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All Journal Frontier Advances in Applied Science and Engineering
Francis Ifeanyi Anyasi
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics

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Predictive Model Control and Intelligent Agent are Used To Optimize The Precision Control of an Electro-Pneumatic Actuator-Based Robotic Bottle Capper Jack Chinedu Edeubaka; Francis Ifeanyi Anyasi; Peter Francis Inyang; Prince Ekpenyong Odo
Frontier Advances in Applied Science and Engineering Vol. 2 No. 1 (2024)
Publisher : Tinta Emas Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59535/faase.v2i1.245

Abstract

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.
Co-Authors Jack Chinedu Edeubaka Peter Francis Inyang Prince Ekpenyong Odo