Adaptive Neural Network Control Framework for Industrial Robot Manipulators
Gulam Dastagir Khan
Abstract
The control of closed architecture industrial robot manipulators poses significant challenges due to limited access to inner controller configurations and specific control gain structures. Traditional methods, such as the computed-torque approach, often fall short when applied to closed architecture robots, leaving a disparity between advanced control algorithms and practical industrial requirements. In response, this paper introduces a unified framework that leverages adaptive neural networks to address these challenges in controlling closed architecture industrial manipulators. Unlike previous methods, our approach operates independently of the robot’s dynamics, inner controller configuration, and control gain structure. We present thorough evidence demonstrating the boundedness of all control variables. Moreover, our proposed strategy offers global applicability, enabling the implementation of a single joint velocity controller across robotic manipulators with closed control architecture, even in diverse conditions. Notably, our strategy streamlines implementation without necessitating complex calculations for updating control variables. Experimental results and comparative studies are provided to illustrate the applicability and effectiveness of our proposed control strategy compared to existing approaches.