Adaptive Neural Network‐Based Backstepping Control of BLDC‐Driven Robot Manipulators: An Operational Space Approach with Experimental Validation

• DOI: 10.1049/cth2.70016
• Publication Date: 2025-03-17T11:29:57Z
• Authors: Sukru Unver; Bayram Melih Yilmaz; Enver Tatlicioglu; Irem Saka; Erman Selim; Erkan Zergeroglu
• Keywords: Robot manipulator

ABSTRACT This study concentrates on end effector tracking control of robotic manipulators actuated by brushless direct current (BLDC) motors, having parametric uncertainties in their kinematic, dynamical and electrical sub‐systems. Specifically, an operational space controller formulation is proposed that does not rely inverse kinematics calculations at position level still ensures practical despite the presence related to mechanical dynamics, manipulator. Compensation for throughout entire system achieved via use neural network‐based adaptations, overall stability closed‐loop guaranteed Lyapunov‐based arguments. We would like note work addresses following problems: (i) incorporation actuator dynamics into error order achieve increased efficiency, (ii) elimination need remove computational burden (iii) compensation subsystem. Experiment studies were carried out a two degree freedom planar robot manipulator equipped with BLDC motors evaluate effectiveness formulation.