Modern railway trains raise higher demands on stability, operational efficiency, and comfort. To this end, distributed tracking control is vital to improve tracking performance and reduce the coupling force between traction units. This paper presents a novel adaptive cooperative tracking control approach for high-speed trains based on a multi-agent model. The proposed method ensures accurate tracking of both displacement and velocity, and effectively mitigates the coupling forces between adjacent traction units. Specifically, an improved multi-agent model of the high-speed train is developed, in which uncertain nonlinear resistance is approximated using an adaptive neural network, and the coupler connecting adjacent agents (traction units) is modeled as a spring-damper system. To formulate a refined controller, prior knowledge of the train system, such as the inherent resistance and coupling structure, is fully utilized so that only a few unknown parameters need to be estimated. Furthermore, an adaptive cooperative controller is designed to achieve accurate speed and displacement tracking. With this controller, multiple agents are simultaneously coordinated, while nonlinearities and uncertainties are handled. The coupling force between adjacent agents is effectively mitigated compared to traditional methods. Comparative simulation studies are conducted to demonstrate the effectiveness of the proposed adaptive cooperative tracking controller.

Railway turnouts are critical but weak devices that branch one track into two or more. To ensure the safety of train passage, the insufficient displacement (ID) of the switch rail must be controlled within the deviation required by the railway engineering. A reasonable tractive layout is the key to maintaining the ID of the switch rail at a relatively low level after its operation. Existing research generally formulates the design problem as a non-convex problem and cannot guarantee global convergence. To tackle this issue, this paper proposes a sparse learning-based design method for the tractive layout of the switch rail. Firstly, by defining the tractive force vector as the indicator variable, we transform the original combinatorial optimization problem into a continuous one and formulate a constrained convex problem by combining the$\boldsymbol {L}_{\mathbf {1}}$norm and the weighted squared ID. Then, an ADMM-based algorithm is developed to efficiently solve the force vector, thereby adaptively selecting the tractive layout. Finally, we validate the effectiveness of the proposed method using two typical turnouts in railway industry.

Optical satellite networks, supported by optical inter-satellite links (OISLs), provide reliable and low-latency optical connectivity. However, periodic and predictable sun outage events significantly compromise OISL availability, leading to frequent OISL interruptions and reduced network reliability. Existing routing algorithms often overlook the regularity of sun outage-induced interrupts and their differentiated impacts on services, resulting in degraded service performance. To address this challenge, this paper proposes a sun outage-enhanced time discretization OISL model and introduces a sun outage link-aware routing (SOLR) algorithm. By incorporating joint awareness of sun outage patterns and service requirements, SOLR employs an adaptive optimization mechanism to dynamically adjust routing decisions within temporal windows. Experimental results demonstrate that SOLR extends stable path durations by 39.9%, reduces interruption rates by 28.5%, and decreases blocking rates by 36.4%, significantly outperforming link-state-based routing algorithms. By effectively mitigating the impact of sun outages, SOLR ensures continuous optical service connections. This interruption-tolerant framework bridges network modeling and service provisioning, offering a robust solution for mission-critical service in optical satellite networks.

The rail surface defects directly impact the safety and efficiency of heavy-haul train operations. Timely assessment of these defects is crucial for informed maintenance decisions, with precise defect detection at its core. In recent years, the accumulation of extensive rail inspection images has led to the application of numerous computer vision-based methods for pixel-level detection of rail surface defects. However, given the constraint of a limited number of labeled defect samples, ensuring the generalization and robustness of existing methods remains challenging, particularly across varying track conditions and complex heavy-haul scenarios. Thus, this paper introduces a Segment-Anything-Model (SAM)-enhanced method for the detection of rail surface defects. First, a shadow-detection-based algorithm is developed to extract the rail regions and mitigate background interference. Then a student-teacher-Simi-network (S-T-Simi)-based unsupervised method is designed to generate prompt information for SAM. Utilizing this prompt information, we develop a task-specified SAM for precise rail defect detection. Finally, comprehensive validation is performed using inspection data collected from diverse heavy-haul tracks. Experimental results indicate that the proposed method achieves highly accurate segmentation of rail defects.

