Predictive Model Control and Intelligent Agent are Used To Optimize The Precision Control of an Electro-Pneumatic Actuator-Based Robotic Bottle Capper

The inferior corking performance observed in bottled drinks from breweries is attributed to poor precision. To address this issue, an enhanced precision control system for an electro-pneumatic actuator-based bottle capper is introduced, utilizing model predictive control and intelligent agent technologies. This involves characterizing the electro-pneumatic actuator system, developing a conventional model for the robotic bottle capper, and designing a rule base to improve precision in the capping mechanism, thereby boosting production capacity per unit time. To achieve this, AI is trained to design a rule-based model ensuring optimal efficacy of the capping mechanism, thus enhancing the brewery industry's production capacity and revenue generation. A SIMULINK model is developed to demonstrate the improved precision control of the electro-pneumatic actuator robotic bottle capper using model predictive control and intelligent agent. Results show a significant increase in corking precision from 94.4% with conventional methods to 99.9% when intelligent agents are incorporated into the system. This translates to a 5.5% improvement in corking precision, with production capacity increasing from 27,000 crates using conventional methods to 28,570 with model predictive control. Moreover, with the integration of intelligence, the production capacity further rises to 342,500 bottles.