A Reference Model Assisted Adaptive Control Structure for Maglev Transportation System

Dalwadi, Nihal; Deb, Dipankar; Muyeen, S. M.

doi:10.3390/electronics10030332

Cited by 8 publications

(5 citation statements)

References 40 publications

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“…It can adaptively compensate for parametric uncertainties that may arise during the control process. Dalwadi et al [62] designed a position-stabilizing control strategy for a maglev system operating under highly uncertain parametric conditions. The control strategy used a reference model governed by a reference stabilizer with a nonlinear adaptive control structure.…”

Section: Adaptive Control Algorithmsmentioning

confidence: 99%

“…Chen et al [40] designed a feedback controller to stabilize the nominal dynamic equation-based system model and proposed a data-driven new extended state observer (ESO) to estimate the unknown system states. A model reference method was adopted to address uncertainties in control systems and is normally combined with adaptive control [61,62]. In addition, irregularity has been considered when establishing the maglev electromagnetguideway coupling model [51].…”

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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: Adaptive Control Algorithmsmentioning

confidence: 99%

mentioning

confidence: 99%

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

Zhu,

Wang,

2024

Buildings

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“…Designing control parameters for all levitation units to ensure robustness of all the levitation units under worst-case condition poses a significant challenge. In current research [5], [6], [7], the maglev suspension bogie is typically decomposed into four single levitation units. However, static experiments conducted on the CMS-04 low-speed maglev train [8] have demonstrated that the coupling effect between the two levitation units at one side of the bogie cannot be ignored.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Control of Maglev Levitation System via Hamilton–Jacobi–Bellman Multi-Agent Deep Reinforcement Learning

Zhu,

Wang,

2024

IEEE Trans. Veh. Technol.

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The magnetic levitation control system is a key component of a maglev train, ensuring that the airgap between the train and its guideway is stable. Currently, the levitation controller is designed based on a single-point model in which the coupling effect between two levitation units at one side of the bogie in the maglev train is ignored. However, it is found that the coupling effect can cause unstable levitation problems during long-term operations. Hence, to solve the coupling problem of the two levitation units, a cooperative levitation controller based on the Hamiton-Jacobian-Bellman incorporated multi-agent reinforcement learning (HJB-MADRL) is proposed. The MADRL is adopted for the two-point levitation control considering the coupling effect between the two levitation units. To improve the training of the value network in the MADRL, the HJB function is used in control theory to evaluate the optimality of the value function. The proposed algorithm shows an improved performance compared to the original MADRL algorithm. The effectiveness of the proposed cooperative controller using the proposed algorithm is verified by comparing with a conventional proportional-integral-derivatve (PID) controller and a modelguided controller. The robustness of the HJB-MADRL controller is examined in the presence of pitch motion, change in train load, disturbance force, and track irregularity.

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Section: Introductionmentioning

confidence: 99%

“…In the light of recent control strategies in highly nonlinear, unstable or uncertain mechatronic systems such as Magnetic Levitation Systems (MLS), there is now considerable attention on Lyapunov-based nonlinear robust adaptive controllers. 25 Wang et al 26 proposed a network-based active suspension system with event-triggering mechanism due to being easy to install, low application cost, flexibility and reliability. To deal with the network-induced delays, they designed an event-triggered controller.…”

Section: Introductionmentioning

confidence: 99%

Ride comfort improvement using robust multi-input multi-output fuzzy logic dynamic compensator

Ozbek

Özgüney

Burkan

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article aims to improve the ride comfort of a vehicle keeping a satisfactory without road-holding performance by a novel adaptive control method that is insensitive to unknown system dynamics, model parameter changes, external disturbances and without any loss in suspension working space. With this purpose in mind, a new fuzzy integrated model-based adaptive control law for vehicle suspension systems is proposed in this study to improve the ride comfort and to ensure the robustness of system towards unknown model parameters and external disturbances. First, a model-based adaptive control law, which has the robust characteristics is presented. Afterwards, a multi-input multi-output fuzzy logic controller is designed to determine the controller gains dynamically. The stability of controller is ensured by Lyapunov Theory to achieve uniform boundedness error convergence. A 4 degree-of-freedom half-car model with active suspension system is used in this study to assess the performance of the controller. The results are compared among passive, model-based adaptive control law-controlled and novel fuzzy model-based adaptive control law-controlled systems. It has been concluded that fuzzy model-based adaptive control law further attenuates linear and angular motions of the vehicle increasing the ride comfort. The robustness is also verified for vehicle components having different possible parameter values. It is noteworthy that suspension working length returns to its initial position. Thus, the vehicle ride comfort is improved with no suspension working space loss. Finally, the economic feasibility of controllers has been checked in terms of energy consumption.

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A Reference Model Assisted Adaptive Control Structure for Maglev Transportation System

Cited by 8 publications

References 40 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

Cooperative Control of Maglev Levitation System via Hamilton–Jacobi–Bellman Multi-Agent Deep Reinforcement Learning

Ride comfort improvement using robust multi-input multi-output fuzzy logic dynamic compensator

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