A Fuzzy-Logic-System-Based Cooperative Control for the Multielectromagnets Suspension System of Maglev Trains With Experimental Verification

Sun, Yougang; Qiang, Haiyan; Wang, Long; Ji, Wen; Mardani, Abbas

doi:10.1109/tfuzz.2023.3257036

Cited by 35 publications

(10 citation statements)

References 36 publications

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“…The relationship between the optimal control parameters of the controller and the steady-state current was deduced by observing the real-time steady-state levitation current as the load mass changed. Sun et al [89] proposed an adaptive fuzzy cooperative control strategy for a multi-point levitation system. They focused on addressing challenges related to uncertainties, dead-zone, and saturation effects in the control system.…”

Section: Adaptive Control Algorithmsmentioning

confidence: 99%

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 Meanwhile, many review papers have become outdated and no longer reflect the current state-of-the-art research in the field.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

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

Zhu,

Wang,

2024

Buildings

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“…For the control strategies in joint space, precise control can be achieved by fully utilizing real-time information provided by the encoder feedback. Consequently, decoupling control methods can be effectively employed in real-world scenarios to meet the high-speed motion control of the platform [34,35]. However, the drawbacks of the aforementioned controllers in joint space are apparent.…”

Section: Introductionmentioning

confidence: 99%

Synchronization Control with Dynamics Compensation for Three-Axis Parallel Motion Platform

Zhou,

Gao,

Zhang

2024

Actuators

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The three-axis parallel motion platform (TAPMP) with a common stator has low motion inertia, enabling highly precise and high-speed motion over a large range of strokes. The primary challenge faced by the TAPMP lies in the mutual pulling exerted between the common stator motors during motion. The driving forces generated by the motors are closely associated with their synchronization motion, a connection often overlooked in the design of existing controllers. To address this issue, this paper presents a novel synchronization controller with dynamics compensation (SC–DC) to achieve motion synchronization between the three motors, ultimately enhancing the platform’s tracking accuracy in task space. In this SC–DC method, the synchronization error of the common stator motors is introduced to represent the synchronized motion relationship between adjacent motors, and a dynamic feedforward control is adopted to compensate for the motor’s driving force. The stability of the proposed controller is analyzed using Lyapunov theory, demonstrating the convergence of both the tracking error and synchronization error. Trajectory tracking simulations and experimental studies are conducted on the TAPMP. The results show that, compared to the augmented proportional-derivative controller with dynamic compensation, the proposed controller significantly reduces both the MAE of the tracking error and synchronization error on the q1 motor by 71.88% and 73.02%, respectively, demonstrating its performance advantages in trajectory tracking and synchronization.

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“…Because fuzzy control has the advantages of good robustness and being easy to understand, and the design of fuzzy rules can be integrated into the prior human experience, many train control methods combined with fuzzy logic have emerged. [8][9][10][11][12] However, the establishment of fuzzy rules and membership functions relies too much on engineering experience, and it is not easy to adapt to the complex and changeable operating environment, which is not conducive to real-time tracking. MAO et al 13 proposed a new adaptive fault-tolerant sliding mode control scheme considering disturbance and actuator failure.…”

Section: Introductionmentioning

confidence: 99%

Improved active disturbance rejection speed control for autonomous driving of high-speed train based on feedforward compensation

Yue,

Wang,

Xiao

et al. 2023

Science Progress

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Due to the complex and changeable train operation environment and the unstable and time-varying parameters, accurate modeling is limited. Therefore, a modified active disturbance rejection control algorithm based on feedforward compensation (FC-MADRC) is proposed targeting the speed control problem of trains under the circumstances of external disturbances, which reduces the dependence on the train model. Firstly, the state space equation is established based on the single-particle mathematical model of the train, and all the running resistances are regarded as disturbances. Secondly, the FC-MADRC algorithm is designed. Based on the terminal attractor function and the novel Sigmoid function, an improved tracking differentiator (ITD) is designed. An improved fal (nsfal) function with better smoothness is constructed by using the properties of the Dirac δ function, and an ameliorative nonlinear state error feedback (ANLSEF) and a modified extended state observer (IESO) are designed based on the nsfal function. Furthermore, based on the thought of PID, the integral term of error is introduced into ANLSEF for the nonlinear operation to reduce the steady-state error of train speed tracking. In order to promote the robustness and control accuracy of the system, the feedforward compensation term and disturbance compensation term are combined to perform dynamic compensation for disturbances in real time. Finally, the simulation is carried out with CRH380A train data. The results indicate that compared with conventional ADRC and 2DOF-PID, FC-MADRC has the more vital anti-disturbance ability and higher tracking accuracy. FC-MADRC has the advantages of solid anti-disturbance, fast response, and high tracking accuracy. Under the premise of external disturbance, it can still achieve accurate speed tracking under different road conditions.

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A Fuzzy-Logic-System-Based Cooperative Control for the Multielectromagnets Suspension System of Maglev Trains With Experimental Verification

Cited by 35 publications

References 36 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

Synchronization Control with Dynamics Compensation for Three-Axis Parallel Motion Platform

Improved active disturbance rejection speed control for autonomous driving of high-speed train based on feedforward compensation

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