A Novel Dual-Loop Model-Free Adaptive Iterative Learning Control and Its Application to the Refrigeration Systems

Abstract

This study investigates a novel dual-input and dual-output Model-Free Adaptive Iterative Learning Control (A-MFAILC) approach for energy-saving control of refrigeration systems, aiming to maintain a minimum stable superheat and a constant evaporation temperature. Superheat control is often unstable due to the complex and high-order nature of refrigeration systems. Furthermore, these systems often face large time delays, which complicate the tracking control process. Such delays can cause inefficiencies and instability in maintaining desired operational parameters, making it challenging to achieve energy savings. To get around these problems, a novel Model-Free Adaptive Iterative Learning Control algorithm has been proposed by incorporating input rate constraints for time-delayed systems.The proposed A-MFAILC algorithm with a single input and single output has been extended to dual input and dual output energy-saving control of refrigeration systems. Complete proofs of convergence analysis have been provided, and the algorithm's performance has been fully evaluated. Simulation tests based on the proposed A-MFAILC algorithm, developed for dual-loop control systems, have been conducted on refrigeration systems. Step signals have been used as input signals for comprehensive performance testing. As a result, the proposed approach demonstrates higher tracking stability and fast response speed, with an average tracking accuracy of 98.68% and 93.87% for superheat and evaporation temperature, respectively.