Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm

Abstract

Since the positioning accuracy of sensors degrades due to noise and environmental interference when a single sensor is used to localize a suspended rare-earth permanent magnetically levitated train, a multi-sensor information fusion method using multiple sensors and self-correcting weighting is proposed for permanent magnetic levitated train localization. A decay memory factor is introduced to reduce the weight of the influence of historical measurement data on the fusion estimation, thus enhancing the robustness of the fusion algorithm. The Kalman filtering results suffer from inaccuracy when process noise is present in the system. In this paper, we use a covariance adaptive scheme that replaces the prediction step of the Kalman filter with covariance. It uses the covariance adaptive scheme to search the posterior sequence online and reconstruct the prior error covariance. Since the process noise covariance is not used in the new adaptive scheme, the negative impact of the mismatch noise statistics is greatly reduced. Simulation and experimental results show that the use of multi-sensor information fusion and covariance adaptive Kalman algorithm has significant advantages in terms of adaptability, accuracy and simplicity.

Analysis of Transportation Systems for Colonies on Mars

“…The energy consumption analysis of maglev trains and spaceplanes extends the theoretical models presented in earlier works [2][3][4][5] by incorporating detailed simulations that account for the unique challenges posed by the Martian environment, such as reduced gravity, atmospheric drag, and topographical variations. The inclusion of regenerative braking in the maglev system and the consideration of ascent and descent phases in the spaceplane model offer a more comprehensive understanding of their operational dynamics, building upon the foundational knowledge established in previous studies.…”

“…The findings of this study contribute to the growing body of research on Martian transportation systems and their role in supporting future colonization efforts. Previous studies have explored the potential of maglev trains [2,3] and spaceplanes [4] as viable transportation options for Mars, highlighting their respective advantages in terms of energy efficiency, speed, and infrastructure requirements. Our results align with these findings, providing a quantitative basis for comparing the performance and cost implications of these systems in the Martian context.…”

“…Maglev Trains on Mars: The application of magnetic levitation technology for Martian transportation was studied in [2], emphasizing its potential for reducing wear and operational costs in the abrasive Martian environment. Further theoretical models have explored the energy efficiency and feasibility of constructing maglev systems [3] on Mars, considering the planet's unique geological and atmospheric conditions.…”

Optimal Control for a Superconducting Hybrid MagLev Transport System with Multirate Multisensors in a Smart Factory

“…The proposed compensator consists of two parts: a state estimator and a feedback controller [ 21 ]. The state estimator estimates the state according to the operation time of the feedback controller through a uniform time lifting operation based on the system model of the outputs measured with different sampling times [ 22 , 23 ]. By fusing several different heterogeneous sensors into one fast sensing time, the proposed state estimator can improve the control performance of sensors that operate slowly [ 24 , 25 ].…”