A novel MIMO data-driven integral predictive sliding mode control (DIPSMC) scheme is proposed based on the dynamic linearization (DL) and state observer method, intended for the automatic driving systems of multi-power unit high-speed trains (HSTs) influenced by system couplings, input constraints, and external disturbances. Initially, by introducing a nonlinear fast integral terminal sliding mode (NFITSM) surface instead of the traditional sliding mode function, facilitating rapid convergence of system errors and reducing sliding mode chattering. Additionally, a parameter update law and an adaptive extended state observer (AESO) are designed to estimate input gains and total uncertainties, respectively, addressing the issue in traditional discrete-time sliding mode control (DSMC) that requires large switching gains to handle disturbances. Subsequently, combining the rolling time domain optimization concepts in predictive control, it follows the reference trajectory of the predefined reaching law, allowing the system to explicitly handle control constraints and obtain higher tracking accuracy. This scheme concurrently accounts for and compensates the total uncertainties arising from system couplings, parameter estimation errors, and unknown disturbances. The principal advantage of this scheme is its design based entirely on a DL data model equivalent to the HST system, characterized by a low-order controller with robust chattering mitigation and disturbance rejection capabilities. Finally, comparative testing experiments of the proposed scheme are conducted on the CRH380A HST simulation platform. Experimental results indicate that under the proposed control scheme, the velocity and displacement error ranges for each power unit of the HST are within ±0.156 km/h and$\pm 1.~1$m, respectively, with control force and acceleration ranges of [−53.2 kN, 46.5 kN] and [−0.568 m/s2, 0.476 m/s2], respectively, and low chattering levels, fulfilling the efficiency and safety requirements of the trains.

Quantum entanglement enables quantum networks to provide end-to-end sharing of entangled particles, establishing multi-hop path-to-path connections between remote parties. Implementing entanglement distribution plays a vital role in increasing the network scale, and practical entanglement algorithms are required to provide end-to-end multi-hop quantum entanglement. We consider the real-time entanglement distribution (R-TED) and pre-established entanglement distribution (P-EED) to meet this requirement. Based on these two types of entanglement distribution, we propose two algorithms, i.e., R-TED-based routing and entangled pairs allocation (REA) algorithm as well as P-EED-based REA algorithm for end-to-end entanglement establishment, where the practical physical factors (e.g., finite storage capacity and limited storage time) are considered. The R-TED-based REA algorithm can orchestrate the nodes in a route and perform entanglement swapping by adopting real-time entanglement. For the P-EED-based REA algorithm, remote entangled particle sharing can be achieved via pre-shared entanglement distribution and hop-by-hop entanglement swapping. This way, the entanglement routing selection satisfies the storage time constraint and allows two far-apart nodes to share long-distance entangled particles with limited memory cells. We evaluate the performance of the proposed algorithms under different network topologies and sizes, based on which we demonstrate that the network size can significantly affect the efficiency advantage achieved by the P-EED-based approach over the R-TED-based approach.

In recent years, research on satellite networks has gained significant attention, with their capability for seamless global coverage and meeting real-time communication demands serving as a key solution to address deficiencies in ground communication network coverage and to improve the real-time transmission of services. Traditional satellite networks, originally employed for singular purposes such as data relay, are gradually transitioning to satellite internet to support various Internet-based services. The integration of satellites with ground networks, known as the Satellite-Terrestrial Integrated Network (STIN), has become an inevitable trend, making the deployment of ground stations (GSs) a critical issue in the STIN construction. Traditional GS deployment strategies are insufficient to meet the real-time demands of emerging services. In this context, a GS deployment strategy based on the data center-user gravity model (GSD-DG) is proposed, where the influence of all data center factors on GS deployment is considered. This approach takes into account constraints such as satellite connectivity, user traffic, and data center gravity. The integration of GS with data centers plays a pivotal role in enhancing the internet service latency performance. Simulation results indicate that the proposed strategy significantly reduces service latency by 24.5% compared to the benchmark, providing a more effective GS deployment solution to further optimize the STIN service latency performance.

The diagnosis of railway system faults is significant for its comfort, efficiency, and safety. The rail profile faults are the most direct impact factors when considering the health conditions of rails. This paper puts forward rail fault diagnosis from two perspectives: quantifying the level of complexity and chaos of different profiles, and measuring the level of correlation between different profiles, which correspond to the newly proposed refined dispersion entropy (RDE) method and the correlation plane method, respectively. The RDE uses weighted-dispersion patterns to extract accurate time domain features from rail profile data, and the correlation plane can characterize nonlinear and non-monotonic relationships between analyzing subjects, which are the main contributions of this study. Experimental results with simulated and reality-based data show that the proposed methods can identify faulty profile data and discriminate different types of profile faults more effectively when compared with existing methods.

