Genetic Algorithm Tuned Super Twisting Sliding Mode Controller for Suspension of Maglev Train With Flexible Track

Teklu, Esaias Abera; Abdissa, Chala Merga

doi:10.1109/access.2023.3262416

Cited by 17 publications

(4 citation statements)

References 35 publications

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“…The proposed controller was verified through simulations and experiments, and the results demonstrate that it effectively weakened chattering and exhibited adaptability to external disturbances and state variable transitions. Teklu et al [97] proposed a genetic-algorithm-tuned super-twisting SMC on a vehicle guideway coupling model with varying load and external random disturbances.…”

Section: Sliding Mode Control Algorithmsmentioning

confidence: 99%

“…The proposed controller was verified on a full-scale Internet of Things (IoT) maglev train system at Tongji University. In addition, sliding mode robust adaptive control [109], robust control [71], feedback linearization control [46], double loop PID considering control gain perturbation [86], and GA tuned super-twisting-SMC (ST-SMC) [97] have been proposed to suppress the vibration of the coupling system.…”

Section: Vehicle-guideway Vibration Suppression Control Designmentioning

confidence: 99%

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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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Section: Sliding Mode Control Algorithmsmentioning

confidence: 99%

Section: Vehicle-guideway Vibration Suppression Control Designmentioning

confidence: 99%

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

Zhu,

Wang,

2024

Buildings

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“…For the maglev train suspension system, preliminary research mainly focused on dynamic stability [6][7][8], running smoothness [9][10][11] and advanced control algorithms [12][13][14][15][16]. In recent years, the performance analysis and evaluation methods of suspension control systems have begun to attract special attention of researchers.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of the suspension system on Maglev trains based on measurement data

Ni,

Dai,

et al. 2024

Metrology and Measurement Systems

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In order to ensure the safe operation of electromagnetic suspension (EMS) maglev trains, it is necessary to pay attention to the control loop performance of the suspension system. The suspension system with closed-loop control is tuned to achieve excellent performance at its early stage of operation. After running for a period of time, the control loop may encounter problems e.g., degraded operation, and paralysis may occur in severe cases. In order to quantify the control performance of the suspension system in an explicable manner, this paper proposed a data-driven control loop performance evaluation method based on fractal analysis, which does not require any external sensors and can be applied without data source restrictions such as dimension, volume and resolution. The control loop performances of such suspension systems were monitored, analysed, and evaluated by cross-sectional study, based on the field data of a commercial operation line in the commissioning stage. Furthermore, the track condition was revealed by capturing performance changes of the suspension system running on different guideway girders. The results demonstrate that the proposed method enables early warning of the degeneration of the suspension systems and the track.

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“…The influence of time delays and disturbances on the system is effectively suppressed. In addition, several scholars have applied artificial intelligence algorithms to tackle the challenge, such as neural networks [30][31][32], optimization techniques [33,34], genetic algorithms [35], and so on.…”

Section: Introductionmentioning

confidence: 99%

Decoupling Control for Module Suspension System of Maglev Train Based on Feedback Linearization and Extended State Observer

Li,

Leng,

et al. 2023

Actuators

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The suspension gap of the electromagnetic suspension maglev train is around 8 mm. In practice, it is found that the system gap fluctuations are amplified due to the inner coupling of the suspension module system in the maglev train. In addition, maglev trains are affected by load disturbances and parameter perturbations during operation. These uncertainties reduce the ride comfort. Therefore, it is necessary to propose a novel control strategy to suppress inner coupling while reducing the influence of uncertainties on the system. In this paper, a control strategy based on feedback linearization and extended state observer (ESO) is proposed to address this challenge. Firstly, the suspension module system model is established with parameter uncertainties and external disturbances. Additionally, the inner coupling of the suspension module is represented in this model. Subsequently, the feedback linearization method based on differential geometry theory is applied to reduce the effect of inner coupling. Meanwhile, the system uncertainties are transformed into equivalent disturbances by this method. Afterward, a linear ESO is designed to estimate the equivalent disturbances. Finally, a state feedback controller is used to achieve stable suspension and compensate for the disturbances. Simulation and experimental results show that the proposed decoupled control strategy significantly suppresses the influence of inner coupling and uncertainties on the system compared with the traditional PID control strategy.

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Genetic Algorithm Tuned Super Twisting Sliding Mode Controller for Suspension of Maglev Train With Flexible Track

Cited by 17 publications

References 35 publications

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

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

Performance evaluation of the suspension system on Maglev trains based on measurement data

Decoupling Control for Module Suspension System of Maglev Train Based on Feedback Linearization and Extended State Observer

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