A nonlinear control method for the electromagnetic suspension system of the maglev train

Xu, Junqi; Zhou, Yuan

doi:10.1007/bf03325756

Cited by 19 publications

(4 citation statements)

References 4 publications

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“…Some parameters of the electromagnetic suspension system are presented in Table 13.1 (other sets of parameters can be found in [103,183,231]), and the allowable bounds of θ(k) are listed in Table 13.2. With the parameters given in Tables 13.1 Constructing a non-PDC fuzzy controller by (13.27), we can get the system states of the closed-loop system.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Systems, such as that in high-speed maglev passenger trains [231,105], levitation of wind tunnel models, levitation of molten metal in induction furnaces, vibration isolation and frictionless bearings, are mostly based on electromagnetic suspension systems. Therefore, in this view, electromagnetic suspension systems can be regarded as repulsive system or attractive system which is based on the source of electromagnetic levitation forces.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Control Systems with Time-Delay and Stochastic Perturbation

Wu¹,

Su²,

Shi³

2015

Studies in Systems, Decision and Control

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The series "Studies in Systems, Decision and Control" (SSDC) covers both new developments and advances, as well as the state of the art, in the various areas of broadly perceived systems, decision making and control-quickly, up to date and with a high quality. The intent is to cover the theory, applications, and perspectives on the state of the art and future developments relevant to systems, decision making, control, complex processes and related areas, as embedded in the fields of engineering, computer science, physics, economics, social and life sciences, as well as the paradigms and methodologies behind them. The series contains monographs, textbooks, lecture notes and edited volumes in systems, decision making and control spanning the areas of Cyber-Physical SystemsSocial Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output.More information about this series at

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy Control Systems with Time-Delay and Stochastic Perturbation

Wu¹,

Su²,

Shi³

2015

Studies in Systems, Decision and Control

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“…State feedback control can be directly adopted in nonlinear systems. Xu and Zhou et al [82] adopted the state feedback control method to replace the nonlinear part in the nonlinear maglev system. The electromagnetic force was decomposed into four parts for controller design.…”

Section: State Feedback Control Algorithmsmentioning

confidence: 99%

A Review of Levitation Control Methods for Low- and Medium-Speed Maglev Systems

Zhu,

Wang,

2024

Buildings

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Maglev transportation is a highly promising form of transportation for the future, primarily due to its friction-free operation, exceptional comfort, and low risk of derailment. Unlike conventional transportation systems, maglev trains operate with no mechanical contact with the track. Maglev trains achieve levitation and guidance using electromagnetic forces controlled by a magnetic levitation control system. Therefore, the magnetic levitation control system is of utmost importance in maintaining the stable operation performance of a maglev train. However, due to the open-loop instability and strong nonlinearity of the control system, designing an active controller with self-adaptive ability poses a substantial challenge. Moreover, various uncertainties exist, including parameter variations and unknown external disturbances, under different operating conditions. Although several review papers on maglev levitation systems and control methods have been published over the last decade, there has been no comprehensive exploration of their modeling and related control technologies. Meanwhile, many review papers have become outdated and no longer reflect the current state-of-the-art research in the field. Therefore, this article aims to summarize the models and control technologies for maglev levitation systems following the preferred reporting items for systematic reviews and meta-analysis (PRISMA) criteria. The control technologies mainly include linear control methods, nonlinear control methods, and artificial intelligence methods. In addition, the article will discuss maglev control in other scenarios, such as vehicle–guideway vibration control and redundancy and fault-tolerant design. First, the widely used maglev levitation system modeling methods are reviewed, including the modeling assumptions. Second, the principle of the control methods and their control performance in maglev levitation systems are presented. Third, the maglev control methods in other scenarios are discussed. Finally, the key issues pertaining to the future direction of maglev levitation control are discussed.

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“…The malfunction of suspension controller means that the suspension controller fails to suspend the maglev train within a certain range. Experiments have indicated that the malfunction of the suspension control system easily occurs when the linearized model is subjected to interference [11]. In addition, malfunction of the suspension control system can be caused by the failure of its components such as the abnormal of suspension sensors (including gap, current, and acceleration sensors), the short circuit failure, the electromagnet with a less effective number turn or higher temperature, the earth leakage failure of electromagnet, and the failure of power supply [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Malfunction Detection of Maglev Suspension Controllers

Wang,

et al. 2023

Mathematics

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This study aims to develop a track-side online monitoring system for malfunction detection in the suspension controllers of maglev trains during their in-service operation. The hardware module of the system includes two arrays of accelerometers deployed on an F-type rail and a data acquisition unit. The software module of the system consists of codes for three functions: (i) the identification of time intervals in relation to the passage of each suspension controller via synchrosqueezing transform; (ii) the extraction of a feature index (FI) sequence synthesized by modulating the response amplitude, frequency, and running speed; and (iii) the formulation of a Bayesian dynamic linear model for real-time malfunction detection in maglev suspension controllers. For verification of the proposed monitoring system and malfunction detection algorithm, full-scale tests have been conducted on an maglev test line using the devised system, where a maglev train was run at different speeds with malfunction occurring in the suspension controllers. The malfunction detection results of the proposed approach are exemplified via comparison with the recorded suspension gaps after the trial run of the maglev train. The fidelity of the results obtained using the extracted FI sequence and using the raw monitoring data are compared. The superiority of the proposed malfunction detection algorithm is also discussed via comparison with the results of the different train speeds.

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A nonlinear control method for the electromagnetic suspension system of the maglev train

Cited by 19 publications

References 4 publications

Fuzzy Control Systems with Time-Delay and Stochastic Perturbation

Fuzzy Control Systems with Time-Delay and Stochastic Perturbation

A Review of Levitation Control Methods for Low- and Medium-Speed Maglev Systems

Real-Time Malfunction Detection of Maglev Suspension Controllers

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