The energy-efficient train control (EETC) problem is investigated in this article. And a soft actor-critic (SAC)-based method is proposed to optimize the train driving strategy. First, EETC problem is converted to the inverse problem, i.e., minimizing the trip time of the journey with constant energy consumption. Based on the conversion, the EETC problem is reformulated as a finite Markov decision process, which can be solved by deep reinforcement learning algorithms. Second, an optimization method based on the SAC method is designed to calculate the optimal driving strategy of the train with introducing the reservoir sampling method. Finally, some case studies are conducted to verify the effectiveness and performance of the proposed method. Simulation results demonstrate that a good energy-saving performance can be achieved. In single interval, the SAC-based method can reduce about 1.65% of the energy consumption compared with numerical method. And the energy consumption reduction can be extended to be 6.49% when the proposed approach is applied in multiple intervals.

Railway turnout is the key infrastructure for trains to change their routes. In order to ensure the smoothness and safety of the train’s passing through turnouts, the insufficient displacement (ID) of switch rails after their conversions must be controlled within a permitted range. During design stage, it is quite important for the reduction of the ID to reasonably arrange the tractive points. In the existing literature, a feasible tractive layout is commonly suggested through the manual analysis using the finite element model of the switch rail. However, as the design space may explode for long rails with multiple tractive points, it is time-consuming for such labor-intensive methods to search a feasible tractive layout, and the result may be non-optimal. Therefore, it is necessary to develop an efficient method to arrange the tractive points optimally in order to minimize the ID. To this end, we propose a physics-informed optimization method for the design of the tractive layout. First, a tailored direct stiffness method is introduced to accurately estimate the ID given any tractive layout and frictions. On this basis, we establish an optimization model for the selection of the tractive locations with the objective of minimizing the expectation of the ID. To address the sparsity issue of the decision variable, an Encoding Rule with a hierarchical indexing method is proposed to improve the efficiency of genetic algorithm. Next, the number of tractive points is determined. Finally, several sets of experiments are conducted to demonstrate the effectiveness of the proposed method, which decreases the ID by 25.61% and 12.7% in terms of the maximal and mean values for the case with the switch of length 44.1m.

This paper investigates the disturbance observer based robust cruise control problem for the heavy haul train with input saturation and disturbances. Both uncertain parameters and actuator saturation are taken into account in the dynamic model of the heavy haul train. To reject the influence of disturbances from the input channel, a linear disturbance observer is proposed to approximate the unknown disturbance, and an augmented system is constructed by combining the train state and the disturbance estimation error. According to the Lyapunov stability analysis method and the guaranteed cost control theory, a sufficient condition for the existence of the composite state-feedback control law and the disturbance observer parameter matrix are obtained, the coupler deviation and the disturbance estimation error are stable at the equilibrium point, and meanwhile the minimization of a given train performance index is ensured. Numerical experiments are provided to illustrate the effectiveness of the proposed approach.

Contactless fault diagnosis is one of the most important technique for fault identification of equipment. Based on the idea of contactless fault diagnosis, this paper presents a sound-based diagnosis method for railway point machines (RPMs). First, the sound signals are preprocessed using empirical mode decomposition (EMD). Entropy, time-domain and frequency-domain statistical parameters of the first 15 intrinsic mode functions (IMFs) are then extracted. Second, a two-stage feature selection strategy blending Filter method and Wrapper method is proposed, which can significantly reduce the dimension of features and select the optimal features. The superiority and effectiveness of the proposed feature selection strategy are verified by comparing with other feature selection methods. Third, a weighted majority voting (WMV)-based ensemble classifier optimized using particle swarm optimization (PSO) is developed and compared with single classifiers. And the ensemble patterns are discussed to select the most optimal ensemble pattern. The average diagnosis accuracies of 10 repeated trails of reverse-normal and normal-reverse switching processes reach 99% and 99.93%, respectively, which indicates the effectiveness and feasibility of the proposed method.

This paper proposes a trajectory optimization approach for high-speed trains to reduce traction energy consumption and increase riding comfort. Besides, the proposed approach can also achieve energy-saving effects by optimizing the operation time between stations. First, an optimization model is developed by defining the objective function as a trade-off function of the traction energy consumption and riding comfort. In addition to constraints in the classic optimal train control model, three new factors–the discrete throttle settings, neutral zones, and sectionalized tunnel resistance–are considered. Then, the model is discretized and turned into a multi-step decision optimization problem. All the nonlinear constraints are approximated using piecewise affine (PWA) functions, and the trajectory optimization problem is turned into a mixed integer linear programming (MILP) problem which can be solved by existing solvers CPLEX and YALMIP. Finally, some case studies with real-world data sets are conducted to present the effectiveness of the proposed approach. The simulation results are compared with the practical running data of trains, which shows that the proposed model and the optimization approach save energy and improve the riding comfort.

