Robotic Arm Drift Compensation Using Adaptive Computed Torque Control Based on Reinforcement Learning

Author(s)	Mr. Pride Mashiyani, Didymus Tanyaradzwa Makusha
Country	Zimbabwe
Abstract	Robotic arms often experience drift over time due to various factors such as temperature fluctuations, gearbox backlash, wear and tear, sensor inaccuracies, and changes in load. While model-based methods have addressed drift compensation, they come with considerable limitations compared to data-driven approaches. This paper will review the PID, Computed Torque Control and Adaptive Computed Torque Control based on Reinforcement Learning, soft actor critic (SAC) model, examining their strengths, weaknesses, and potential areas for improvement to enhance the accuracy and precision of robotic systems in industrial settings.
Keywords	Computed Torque Control (CTC), Machine Learning (ML), Propotional, Integral, Derivative (PID), Reinforcement Learning (RL), Soft Actor Critic (SAC).
Field	Computer > Automation / Robotics
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-06-11
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.47315
Short DOI	https://doi.org/g9pzw6

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