Embedded Model Predictive Control for EMS-type Maglev Vehicles

TL;DR

This paper explores the application of embedded model predictive control to high-speed EMS-type maglev vehicles, demonstrating its robustness and feasibility on resource-limited hardware for stabilizing nonlinear systems at speeds over 600 km/h.

Contribution

It introduces a parameterized model predictive control approach tailored for embedded systems in high-speed maglev vehicles, with validation through processor-in-the-loop testing.

Findings

MPC effectively stabilizes nonlinear, constrained maglev systems at high speeds.

Embedded implementation of MPC is feasible on microcontrollers.

Processor-in-the-loop tests confirm real-time applicability.

Abstract

Current developments of high-speed magnetic levitation technology using the principle of the electromagnet suspension (EMS) focus on reaching vehicle speeds of more than 600 km/h. With increasing vehicle speeds, however, updated control algorithms need to be investigated to reliably stabilize the system and meet the demands in terms of ride comfort. This article examines the modern and popular approach of model predictive control and its application to the magnetic levitation control system. Investigated key aspects are the parameterization of the model predictive controller and its implementation on embedded, resource constrained hardware. The results reveal that model predictive control is capable to robustly stabilize the highly nonlinear and constrained system even at very high speed. Furthermore, processor-in-the-loop studies are carried out to validate the designed control algorithms on a microcontroller.