Railway Automatic Switch (RAS) plays a crucial role in Turnout Switching System (TSS). The size of RAS’s Stationary Contacts (SCs) directly affects connectivity of the pivotal control and feedback circuit, which further influences the remaining useful life of TSS. However, it is impossible to avoid normal wear and tear or fractures of SC during daily operation, resulting in size change of SCs. Therefore, it is vital to monitor the size of SCs. However, due to lack of wear samples, it is hard to design automatic algorithms for this task, especially for developing currently popular deep learning. To this end, this paper proposes a computer vision method forrailway automatic switch stationary contacts wear detection under few-shot occasions.Our method includes two key modules: a Few Shot SC DETection (FSDet) module and a Contour-based Size MEAsurement (CSMea) module, which together form a system that achieves accurate SC detection and size monitoring. The FSDet module formulates a multi-template deep feature matching pipeline, which plays the role of detecting all SCs in an image under the few shot manner. Then, the CSMea module takes the above detected SC patches as input and detects wear regions utilizing contour features and key point features. Finally, size of SCs can be calculated in image level by computing average pixels distance in wear regions and rescaled into real world level using image calibration tools. Experimental results demonstrate that the proposed method can accurately and robustly detect and measure the size of different SC structures in few-shot occasions.

Learning and identifying key concepts from past fault records are essential for us to understand the causes of these faults, which lay the foundation for the fault diagnosis and prognosis. At present, faults in many fields (e.g., rail, automobile, and smart grid) are recorded in textual form. Due to the lack of effective mining and analysis tools, latent information in the massive textual data (text records) has not been fully unearthed. In this paper, a novel Adversarial Training-based Lattice LSTM model called AT-Lattice is proposed to address this problem. In this model, the Named Entity Recognition (NER) is achieved by Lattice LSTM and Conditional Random Field (CRF), where the Lattice LSTM is used to provide sequence information between words, and the CRF is used to get the final entity prediction result. In addition, the Chinese Word Segmentation (CWS) task is introduced to conduct the adversarial training with the NER task. The framework of the adversarial training is able to make full use of the boundary information and filter out the noise caused by the introduced CWS task. More importantly, extensive experiments are conducted on five different train fault datasets collected by a rail transit company. The results demonstrate that the proposed model outperforms the state-of-the-art baselines.

For enhancing the operation efficiency of the fully automatic operation (FAO) system in the urban rail transit (URT), this paper investigates the robust dynamic train regulation problem with respect to frequent disruptions and imperfect wireless transmissions. To better express the characteristic of the arriving passengers, the fuzzy passenger arrival rate is adopted to address the uncertainty of the passenger flow, and a T-S fuzzy state-space model is established to express the periodical movement of the train traffic in an URT loop line. By considering the possible packet dropout phenomenon during the wireless data transmissions, which may lead to the instability of the train traffic system and degrade the performance of the regulation strategy, a robust real-time train regulation strategy is developed based on the fuzzy predictive control theory, which distinguishes existing studies in that the uncertainty dropout rate is contemplated to address the complexity of the actual operation environment. A sufficient condition for the proposed control law is presented to guarantee that the nominal train schedule is recovered from disturbed situations with a given attenuation level by means of the$H_{\infty }$performance index, and meanwhile the optimization of the upper bound on the objective function balancing the service efficiency and control cost is achieved. Numerical simulations based on the Beijing subway loop line 2 are presented for demonstration of the effectiveness of the introduced strategy.

With the increasing demand for rail transit, wireless communication technologies are playing a growing significant role in train control systems, which enables the railway systems to provide a higher capacity and more efficient services. However, due to the nature of radio frequency propagation, the quality of the train-to-ground wireless connections is highly dependent on a well-planned deployment of the wayside access points. To improve both the accuracy and the efficiency in railway network planning, in this paper, a deep learning technology is exploited to model the wireless propagation, which was very difficult to deterministically predict at a fast speed in our previous research due to the high computation demanding. In this proposed wireless propagation model, Kalman filter is utilized to update the neural network parameters online, which makes this model can meet the variation of the environment. The numeric evaluation result shows that the deep neural network based wireless channel model can precisely predict the outage probability with a very low computational cost.

