Robust nonsingular sliding mode control of the maglev train system: case study

Alain, Kammogne Soup Tewa; Kenmogné, Fabien; Martin, Siewe Siewe; Fotsin, Hilaire Bertrand

doi:10.1007/s42452-021-04341-w

Cited by 4 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The design of the GFTISMC has two parts: (1) the sliding surface design with a recursive structure was employed to enable the system to rapidly approach the desired sliding surface and (2) an integral term was incorporated to mitigate the steady-state error. Alain et al [93] designed a control strategy utilizing a precise model of nonsingular robust sliding mode control. The primary objective of this strategy was to minimize the upper bound of both uncertainty and interference in the sliding mode controller.…”

Section: Sliding Mode Control Algorithmsmentioning

confidence: 99%

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

Zhu,

Wang,

2024

Buildings

View full text Add to dashboard Cite

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 Meanwhile, many review papers have become outdated and no longer reflect the current state-of-the-art research in the field.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.

show abstract

Section: Sliding Mode Control Algorithmsmentioning

confidence: 99%

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

Zhu,

Wang,

2024

Buildings

View full text Add to dashboard Cite

show abstract

“…The AVSC (ua) in Eq. ( 13) is active once only to drive the state trajectory to the positively invariant set Ω which is done in some finite time tbf [8]. Once the trajectory enters Ω, then AVSCbf is on to maintain it inside, i.e.…”

Section: Adaptive Barrier-function-based Vsc Designmentioning

confidence: 99%

“…The attraction area was also determined for the control system, and the numerical results confirmed the system's robustness in different simulation scenarios. Adaptive robust nonlinear SMC for the vertical position of the Maglev system is proposed by Alain [8]. The bound of uncertainty was assumed to be known.…”

Section: Introductionmentioning

confidence: 99%

A Barrier Function-Based Variable Structure Control for Maglev System

Ridha¹,

Kadhim²

2022

JESA

View full text Add to dashboard Cite

Magnetic levitation (Maglev) systems are widely employed in the industry especially in mechatronics systems for precise positioning and suspension. They are inherently unstable having nonlinear models with uncertain parameters and exposed to external disturbances. Therefore, high-performance robust control designs are recommended for these systems. An Adaptive Variable Structure Controller based on barrier function (AVSCbf) is designed for the first time in this work to control the displacement of the ball position of a disturbed Maglev system. This approach does not require prior knowledge of the disturbance upper bounds in the design procedure. The state space region defined by the barrier function is designed to be attractive and invariant. This feature is essential to reject disturbances and handle parametric uncertainties. The adaptive law is activated when the state trajectory is initiated outside the invariant set defined by the barrier function. The gain of the VSC is adapted according to an adaptation law, which considers the system input constraints. The control input is constrained to be a bounded positive quantity. The adaptive VSC is only applied during the reaching phase. Once the state reaches the invariant set, the barrier-function-based VSC is applied to confine the state inside it. The resulting overall controller is a chattering-free VSC since the barrier-function based VSC is continuous. The steady-state error is limited to a minimal value by only specifying the barrier function parameter. Numerical simulations are conducted to show the efficiency of the new approach. Three types of VSC controllers for the Maglev system are compared. AVSCbf is compared to the performance of adaptive only VSC without the barrier function (AVSC) and both are designed in this work. AVSCbf is also compared to the classical VSC performance from previous work in the literature. The results of the comparison showed the efficiency of the proposed controller.

show abstract