Distributed Closed-Loop Reference Adaptive Learning Control for Parallel Mutual Inductance Circuits

Abstract

Parallel mutual inductance circuits (PMICs) are fundamental components of levitation control systems in maglev transportation, which are controlled to regulate levitation gaps. Conventional levitation controllers are typically designed individually for these circuits, with the collaboration performance being overlooked, consequently leading to levitation failures. This brief proposes a distributed closed-loop reference iterative learning control for the collaboration issue of PMICs by leveraging the operation repetitiveness of maglev trains. Particularly, a novel closed-loop reference model is proposed to rectify the original trajectory by receiving feedback from these circuits, thereby promoting the transient response and accelerating the convergence speed. A new composite energy function (CEF) is established to facilitate convergence analysis, and the effectiveness of the proposed control scheme is validated through simulation results.