On dual-loop model-free adaptive iterative learning control and its application

Abstract

This study investigates a dual input and dual output Model-Free Adaptive Iterative Learning Control (MFAILC)-based energy-saving control of the refrigeration system to maintain a minimum stable superheat and a constant evaporation temperature. Traditional PID control for superheat control is often unstable due to the complex and high-order nature of the refrigeration systems. Additionally, the presence of nonlinearities and time variations complicates the design of smart controllers. To get around these problems, an advanced control technique MFAILC algorithm was first designed for single input and single output. Subsequently, the proposed MFAILC algorithm was extended to dual-input and dual-output energy-saving control of refrigeration systems. To test the performance of this innovative methodology, a qualitative and quantitative comparisons, as well as a statistical ANOVA test, have been conducted between the proposed method and the Model-Free Adaptive Control (MFAC) algorithm to evaluate the performance. Step signals have been utilized as the given 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 98.10% for superheat and 91.72% for evaporator temperature, among the simulation experiments.