Precision Dosing Cylinder Gluing Operations Using Model Predictive Control-Enhanced Reinforcement Learning

This paper presents the development of an advanced servo system for gluing operations, which integrates a dosing cylinder enhanced by reinforcement learning algorithms to optimize efficiency and accuracy. The system consists of an upper computer, a programmable logic controller (PLC), a main controller (featuring a core board and an IO board), and a dosing cylinder equipped with a servo motor and a glue gun. A comprehensive system model was developed by 1 analyzing the interactions among mechanical, hydraulic, and servo motor subsystems. The implementation of an enhanced Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm, augmented with Model Predictive Control (MPC), enables precise control over voltage inputs and glue gun temperature adjustments. The MPC component predicts future states of the system based on the current state and actions, refining the action selection process by considering the long-term effects of actions. Rigorous simulation testing of the enhanced DDPG algorithm, alongside other algorithms, in configurations involving single and dual cylinder setups, validates its effectiveness, demonstrating significant enhancements in the automated control of the gluing process. The real-world experimental results indicate high positioning accuracy, consistent glue dispensing , and overall stability, making this system suitable for industrial applications requiring precise and reliable gluing.