Adaptive Neural Network Control Framework for Industrial Robot Manipulators

Controlling closed architecture industrial robot manipulators poses significant challenges due to limited access to inner controller configurations and specific control gain structures. The absence of open torque or voltage interfaces further compounds these difficulties. Consequently, traditional methods such as the computed-torque approach often prove inadequate when applied to closed architecture robots, widening the gap between advanced control algorithms and practical industrial requirements. In response, this paper introduces a unified framework that utilizes adaptive neural networks to tackle these challenges in controlling closed architecture industrial manipulators. Our approach operates independently of the robot’s dynamics, inner controller configuration, and control gain structure. We provide thorough evidence showcasing the boundedness of all control variables. Moreover, the proposed approach is versatile, allowing for the use of a single joint velocity controller across robotic manipulators employing closed control architecture, even under varying conditions. Our strategy streamlines implementation without requiring 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.