Suspension System Of Maglev Train Research Articles

Parameter optimization of electromagnetic suspension-type maglev train control system based on multi-objective grey wolf non-dominated sorting hybrid algorithm-Ⅱ hybrid algorithm

This paper presents a novel hybrid algorithm based on CMOGWO-ADNSGA-II to solve the vibration stability problem during the operation of a EMS-type maglev train dynamics model subjected to strong non-linear magnetic buoyancy. The proposed algorithm optimizes the control system parameters of EMS-type maglev train suspensions by combining an improved multi-objective chaotic grey wolf algorithm (CMOGWO) with an improved non-dominated Sorting genetic algorithm-II (ADNSGA-II) to enhance the search capability of the algorithm and ensure population diversity. The efficacy of the algorithm is demonstrated by applying it to the EMS-type maglev train suspension frame control system to find the optimal control parameters. Experimental results show that the system with the optimal parameters applied significantly reduces the suspension gap amplitude and the corresponding standard deviation, as well as the vertical acceleration amplitude and the corresponding standard deviation during operation. The proposed algorithm provides a good solution for EMS-type maglev train suspension vibration control, which can improve its performance and safety.

Study on levitation control performance assessment of maglev train based on minimum entropy

In order to meet the strict requirements of the reliability and safety of the commercial operation of the maglev train, it is necessary to carry out the control performance evaluation of the suspension system. However, at present, variance is often used as the evaluation index of control system. This method cannot get accurate performance evaluation results when the system is affected by non-Gaussian disturbance. Therefore, aiming at the non-Gaussian control performance evaluation of maglev train suspension system, a control performance evaluation method of maglev train suspension system based on minimum entropy is proposed in this paper. In view of the non-Gaussian characteristics in the operation data of maglev train suspension system, this paper introduces the feedback invariant entropy of non-Gaussian system, and then realizes the evaluation and analysis of the control performance of maglev train suspension system based on the minimum entropy criterion. Finally, combined with the evaluation and analysis of the operation data of Changsha medium and low-speed maglev train, this paper verifies the effectiveness and accuracy of the proposed performance evaluation method for the control performance evaluation of maglev train suspension system.

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

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.

Adaptive fault-tolerant control of high-speed maglev train suspension system with partial actuator failure: design and experiments

Adaptive fault-tolerant control of high-speed maglev train suspension system with partial actuator failure: design and experiments

Data-Driven Nonlinear Iterative Inversion Suspension Control

The commercial operation of the maglev train has strict requirements for the reliability and safety of the suspension control system. However, due to a large number of unmodeled dynamics of the suspension system, it is difficult to obtain the precise mathematical model of the suspension system. After the suspension system has been operated for a long time with high load, the system model will change due to the wear, aging and failure of components, as well as the settlement of the line and track. The control performance is degraded. Therefore, this paper proposes a data-driven nonlinear iterative inversion suspension control algorithm, which can achieve high-precision tracking performance recovery control after control performance degradation without depending on the suspension system model. The control performance of the suspension system is improved by learning the measured data of the historical suspension system, and the fast convergence of the tracking error and high-precision stable suspension control are realized in the presence of unmodeled dynamics and external noise interference. Based on the historical suspension data of the maglev train suspension control system, the inverse dynamics model of the suspension system is identified by iterative inversion learning based on data drive, and the suspension control framework based on iterative inversion is designed. Then, the nonlinear input update strategy is used to realize the rapid convergence of the learning process. Finally, the simulation experiment of the maglev train suspension system and the physical experiment of the maglev system experimental platform are combined. It is verified that the proposed levitation control algorithm can achieve high-precision fast tracking performance recovery control after the system control performance degrades under noise environment.

Sliding Mode Bifurcation Control Based on Acceleration Feedback Correction Adaptive Compensation for Maglev Train Suspension System With Time-Varying Disturbance

In order to ensure the suspension stability of maglev train, the active control problems of static suspension and dynamic suspension are studied. First, the mathematical model of dynamic suspension characteristics of medium- and low-speed maglev train is established. Second, the PID controller is studied. The results show that the controller is very sensitive to time-varying disturbances such as nonuniformity and load, which leads to the decrease in system robustness. In order to suppress the disturbance effectively, a suspension control method based on the sliding mode reaching law is proposed. Based on the bifurcation phenomenon, the bifurcation point is solved to determine the main control parameters. On the basis of this, an acceleration feedback correction and adaptive control module based on the radial basis function (RBF) network is constructed to effectively suppress the vibration of the electromagnet. The controller consists of a sliding mode bifurcation control law, an acceleration feedback correction module, and an adaptive compensation loop. The simulation and experimental results show that the proposed control algorithm can significantly reduce the variation range of suspension gap with a smoother control current in the presence of complex disturbances and significantly suppress the coupled vibration.

