Adaptive fault-tolerant tracking control for multi-joint robot manipulators via neural network-based synchronization

Le, Quang Dan and Yang, Erfu (2024) Adaptive fault-tolerant tracking control for multi-joint robot manipulators via neural network-based synchronization. Sensors, 24 (21). 6837. ISSN 1424-8220 (https://doi.org/10.3390/s24216837)

[thumbnail of Le-Yang-Sensors-2024-Adaptive-fault-tolerant-tracking-control-for-multi-joint-robot-manipulators]
Preview
 Text. Filename: Le-Yang-Sensors-2024-Adaptive-fault-tolerant-tracking-control-for-multi-joint-robot-manipulators.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (5MB)| Preview

Abstract

In this paper, adaptive fault-tolerant control for multi-joint robot manipulators is proposed through the combination of synchronous techniques and neural networks. By using a synchronization technique, the position error at each joint simultaneously approaches zero during convergence due to the constraints imposed by the synchronization controller. This aspect is particularly important in fault-tolerant control, as it enables the robot to rapidly and effectively reduce the impact of faults, ensuring the performance of the robot when faults occur. Additionally, the neural network technique is used to compensate for uncertainty, disturbances, and faults in the system via online updating. Firstly, novel robust synchronous control for a robot manipulator based on terminal sliding mode control is presented. Subsequently, a combination of the novel synchronous control and neural network is proposed to enhance the fault tolerance of the robot manipulator. Finally, simulation results for a 3-DOF robot manipulator are presented to demonstrate the effectiveness of the proposed controller in comparison to traditional control techniques.

ORCID iDs

Le, Quang Dan and Yang, Erfu ORCID logoORCID: https://orcid.org/0000-0003-1813-5950;
CORE (COnnecting REpositories)