Adaptive Neural Network Control of Electromagnetic Suspension System

Suebsomran, Anan

doi:10.2316/journal.206.2014.2.206-3728

Cited by 5 publications

(5 citation statements)

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“…Because of the difficulty in analyzing the coupling among the four electromagnets on the physically-decoupled EMS bogie, many studies in the literature only focused on the controller design for the single-degree-of-freedom (DoF) system, i.e., single set of electromagnet [4]. Lee et al [5], Sinha and Pechev [6], Su et al [7], and Suebsomran [8] optimized the dynamic performance of the single-DoF system with advanced control algorithms, such as the gain-scheduling control [5], the optimal control [6], the fuzzy control [7], and the neural network control [8], respectively.…”

Section: Introductionmentioning

confidence: 99%

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Coupled Robust Constant-Power-Control Algorithm for Rigid Quadruple EMS Vehicle

Zhang

2023

IEEE Access

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The electromagnetic-suspension (EMS) vehicle has been commercialized for the urban transportation in many countries. However, industrial applications of the small-size rigid quadruple counterpart are still restricted by the coupling problem or the so-called statically indeterminate problem among the four electromagnets. Inspired by the robust zero-power-control algorithm for the permanent-electromagneticsuspension (PEMS) system, this work proposes the robust constant-power-control algorithm with the augmented integral controller to compensate the current coupling among the four electromagnets and to stabilize the rigid quadruple EMS vehicle. The proposed algorithm only involves three feedback control loops and four parameters. Detailed analytical derivations are addressed to determine the four parameters, whereas the numerical simulations are presented to demonstrate the effectiveness and the outstanding robustness of the proposed algorithm. Hence, the coupled robust constant-power-control algorithm significantly enhances the stability and reduces the complexity in the controller design as well as the mechanical structure for the rigid quadruple EMS vehicle. Also, the proposed algorithm is so simple that plenty of room is available to incorporate intelligent control algorithms. Consequently, this work sheds light on the intelligent EMS transportation system for more industrial applications.

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Section: Introductionmentioning

confidence: 99%

“…The magnetic force between the guideway and the electromagnet can be modelled as the bi-variable function [8], [14] of I and Z h as,…”

Section: Introductionmentioning

confidence: 99%

Coupled Robust Constant-Power-Control Algorithm for Rigid Quadruple EMS Vehicle

Zhang

2023

IEEE Access

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“…From simulation and experiment, it was verified that the appropriate neural network model can be used to simulate the magnetic system. Anon and Suebsran [9] proposed an adaptive neural network control structure, which used the radial basis function (RBF) network to approximate the non-linear links in the magnetic levitation system in electromagnetic suspension (EMS). Yongzhi Jing et al [10] introduced a non-contact inductive gap sensor method for compensating high-speed maglev trains based on the RBF network.…”

Section: Introductionmentioning

confidence: 99%

Research of RBF-PID Control in Maglev System

Song

et al. 2020

Symmetry

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Control of the maglev system is one of the most significant technologies of the maglev train. The common proportion integration differentiation (PID) method, which has fixed control parameters, ignores the non-linearity and uncertainty of the model in the design process. In the actual process, due to environmental changes and interference, the inherent parameters of the system will drift significantly. The traditional PID controller has difficulty meeting the control requirements, and will have poor control effect in the actual working environment. Therefore, a radial basis function (RBF)-PID controller is designed in this article, which can use the information from the levitation system identified by the RBF network to adjust the parameters of the controller in real time. Compared with the traditional PID control method, it is shown that the RBF-PID method can improve the control performance of the system through simulation and experiment.

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“…A method for predicting wheel slip based on a support vector machine and an adhesion theory was presented in [8]. Adhesion state recognition based on traditional neural network and support vector machine has avoided detailed analysis of the mechanism of slippage that traditional methods find challenging, but outstanding problems with this approach include a slow learning speed [9], [10] and difficulty in adjusting the parameters.…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition of Adhesion States Using an Extreme Learning Machine

Zhang¹,

Cheng²,

He³

et al. 2017

International Journal of Robotics and Automation

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In this study, the use of an extreme learning machine (ELM) for automatic identification of the adhesion state is investigated. The influence of different activation functions and the number of neurons in the hidden layers on the recognition performance is investigated.This study aims to select a better activation function and construct an approach for the adhesion state recognition by using an ELM.Monitoring data on the adhesion characteristics of a heavy-duty locomotive are used to fabricate the recognition model. Comparing with the backpropagation (BP) neural network and a BP optimized algorithm, the experimental results show that our ELM recognition method has a faster training speed and higher recognition accuracy than the other two approaches.

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Adaptive Neural Network Control of Electromagnetic Suspension System

Cited by 5 publications

References 0 publications

Coupled Robust Constant-Power-Control Algorithm for Rigid Quadruple EMS Vehicle

Coupled Robust Constant-Power-Control Algorithm for Rigid Quadruple EMS Vehicle

Research of RBF-PID Control in Maglev System

Automatic Recognition of Adhesion States Using an Extreme Learning Machine

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