Vibration analysis and multi-state feedback control of maglev vehicle-guideway coupling system

<abstract> <p>Due to the existence of elastic modes in the track, the suspension system of maglev train is prone to vehicle-track coupling vibration, which has become an important problem restricting the further development of maglev train technology. In view of the limitation of the existing rigid track suspension model, this paper establishes an electromagnet-controller-elastic track coupling system model. And then, the nonlinear maglev system is transformed into a linear system by Hartman-Grobman theorem. Since the elastic deformation of the track is difficult to measure, a tracking differentiator is presented to filter out the interference of the displacement signal and obtain the differential signal of the gap between the electromagnet and the track. In order to suppress the vehicle-track coupling vibration, a four-state feedback control method is proposed by introducing the gap differential feedback signal. According to the Hurwitz algebraic criterion, the stability of four-state feedback control system is compared with that of three-state feedback control system. Simulation results show that, the four-state feedback control method can provide the elastic deformation information of the track, and can suppress the coupling vibration between the vehicle and the elastic track effectively.</p> </abstract>

Research on Real-Time Monitoring and Performance Optimization of Suspension System in Maglev Train

With the success of the commercial operation of the maglev train, the demand for real-time monitoring and high-performance control of the maglev train suspension system is also increasing. Therefore, a framework for performance monitoring and performance optimization of the maglev train suspension system is proposed in this article. This framework consists of four parts: plant, feedback controller, residual generator, and dynamic compensator. Firstly, after the system model is established, the nominal controller is designed to ensure the stability of the system. Secondly, the observer-based residual generator is identified offline based on the input and output data without knowing the accurate model of the system, which avoids the interference of the unmodeled part. Thirdly, the control performance is monitored and evaluated in real time by analyzing the residual and executing the judgment logic. Fourthly, when the control performance of the system is degraded or not satisfactory, the dynamic compensator based on the residual is updated online iteratively to optimize the control performance. Finally, the proposed framework and theory are verified on the single suspension experimental platform and the results show the effectiveness.

Component-Level Fault Detection for Suspension System of Maglev Trains Based on Autocorrelation Length and Stable Kernel Representation

At present, in the suspension system of maglev train, the self-diagnosis of most of the components can be realized employing direct sensor measurement, however, there are still some components that cannot be directly measured by sensors or model-based methods, such as the power components in the suspension control box, electromagnetics and related connectors. And although the detection method based on Stable Kernel Representation (SKR) can detect the fault, the length of the data used for SKR affects the speed and results of the detection. To obtain better detection results with the shortest possible data, this paper studies a component-level fault detection method based on autocorrelation length and SKR. This method uses the autocorrelation length to determine the length of the data used to identify the SKR, and then applies the SKR to build a residual generator and set the residual threshold. The experimental results show that the studied method can detect faults of the suspension control box in real-time and effectively, and the simulation results show that the studied method can detect faults of the electromagnet in real-time and effectively. In addition, compared with the traditional method, the method in this paper can obtain better results with less data.

Model identification and nonlinear adaptive control of suspension system of high-speed maglev train

The suspension system of maglev train has intrinsic nonlinear characteristics. The identification processing ability of nonlinear terms and the control accuracy of control algorithm have an important influence on the suspension stability of the train. Especially when the track stiffness is weak, it is easier to affect the suspension stability under the action of small deformation, resulting in the phenomenon of point dropping/rail smashing. Starting from the nonlinear dynamic modelling of maglev suspension system, this paper focuses on the system parameter identification of nonlinear terms and the design of nonlinear control algorithm. Based on Hopfield neural network, the error function and network identification scheme are constructed. In addition, in order to further improve the control accuracy and system robustness, radial basis function (RBF) network adaptive control is carried out based on the identified system model, and the control performance is improved by RBF network approximation principle. Through the numerical simulation analysis, it can be found that the identification effect is good. Moreover, the proposed control algorithm has an obvious effect on improving the control performance and system robustness, which verifies the reliability of the identification results and the control algorithm.

