Global fast terminal integral sliding mode control based on magnetic field measurement for magnetic levitation system

Li, Guidan; Ren, Zongqin; Li, Bin; Fu, Tongling; Duan, Peihua

doi:10.1002/asjc.2660

Cited by 2 publications

(2 citation statements)

References 37 publications

(52 reference statements)

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“…Modifications have been made to SMC for various purposes. To achieve global fasttracking control of the maglev system during the startup and stable processes, Li et al [18] built a second-order flux linkage model. They proposed a global fast-terminal integral sliding mode controller (GFTISMC) with a recursive structure to enhance the system's overall fast response speed and minimize the steady-state error.…”

Section: Sliding Mode Control Algorithmsmentioning

confidence: 99%

“…Due to the inherently strong nonlinearity of the magnetic levitation system, controllers based on linear simplified designs may become unstable or ineffective when the system is subjected to external disturbances. To enable the designed levitation controller to handle external disturbances as smoothly and efficiently as possible, advanced levitation controllers based on different nonlinear control theories have been proposed to improve the overall performance of the control system, such as robust control [11][12][13], adaptive control [14,15], and sliding mode control (SMC) [16][17][18]. However, most of these control strategies degrade in performance when there are disturbances because they rely on a precise model and detailed information about the system.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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%