Quantum key distribution (QKD) has demonstrated a great potential to provide future-proofed security, especially for 5G and beyond communications. As the critical infrastructure for 5G and beyond communications, optical networks can offer a cost-effective solution to QKD deployment utilizing the existing fiber resources. In particular, measurement-device-independent QKD shows its ability to extend the secure distance with the aid of an untrusted relay. Compared to the trusted relay, the untrusted relay has obviously better security, since it does not rely on any assumption on measurement and even allows to be accessed by an eavesdropper. However, it cannot extend QKD to an arbitrary distance like the trusted relay, such that it is expected to be combined with the trusted relay for large-scale QKD deployment. In this work, we study the hybrid trusted/untrusted relay based QKD deployment over optical backbone networks and focus on cost optimization during the deployment phase. A new network architecture of hybrid trusted/untrusted relay based QKD over optical backbone networks is described, where the node structures of the trusted relay and untrusted relay are elaborated. The corresponding network, cost, and security models are formulated. To optimize the deployment cost, an integer linear programming model and a heuristic algorithm are designed. Numerical simulations verify that the cost-optimized design can significantly outperform the benchmark algorithm in terms of deployment cost and security level. Up to 25% cost saving can be achieved by deploying QKD with the hybrid trusted/untrusted relay scheme while keeping much higher security level relative to the conventional point-to-point QKD protocols that are only with the trusted relays.

Energy-efficient train operation is regarded as an effective way to reduce the operational cost and carbon emissions in metro systems. Reduction of the traction energy and increasing of the regenerative energy are two important ways for saving energy, which is closely related to the train timetable and driving strategy. To minimize the systematic net energy consumption, i.e., the difference between the traction energy consumption and the reused regenerative energy, this paper proposes an integrated train operation approach by jointly optimizing the train timetable and driving strategy. A precise train driving strategy is presented and the timetable model considers the headway between successive trains, the distribution of the trip time, and passenger demand in this paper. In addition, a distributed regenerative braking energy model is proposed, based on which the integrated optimization model is formulated. Then, a two-level approach is proposed to solve the problem. At the driving strategy level, the train control problem is transferred into a multi-step decision problem and the Dynamic Programming method is introduced to calculate the energy-efficient driving strategy with the given trip time. As for the timetable level, the trip times and headway of trains are optimized by using the Simulated Annealing algorithm based on the results of dynamic programming method. The timetable optimization level balances the mechanical traction energy of multi-interstations and the amount of the reused regenerative energy such that the net mechanical energy consumption of the metro system is minimized. Furthermore, two numerical examples are conducted for train operations in the peak and off-peak hours separately based on the real-world data of a metro line. The simulation results illustrate that the proposed approach can produce a good performance on energy-saving.

Quantum key distribution (QKD) networks are potential to be widely deployed in the immediate future to provide long-term security for data communications. Given the high price and complexity, multi-tenancy has become a cost-effective pattern for QKD network operations. In this work, we concentrate on addressing the online multi-tenant provisioning (On-MTP) problem for QKD networks, where multiple tenant requests (TRs) arrive dynamically. On-MTP involves scheduling multiple TRs and assigning non-reusable secret keys derived from a QKD network to multiple TRs, where each TR can be regarded as a high-security-demand organization with the dedicated secret-key demand. The quantum key pools (QKPs) are constructed over QKD network infrastructure to improve management efficiency for secret keys. We model the secret-key resources for QKPs and the secret-key demands of TRs using distinct images. To realize efficient On-MTP, we perform a comparative study of heuristics and reinforcement learning (RL) based On-MTP solutions, where three heuristics (i.e., random, fit, and best-fit based On-MTP algorithms) are presented and a RL framework is introduced to realize automatic training of an On-MTP algorithm. The comparative results indicate that with sufficient training iterations the RL-based On-MTP algorithm significantly outperforms the presented heuristics in terms of tenant-request blocking probability and secret-key resource utilization.

Optimization of island microgrids should configure the module type and size in such a way that multiple objectives can be balanced. This paper presents a bioinspired optimization approach of microgrid sizing, with two salient features. First, the multiple objectives are categorized into four types: reliability, economy, renewable technology, and pollution. We present a triangular aggregation model, which is straightforward and cost effective to compute the fitness. Second, a bioinspired algorithm named Levy-Harmony is developed. We embed the Levy flight into the Harmony vector updating to enhance the global searching ability and, meantime, adopt a bias factor to avoid unnecessary exploration. The searching speed and accuracy are well balanced and improved. The real datasets are used for comparative studies, demonstrating the superiority of the proposed scheme against typical existing approaches.