On Convergence Performance of Discrete-Time Optimal Control Based Tracking Differentiator

Time optimal control (TOC) based tracking differentiator (TD) was first proposed by Han as a practical solution to avoid setpoint jump in active disturbance rejection control. In practice, the discrete-time optimal control (DTOC) is implemented in the form of state feedback for a double-integral system, which is widely used to design controllers, observers and noise-tolerant differentiators. The convergence of the DTOC-TD, however, has not been fully understood. This article provides a rigorous full convergence analysis of the DTOC-TD. It then illustrates the frequency-domain characteristics of this DTOC-TD in signal-tracking filtering and differentiation acquisition, giving a rule of thumb for regulating the parameters. Finally, the case studies including comparison simulations and experiments on processing gap sensor' signals in the suspension system of maglev train are carried out to verify the effectiveness of the DTOC-TD.

Research on hardware-in-the-loop simulation of single point suspension system based on fuzzy PID

The stability control of maglev trains is one of the core problems in the research of maglev train technology and the realisation of this goal is of the great scientific value in the field of magnetic levitation. Based on this, the research on the suspension control strategy of single point maglev system is founded and the control strategy is verified by experiments on Magnetic Levitation Ball System (MLBS). Aiming at the structure of multi-group independent control system of maglev train suspension frame, in order to improve the overall stability control ability of the suspension frame, the coupling relationship between the subsystems is established by introducing the suspension response deviation compensator. Finally, the effect of single point suspension control system is discussed and the cooperative control of suspension frame is carried out on MATLAB. Simulation and analysis show that each subsystem has good anti-jamming ability and the suspension system realises the balanced and stable control under different interference signals, which provides a certain reference value for further study of maglev train suspension control.

Research on hardware-in-the-loop simulation of single point suspension system based on fuzzy PID

The stability control of maglev trains is one of the core problems in the research of maglev train technology and the realisation of this goal is of the great scientific value in the field of magnetic levitation. Based on this, the research on the suspension control strategy of single point maglev system is founded and the control strategy is verified by experiments on Magnetic Levitation Ball System (MLBS). Aiming at the structure of multi-group independent control system of maglev train suspension frame, in order to improve the overall stability control ability of the suspension frame, the coupling relationship between the subsystems is established by introducing the suspension response deviation compensator. Finally, the effect of single point suspension control system is discussed and the cooperative control of suspension frame is carried out on MATLAB. Simulation and analysis show that each subsystem has good anti-jamming ability and the suspension system realises the balanced and stable control under different interference signals, which provides a certain reference value for further study of maglev train suspension control.

Internet of Things-Based Online Condition Monitor and Improved Adaptive Fuzzy Control for a Medium-Low-Speed Maglev Train System

The maglev rail transit has entered a rapid development stage. In order to prevent potential safety hazards in the operation of maglev train, the related monitoring technology needs to be studied urgently. In this article, in view of the wide application of the Internet of Things (IoT) in intelligent transportation, a new method for realizing suspension control for medium-low-speed maglev trains using the IoT and an adaptive fuzzy controller is proposed. First, a mathematical model of the suspension system of medium-low-speed maglev trains is established. Then, the basic composition of the IoT and the circuit design of the key components of maglev trains are introduced. On this basis, an improved Apriori algorithm is used to extract the stored historical database and establish a trusted database. Then, according to the data of the trusted database, the suspension airgap control law is extracted, and the adaptive fuzzy rules of the maglev train suspension system are determined. An improved adaptive suspension controller is designed. Finally, the effectiveness of the method is verified by experiments utilizing a full-scale maglev train.

Optimal Design of Gap Sensor for High-Speed Maglev Train

The gap sensor plays an important role in high-speed maglev train which is able to measure the variation of trains’ suspension gap. The magnetic fields distribution is different clearly when the sensor is opposite to different position in a tooth-groove period of stator railway. The fluctuation of equivalent inductance leads to the slot-effect. The slot-effect is an important factor affecting the stability of the high-speed maglev train suspension control system. The distribution of magnetic field intensity of the flat rectangular coil in different gaps and positions is analysed, and the mechanism of slot-effect is revealed. A new coil is proposed in this paper and this optimized coil can create a smaller slot error than the flat rectangle coil. The simulation results show that the slot error of optimized coil is always less than the error of original coil in any gap distance. The slot error is reduced by half at the working air gap from 8mm to12mm. The slot error of the optimized coil reaches a maximum at 2mm and reaches its minimum at the rated suspension gap 10mm.

