subject to input saturation, external disturbance and prescribed constraints is …