An Adaptive Neural Non-Singular Fast-Terminal Sliding-Mode Control for Industrial Robotic Manipulators

Abstract

Featured Application: The proposed control methodology could be applied to not only the joint position tracking control for industrial robotic manipulators such as serial, parallel robots, and an electrohydraulic series elastic manipulator, but also other mechanical systems that belong to the class of general nonlinear second-order system. For example, it could be applied for the stabilization or trajectory tracking of mechanical systems as a piezo positioning stage, magnetic levitation systems, or for the chaos control, synchronization, and anti-synchronization of chaotic complex systems.Abstract: In this study, a robust control strategy is suggested for industrial robotic manipulators. First, to minimize the effects of disturbances and dynamic uncertainties, while achieving faster response times and removing the singularity problem, a nonsingular fast terminal sliding function is proposed. Second, to achieve the proposed tracking trajectory and chattering phenomenon elimination, a robust control strategy is designed for the robotic manipulator based on the proposed sliding function and a continuous adaptive control law. Furthermore, the dynamical model of the robotic system is estimated by applying a radial basis function neural network. Thanks to those techniques, the proposed system can operate free of an exact robotic model. The suggested system provides high tracking accuracy, robustness, and fast response with minimal positional errors compared to other control strategies. Proof of the robustness and stability of the suggested system has been verified by the Lyapunov theory. In simulation analyses, the simulated results present the effectiveness of the suggested strategy for the joint position tracking control of a 3-degree of freedom (3-DOF) PUMA560 robot.

Coupling Control Strategy and Experiments for Motion Mode Switching of a Novel Electric Chassis

“…To make E 1 zero, the Lyapunov function is used to judge the system stability. Similar to the studies of [34][35][36], taking the wheel 1 of the FC as an example, a Lyapunov function is constructed to judge the system stability, as shown by Equation (21):…”

Special Feature on Advanced Mobile Robotics

“…Kinematics and their system model are important factors to solve the problem. Vo et al [42] proposed an adaptive sliding-model control to industrial robotic manipulators. It uses a system model with radial-basis function neural network.…”

A Neural Network Based Sliding Mode Control for Tracking Performance with Parameters Variation of a 3-DOF Manipulator

“…However, up to now, formulating controllers satisfying control performance and the stability of the system is still a big challenge due to its high nonlinearity, coupling dynamics reactions, and uncertainties [1]. In order to deal with these problems, many approaches such as using a proportional integral derivative (PID) control [2], feedback linearization [3], robust control [4,5], adaptive control [6][7][8], backstepping (BSP) control [9,10], hybrid proportional derivative sliding mode control (PDSMC) [11], sliding mode control (SMC) [12][13][14][15] and even intelligent control [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] have been studied.…”

“…Neural network control (NN) has been successfully used in many commercial and industrial applications in recent years [21][22][23][24][25][26][27][28][29][30][31]. In those studies, the NN is used as a compensator or an approximator to boost or enhance the control signal, compensate uncertainties, or eliminate disturbances.…”

“…Vo. et al proposed the radial basis function neural network (RBFNN) to approximate unknown robot function [26]. To et al utilized RBFNN to control the position of a piezo positioning stage [27].…”