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Embedded Model Predictive Control for EMS-type Maglev Vehicles

Arnim Kargl, Mario Hermle, Zhiqiang Zhang, Yanmin Li, Dainan Zhao, Yong Cui, Peter Eberhard

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.

Embedded Model Predictive Control for EMS-type Maglev Vehicles

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.
Paper Structure (15 sections, 13 equations, 11 figures, 1 table)

This paper contains 15 sections, 13 equations, 11 figures, 1 table.

Table of Contents

  1. Introduction
  2. Magnetic Levitation System
  3. Analysis Model
  4. Synthesis Model
  5. Model Predictive Control Design
  6. Model Predictive Control Implementation
  7. Direct Methods
  8. Indirect methods
  9. Embedded Implementation
  10. Simulative Investigation
  11. Prediction Horizon
  12. Cost Function Parameterization
  13. Robustness against Disturbances
  14. Computation Times and Suboptimality
  15. Conclusion and Outlook

Figures (11)

  • Figure 1: Regions of attraction of the Maglev system using an LQR and nonlinear MPC.
  • Figure 2: Cross-sections of an EMS-type magnetic levitation vehicle and illustration of the electromagnetically levitated single-point mass.
  • Figure 3: Receding horizon scheme visualized for a dynamic system with scalar state and input.
  • Figure 4: Illustration of the multiple shooting discretization for a simple boundary value problem.
  • Figure 5: Processor-in-the-loop (PiL) setup including the simulation model implemented in Matlab / Simulink and the control algorithm implemented on the embedded target hardware. The communication is realized by a USB--UART interface.
  • ...and 6 more figures