Characteristics Optimization of the Maglev Train Hybrid Suspension System Using Genetic Algorithm

Safaei, Farhad; Suratgar, Amir Abolfazl; Afshar, Ahmad; Mirsalim, Mojtaba

doi:10.1109/tec.2014.2388155

Cited by 40 publications

(17 citation statements)

References 22 publications

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“…On this basis, the levitation controller controls the output current of the chopper according to the levitation gap data, so that the electromagnetic attraction generated by the suspension electromagnetic coils can ensure that the levitation gap is within a preset range [8,9]. LCMs fulfill basic requirements for maintaining the expected levitation gap against disturbances from the train body vibration and track unevenness during train operation through the use of advanced control strategies [10][11][12][13], which results in a complex levitation control system with coupling interaction. On top of these hardware subsystems, an effective online condition monitoring (CM) system enables realtime levitation condition awareness of the maglev train.…”

Section: Introductionmentioning

confidence: 99%

A Levitation Condition Awareness Architecture for Low-Speed Maglev Train Based on Data-Driven Random Matrix Analysis

et al. 2020

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Modern low-speed maglev trains typically use multi-node decentralized levitation control modules, which results in a complex levitation control system with coupling interaction. Conducting systematic levitation condition awareness of the levitation control system is still a promising challenge. In this paper, under the hypothesis of levitation residuals following normal distribution, a levitation condition awareness architecture for the levitation control system is proposed based on data-driven random matrix analysis. The proposed architecture consists of an engineering procedure followed by a cascaded mathematical procedure. In the decentralized engineering procedure, the data-driven modeling for individual levitation control modules is achieved by nonlinear autoregressive modeling with an exogenous input neural network, and the unknown parameters are identified by a modified combinatorial genetic algorithm. On this basis, high-dimensional analysis of streaming residual random matrices for the levitation control system is conducted aided by large-dimensional random matrix theory, and the control limits of the constructed indicators are well-designed using the theorical distributions. Based on the comparative analysis of the experimental datasets, the proposed awareness architecture is verified to show the effectiveness of the systematic condition evaluation of the levitation system, and incipient train-guideway interaction vibration abnormalities can be detected in a timely manner. INDEX TERMS Low-speed maglev train, levitation control modeling, levitation condition awareness, random matrix analysis

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Section: Introductionmentioning

confidence: 99%

A Levitation Condition Awareness Architecture for Low-Speed Maglev Train Based on Data-Driven Random Matrix Analysis

et al. 2020

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“…An E-shape hybrid electromagnet was designed by Tzeng et al with the advantage of less flux leakage and enhanced guidance ability in 1994 [10]. Many subsequent works on the PEMS system focused on optimizing the permanent magnet parameters to minimize power loss and maximize carrying capacity [11][12][13], which has paved the way for a full scale PEMS maglev train. In 2017, a single carriage PEMS high speed maglev train was tested on a 1.5km maglev line [14].…”

Section: Introductionmentioning

confidence: 99%

Fault tolerant control for joint structure in PEMS high speed maglev train

Wang

2020

Asian Journal of Control

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The PEMS high speed maglev train, which features a permanent magnet inside an electromagnet, is a new kind of maglev train for long distance intercity transportation. The joint structure, which consists of two single levitation sub‐systems, is the fundamental levitation unit. Two kinds of faults are considered and corresponding fault tolerant control strategies are proposed. The first fault condition is when a gap sensor that is part of a single levitation system is faulty. For this kind of fault, a fault tolerant control strategy based on signal reconfiguration is proposed. The second fault condition is when the whole of a single levitation sub‐system is faulty. Under this condition, a faulty model is firstly established, then a fault tolerant control strategy is designed. When this kind of fault is detected, a switch from the normal controller to the fault tolerant controller can make the faulty system stable.

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“…To meet different application requirements, there has been a variety of maglev trains, among which permanent magnet electromagnetic suspension (PEMS) type maglev train is an innovative maglev aiming at energy-saving and long time levitation [4]. In PEMS maglev train, permanent magnet is added inside the iron core of the electromagnet, the size of the permanent magnet is optimized to reduce the levitation current and weight of the hybrid electromagnet [5]. Compared with traditional electromagnetic suspension (EMS) system, permanent magnet is added to reduce energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Data Driven State Monitoring of Maglev System With Experimental Analysis

Wang

Luo

et al. 2020

IEEE Access

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The reliability of levitation system plays an important role for the safe operation of maglev train. Monitoring the state of the levitation system helps make early judgement to adopt fault tolerant measurement preventing further damage. In this paper, a data-driven state monitoring problem for PEMS high speed maglev train is studied in detail. Firstly preliminaries about levitation system and problem formulation are described. Then a residual generation method based on system input/ouptput data is given. To tackle the varying operational condition problem, a multi-model switching strategy is proposed. For the non-Gaussian property of the system data, a Box-Cox transformation is adopted. The effectiveness of the proposed method is illustrated by experimental data analysis results.

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Characteristics Optimization of the Maglev Train Hybrid Suspension System Using Genetic Algorithm

Cited by 40 publications

References 22 publications

A Levitation Condition Awareness Architecture for Low-Speed Maglev Train Based on Data-Driven Random Matrix Analysis

A Levitation Condition Awareness Architecture for Low-Speed Maglev Train Based on Data-Driven Random Matrix Analysis

Fault tolerant control for joint structure in PEMS high speed maglev train

Data Driven State Monitoring of Maglev System With Experimental Analysis

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