Multi-Model Switching Based Fault Detection for the Suspension System of Maglev Train

In order to satisfy the requirements of on-line monitoring of the suspension system under complex conditions, a fault detection method for maglev train suspension system based on multi-model switching is proposed. In the proposed method, the healthy samples are extracted through the moving time window, and then the features of the healthy samples are extracted by the Fast Walsh–Hadamard transform and filtered by the median filter. Then, the normalization is used to eliminate the difference of feature vectors, and then the principal component analysis method is used to reduce the dimension and de-correlation of the feature matrix contributing to a hyper-sphere space. Finally, the health threshold and the fault threshold are determined by Euclidean distance, then fault detection models are established for various operating conditions. Taking the positive line operation condition as an example, the proposed method is compared with the method based on the original feature and support vector data description based on the original feature. The results demonstrate that the proposed method is superior to the other two methods in terms of health detection rate and false positive rate. In addition, the proposed method is of the characteristics of low computational complexity, no-parameter optimization, and good robustness. It can be applied in practical engineering providing a certain research basis for data deep mining such as fault diagnosis and fault prediction.

Study on reliability analysis of suspension controller of the medium and low speed maglev vehicle

Suspension controller is the core device of suspension system of the medium and low speed maglev vehicle, its reliability directly affects the stability, reliability and safety operation of the whole medium and low speed maglev train. Reliability analysis is of great theoretical and practical value for improving the performance of the suspension controller.
 Taking Hunan Changsha maglev express as an application case, based on the mechanism and functional structure of the suspension controller, the reliability of the suspension controller is analyzed and studied. According to the Chinese standard GJB/Z 299C, the reliability prediction handbook for electronic equipment, the reliability of the suspension controller is calculated and analyzed by synthesizing the stress analysis method, the component counting method and the RBD reliability block diagram method. The reliability weak points of the suspension controller are analyzed, and the design optimization proposal is suggested to improve the suspension controller reliability.
 Background: Medium and low speed maglev traffic has gained wide attention and engineering application in China. It is very necessary to study the reliability of the medium and low speed maglev train.
 Purpose: The purpose of this paper is to carry out reliability research on the levitation control system of medium low speed maglev train.
 Methods: The stress analysis method, component counting method and RBD reliability block diagram method are used to calculate and analyze the reliability of the suspension controller.
 Results: The reliability quantitative analysis results of the suspension controller are analyzed, and the reliability weakness of the suspension controller is analyzed and studied. A design optimization proposal to improve the reliability of the suspension controller is proposed.
 Conclusion: Through the reliability prediction analysis of the suspension controller, the reliability and weakness of the suspension controller can be determined, which provides theoretical guidance for the improvement of the design scheme and the performance optimization of the maglev train suspension controller.

Power traction and suspension system of maglev train

Power traction and suspension system of maglev train

Fault Detection Based on Tracking Differentiator Applied on the Suspension System of Maglev Train

A fault detection method based on the optimized tracking differentiator is introduced. It is applied on the acceleration sensor of the suspension system of maglev train. It detects the fault of the acceleration sensor by comparing the acceleration integral signal with the speed signal obtained by the optimized tracking differentiator. This paper optimizes the control variable when the states locate within or beyond the two-step reachable region to improve the performance of the approximate linear discrete tracking differentiator. Fault-tolerant control has been conducted by feedback based on the speed signal acquired from the optimized tracking differentiator when the acceleration sensor fails. The simulation and experiment results show the practical usefulness of the presented method.

Hopf bifurcation of the maglev time-delay feedback system via pseudo-oscillator analysis

This paper investigates the local dynamics around the trivial solution of suspension system of maglev train with time-delayed feedback signals. With characteristic root method, the linear stability analysis of the maglev system is obtained, which implies that a Hopf bifurcation may occur when time delay exceeds a critical value. To gain insight into the periodic motion, the pseudo-oscillator analysis is used to calculate the bifurcated periodic solution, and to determine the direction of the bifurcation. Unlike the widely used methods such as manifold reduction, the pseudo-oscillator analysis involves simple computation and gives prediction of the local dynamics with high accuracy. Numerical simulation results show that the existence of the Hopf bifurcation and the amplitude of the periodic solution can be determined by time delay and control parameters. So appropriately selecting them can restrain vibration between vehicle and guideway of the system effectively.