Adhesion Characteristic Slope Estimation for Wheel Slip Control Purpose Based on UKF

Pichlík, Petr; Bauer, Jan

doi:10.1109/tvt.2021.3072484

Cited by 8 publications

(6 citation statements)

References 22 publications

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“…With the rapid development of electric traction control, several adhesion utilization control algorithms have been proposed to improve the adhesion utilization performance of railway vehicle in recent years, such as re-adhesion control [7,8] and slip control [6,[9][10][11][12]. The former limits the slip velocity to an acceptable value after the slip phenomenon has already occurred, while the latter controls the slip velocity in a proper range around the maximum value of the adhesion-slip characteristic.…”

Section: Introductionmentioning

confidence: 99%

“…The data-driven identification methods typically use data-based models, such as empirical formula model [14], artificial neural network model [15], Kalman filter-based identification model [9], or machine learning-based model [16], to identify the adhesion characteristic. However, the accuracy of data-driven models depended on the coverage of the training data samples.…”

Section: Introductionmentioning

confidence: 99%

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Research on the Identification of Nonlinear Wheel–Rail Adhesion Characteristics Model Parameters in Electric Traction System Based on the Improved TLBO Algorithm

Gan,

Zhao,

Wei

et al. 2024

Electronics

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The wheel–rail adhesion is one of the key factors limiting the traction performance of railway vehicles. To meet the adhesion optimization needs and rapidly obtain wheel–rail creep characteristics under specific operating conditions, an engineering identification method for wheel–rail adhesion characteristics based on a nonlinear model is proposed. The proposed method, built upon the traditional Teaching-Learning-Based Optimization (TLBO) algorithm, has been adapted to the specific nature of nonlinear wheel–rail adhesion model parameters identification, enhancing both the search speed in the early stages and the search accuracy in the later stages of the algorithm. The proposed identification algorithm is validated using experimental data from the South African 22E dual-flow locomotive. The validation results demonstrate that the proposed identification algorithm can obtain a nonlinear wheel–rail adhesion characteristics model with an average adhesion coefficient error of around 0.01 within 50 iteration steps. These validation results indicate promising prospects for the engineering practice of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on the Identification of Nonlinear Wheel–Rail Adhesion Characteristics Model Parameters in Electric Traction System Based on the Improved TLBO Algorithm

Gan,

Zhao,

Wei

et al. 2024

Electronics

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“…The performance of traction/braking controls in rail transit trains depends on the behavior of the wheel-rail contact, which is affected by the state of the rail surface [1], [2], [3]. Accurately identifying the rail surface state is crucial in order to ensure high-performance train control [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Improved Metric-Learning-Based Recognition Method for Rail Surface State With Small-Sample Data

Yu,

Peng,

Liu

et al. 2024

IEEE Access

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Accurately identifying the rail surface state is crucial for enhancing train traction and braking capabilities, as well as ensuring safe operation and maintenance. Few-shot learning is commonly utilized to recognize the rail surface state, effectively resolving the overfitting issue caused by limited sample data. However, when it comes to the actual situation of rail surface state data, few-shot learning faces challenges such as insufficient extraction of crucial feature information and a tendency to lose distinguishing degree information. To address the aforementioned issues, a rail surface state recognition model based on improved metric learning is proposed in this paper. The proposed method incorporates a pyramid-splitting attention mechanism in the feature extraction network. This enables multi-scale spatial information to be extracted from the feature map, while also facilitating cross-dimensional channel attention and interaction between spatial attention features. This addresses the issue of inadequate key feature information extraction caused by a limited number of orbital surface state samples. A deep local description concatenator splices the local features of the query set and various support set feature maps in pairs, replacing the global feature splicing in traditional metric learning. This enables the filtering of interference information such as background, while retaining feature information with significant differentiation to a larger extent. The proposed method was evaluated using a small-sample rail surface state dataset that we built ourselves. According to the experimental results, the proposed method surpasses the existing methods in recognition accuracy, precision, and recall.

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“…Steepest gradient method or Proportional-Integral (PI) controllers aimed to keep the adhesion slope equal to zero are commonly used for this purpose. [15]- [20]. 2) Regulation of the slip velocity using an additional speed controller [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

Torsional Vibration Suppression in Railway Traction Drives

et al. 2022

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Torsional vibrations phenomena are self-excited vibrations that occur in the wheelset of railway powertrains due to the counter-phase oscillation of both wheels. Long-lasting events of this type may lead to the catastrophic failures. Therefore, torsional vibration suppression and mitigation methods have drawn significant attention from the railway industry in the recent few years. Conventional vibration suppression methods reduce motor torque once the oscillation is detected. However, this can result in trip delays. Design of methods which do not compromise the traction capability is challenging. This paper proposes a novel torsional vibration suppression method using a Proportional-Resonant (PR) controller. The proposed method is insensitive to mechanical drive-train parameter variation neither requires adding new sensors to the wheelset. The method requires previous knowledge of the natural frequency of the wheelset torsional mode but this significantly reduces the implementation complexity suffered by other anti-vibration methods. Furthermore, the method will be shown to provide reduced sensitivity to slip velocities and wheelrail conditions.INDEX TERMS Railway traction drives, torsional vibrations, slip-stick phenomenon, slip control, fieldoriented control, proportional-resonant controller.

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Adhesion Characteristic Slope Estimation for Wheel Slip Control Purpose Based on UKF

Cited by 8 publications

References 22 publications

Research on the Identification of Nonlinear Wheel–Rail Adhesion Characteristics Model Parameters in Electric Traction System Based on the Improved TLBO Algorithm

Research on the Identification of Nonlinear Wheel–Rail Adhesion Characteristics Model Parameters in Electric Traction System Based on the Improved TLBO Algorithm

Improved Metric-Learning-Based Recognition Method for Rail Surface State With Small-Sample Data

Torsional Vibration Suppression in Railway Traction Drives

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