Optimization of Urban Rail Automatic Train Operation System Based on RBF Neural Network Adaptive Terminal Sliding Mode Fault Tolerant Control

Yang, Junxia; Zhang, Youpeng; Jin, Yuxiang

doi:10.3390/asi4030051

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…MAO et al 13 proposed a new adaptive fault-tolerant sliding mode control scheme considering disturbance and actuator failure. Although sliding mode control [14][15][16][17][18] has a good tracking effect, it can easily produce a chattering phenomenon. Although some correction methods, such as the saturation function method, can effectively reduce the chattering phenomenon, they will sacrifice the advantage of outstanding anti-disturbance performance.…”

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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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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“…There are few works describing the applications of artificial intelligence methods in Fault-Tolerant control systems. The works in the literature describe only the use of shallow neural networks, mainly perceptron networks, for IM [23,24]. This approach does not require a priori knowledge of the facility and provides very good results, which is its great advantage.…”

Section: Introductionmentioning

confidence: 99%

“…This approach does not require a priori knowledge of the facility and provides very good results, which is its great advantage. Work [23] presents a passive FTC system based on RBF neural networks. The passive controller adaptively compensates for external disturbances.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of Current Sensor Fault Detection Mechanisms for PMSM Drives Based on Neural Networks

Jankowska

Dybkowski

2022

Designs

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This paper describes a vector-controlled Permanent Magnet Synchronous Motor (PMSM) drive system with the current sensor fault detection mechanism. In general, the control structure is based on the well-known Field Oriented Control (FOC) algorithm. The structure is equipped with an additional algorithm for current sensor fault detection based on a neural network. The presented control structure is able to detect typical current sensor faults, such as lack of signal, intermittent signal, variable gain and signal noise. The application of the NN detector guarantees a faster detection of the sensor fault than classical detectors based on algorithmic methods or logical systems. This work focuses on presenting the methodology of designing detectors and their analysis, based solely on simulation analysis. The simulation results, conducted in the Matlab/Simulink environment, are presented for the above-mentioned faults in phase A and phase B for different speed conditions.

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“…In the past few years, many train operation control methods have been studied, most of which have focused on single high-speed trains. These have included but are not limited to fault-tolerant control [2], neural network control [3], sliding mode control [4], fuzzy control [5], and robust control [6]. In [7], a self-organizing radial basis function neural network (RBFNN) was adopted to approximate the nonlinear factors of the train, and the non-singular terminal sliding mode control strategy was designed to control the train.…”

Section: Introductionmentioning

confidence: 99%

Distributed Multiple High-Speed Trains Consensus Control Based on Event-Triggered Mechanism

Zhang

Liu

2022

Symmetry

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This paper studies the consensus control problem of multiple high-speed trains with nonlinear running resistance based on a distributed event-triggered control protocol. Firstly, the multiple high-speed trains are established as a multi-agent model with a leader–follower. For the system whose leader has an unknown input, the effective state observers are introduced to observe the state of the train. Then, an observer-based event-triggered strategy is designed to determine the time when the train state is transmitted to the local controller and adjacent trains. The proposed control scheme is based on local information, and the solutions of the state observer and the gain symmetric matrices parameters of the distributed event-triggered control strategy are constructed in the form of linear matrix inequalities (LMI). Through the Lyapunov stability theory, sufficient conditions for the stable and safe operation of multiple high-speed trains systems are obtained. Finally, a simulation example is given to demonstrate the effectiveness of the proposed method.

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Optimization of Urban Rail Automatic Train Operation System Based on RBF Neural Network Adaptive Terminal Sliding Mode Fault Tolerant Control

Cited by 6 publications

References 21 publications

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

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

Design and Analysis of Current Sensor Fault Detection Mechanisms for PMSM Drives Based on Neural Networks

Distributed Multiple High-Speed Trains Consensus Control Based on Event-Triggered Mechanism

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