earticle

영어

In recent years, desktop and gantry-type robots have been widely adopted across industrial applications, ranging from logistics and 3D printing to factory automation and smart farming. Conventional systems, often driven by AC servo motors and configured with ball screw-based linear motion guides, face limitations such as mechanical friction, slip, and energy loss due to contact based motion stages. To address these challenges, this study proposes an advanced position control framework for gantry-type collaborative robots by integrating a digital PID (Proportional-Integral-Derivative) controller with AI based parameter auto tuning techniques. The proposed approach leverages machine learning and reinforcement learning algorithms to optimize control parameters dynamically, overcoming the limitations of traditional manual tuning. An Octave based simulation environment is used to validate initial PID settings, which are subsequently applied to a real world gantry-type robot platform. The results demonstrate significant improvements in real time control accuracy, trajectory tracking, and operational stability. This research not only enhances precision and responsiveness in collaborative robot systems but also introduces a generalized control methodology applicable to various automation devices utilizing AC servo motors. The AI-augmented control framework shows strong potential for deployment in diverse fields such as manufacturing, assembly, inspection, logistics, and medical robotics. Ultimately, the study contributes to establishing a scalable and intelligent control standard for next-generation industrial robotic systems.