Decoupling Control of Maglev Train Based on Feedback Linearization

Leng, Ping; Li, Yajian; Zhou, Danfeng; Li, Jie; Zhou, Siyang

doi:10.1109/access.2019.2940053

Cited by 15 publications

(15 citation statements)

References 10 publications

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“…the aim is to design a linear integral quadratic controller to make the error between the set-point and the current value of the system output to go to zero. Then the extended version of the model is built as (24).…”

Section: Design Of a Linear Control For The Linearized Closed-loop Sy...mentioning

confidence: 99%

“…The solution had positive results, but the authors did not consider the actual effects of the track irregularities during real-time operation of the suspension module, which has a significant impact on the nonlinear behavior of the system. A solution to solve the fluctuations of the suspension system when a magnetic levitation system passes at low speed over a track step was proposed [24]. The authors developed a feedback linearization controller based on a decoupling technique.…”

Section: Introductionmentioning

confidence: 99%

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Development of a new linearizing controller using Lyapunov stability theory and model reference control

Mfoumboulou

Mnguni

2022

IJEECS

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<p><span>One of the most challenging aspects in the nonlinear control of a magnetic levitation (Maglev) system is to find an efficient control algorithm to achieve the stability and accuracy of the closed-loop system. The challenge is then to develop a linearizing control algorithm to maintain a steel ball at a desired position. In this paper, a novel linearizing control algorithm is proposed, which consists of the Lyapunov direct method (LDM) and the model reference control (MRC). The Lyapunov function is developed using the nonlinear equations of the magnetic levitation system, and the reference model is a linear second order system. Two control methods are developed to guarantee system robustness and output stability. Firstly, a new integral linear quadratic regulator (ILQR) is designed for the reference model. Then, an additional innovative proportional gain is combined with the linearizing controller to make the nonlinear control signal stronger. The simulation results indicate that the proposed linearizing controller has excellent set-point tracking, no time delay, fast rising and settling times, and achieves states stability.</span></p>

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Section: Design Of a Linear Control For The Linearized Closed-loop Sy...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of a new linearizing controller using Lyapunov stability theory and model reference control

Mfoumboulou

Mnguni

2022

IJEECS

View full text Add to dashboard Cite

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“…Wang et al [27] proposed a PNP control strategy to suppress the deviation range of the levitation gap under the excitation of guideway irregularity. Leng et al [28] used the feedback linearization method to solve the problem described in [26]. Besides, Zhou et al [29] and Li et al [30] proposed a scheme to move the sensor position.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Performance Optimization of Electromagnetic Levitation System Considering Sensor Position

Zhou

Cui³

et al. 2020

IEEE Access

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The controlled air gap of the electromagnetic levitation system of maglev train is generally 8-10mm, which makes the vehicle/guideway coupling problem prominent. In practice, it is found that the stability of the magnetic levitation system is affected by guideway irregularities, and the levitation gaps show different dynamic characteristics, which is closely related to the sensor positions. The purpose of optimization of the dynamic performance of the electromagnetic levitation system is to reduce the dynamic deviation amplitude of the levitation gaps. Firstly, a linear model of the module levitation system with guideway is proposed. On this basis, the discrete frequency excitation method is used to obtain the dynamic amplitude response of the levitation gaps at different guideway wavelengths and vehicle speeds. Then, an optimization framework based on sensor positions is proposed. Based on this framework, the optimal sensor position at different speeds is obtained. The simulation results show that the optimal scheme can effectively reduce the deviation amplitude and the difference between the two levitation gaps, thus improving the dynamic performances of the electromagnetic levitation system.

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“…On this basis, the levitation controller controls the output current of the chopper according to the levitation gap data, so that the electromagnetic attraction generated by the suspension electromagnetic coils can ensure that the levitation gap is within a preset range [8,9]. LCMs fulfill basic requirements for maintaining the expected levitation gap against disturbances from the train body vibration and track unevenness during train operation through the use of advanced control strategies [10][11][12][13], which results in a complex levitation control system with coupling interaction. On top of these hardware subsystems, an effective online condition monitoring (CM) system enables realtime levitation condition awareness of the maglev train.…”

Section: Introductionmentioning

confidence: 99%

A Levitation Condition Awareness Architecture for Low-Speed Maglev Train Based on Data-Driven Random Matrix Analysis

et al. 2020

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Modern low-speed maglev trains typically use multi-node decentralized levitation control modules, which results in a complex levitation control system with coupling interaction. Conducting systematic levitation condition awareness of the levitation control system is still a promising challenge. In this paper, under the hypothesis of levitation residuals following normal distribution, a levitation condition awareness architecture for the levitation control system is proposed based on data-driven random matrix analysis. The proposed architecture consists of an engineering procedure followed by a cascaded mathematical procedure. In the decentralized engineering procedure, the data-driven modeling for individual levitation control modules is achieved by nonlinear autoregressive modeling with an exogenous input neural network, and the unknown parameters are identified by a modified combinatorial genetic algorithm. On this basis, high-dimensional analysis of streaming residual random matrices for the levitation control system is conducted aided by large-dimensional random matrix theory, and the control limits of the constructed indicators are well-designed using the theorical distributions. Based on the comparative analysis of the experimental datasets, the proposed awareness architecture is verified to show the effectiveness of the systematic condition evaluation of the levitation system, and incipient train-guideway interaction vibration abnormalities can be detected in a timely manner. INDEX TERMS Low-speed maglev train, levitation control modeling, levitation condition awareness, random matrix analysis

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Decoupling Control of Maglev Train Based on Feedback Linearization

Cited by 15 publications

References 10 publications

Development of a new linearizing controller using Lyapunov stability theory and model reference control

Development of a new linearizing controller using Lyapunov stability theory and model reference control

Dynamic Performance Optimization of Electromagnetic Levitation System Considering Sensor Position

A Levitation Condition Awareness Architecture for Low-Speed Maglev Train Based on Data-Driven Random Matrix Analysis

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