Introducing an improved control method for instrument air unit based on fuzzy and iterative learning control

An instrument air unit is a critical component in industrial processes, providing compressed air to operate instrumentation devices and control various factory operations. It comprises air compressors, filters, dryers, and other equipment, serving as an indispensable element of any industrial setup. However, the instrument air unit exhibits a complex, time-varying system behavior, with delayed and erratic characteristics. This instability and broad range of fluctuations often lead to disturbances in control processes, potentially disrupting production operations. To address these challenges, stability, time management, and precise pressure control of the instrument air unit are vital for maintaining efficiency in industrial applications. In this study, we propose and implement an innovative pressure, timing, and drying control structure that leverages Iterative Learning Control (ILC) combined with fuzzy logic techniques. The primary goal is to achieve stable and accurate pressure regulation, optimized timing sequencing, and compliance with Instrument Air Standards (ISA) to enhance system performance and reliability. Experimental results validate the effectiveness of the proposed method, demonstrating improved control precision and quality assurance in real-world industrial applications.