Targeting to improve the time synchronization accuracy of multi-hop Time-Sensitive Wireless Sensor Networks (TS-WSNs) for mission-critical industrial automation applications, a Minimum Variance Time Synchronization (MVTS) algorithm utilizing the concept of Packet-Coupled Oscillators (PkCOs) is proposed. This MVTS algorithm utlizes an output feedback approach to mitigate the impact of communication noise on the accumulation of synchronization errors. In addition, a Time-Division Multiple Access (TDMA) packet-exchange superframe is introduced to achieve efficient and low-overhead time synchronization. The optimal gain matrix of the MVTS algorithm is obtained by the Linear Matrix Inequality (LMI) optimization with theoretic analysis. The proposed MVTS algorithm is evaluated by both simulation and experiments on an IEEE 802.15.4 hardware testbed. The experimental results show that the proposed algorithm can effectively reduce the growth rate of clock offset along multi-hop nodes and improve the time synchronization accuracy of the TS-WSNs. Note to Practitioners—This paper explores a method to achieve precise time synchronization in TS-WSNs. The primary challenge being addressed is the accumulation of synchronization errors that occur in multi-hop TS-WSNs, which can compromise the accuracy of time synchronization. To mitigate this challenge while taking communication overhead into account, we propose a solution that combines a TDMA-based packet-exchange superframe structure with the MVTS algorithm. This approach introduces an output feedback consensus control scheme to minimize synchronization error variance. The optimal gain matrix for this consensus control scheme is derived through LMI optimization. The algorithm is implemented on an IEEE 802.15.4-compatable wireless node SAM R21 by Microchip and the experimental results of a 10-hop netowrk shows that the maximum synchronization error is$8.32\mu s$, reduced by 56% and 32%, repsectivly, compared to the baseline method FTSP and the recent PISync.

In this paper, we aim to achieve robust and efficient train rescheduling of intercity express railway lines considering periodic train timetables and passenger uncertainties. Considering intercity express railways’ features of longer section lengths, multiple trains running in close succession within long sections and the varying passenger demands, we improve the multi-train state space model of intercity express railway operation, in which the changes and uncertainties of passenger flows are modeled as system parameter uncertainties and the primary train delays caused by temporary speed restriction extreme weather, and signal failure, etc. are modeled as external interference. Then, a real-time train rescheduling controller is developed that combines iterative learning and model predictive control to enhance its resistance against both the model uncertainties (i.e., varying passenger flows) and the external interference (i.e., the primary delays). The objective function of the rescheduling controller is to recover from delays while preventing the control force amplitude from becoming excessively large. Each period of the periodic timetable is modeled as a batch, and a batch-based state space error predictive model is developed to simultaneously recover the nominal timetable and minimize the control force amplitude. It is proven that the error norm will eventually converge to a bounded value as the number of iterations increases. The performance of the proposed method is evaluated through simulations based on the Beijing–Tianjin intercity express rail line.

In railway transportation systems, train section running times are usually uncertain due to the influence of various complex factors, making rescheduled timetables difficult to use continually, and rescheduling has to be repeated. When acceptable, rescheduled timetables should have a certain degree of robustness against such uncertainty. We use the empirical statistical distribution of the deviation between actual and scheduled train arrival times to capture this uncertainty. To equip timetables with acceptable robustness, a chance-constrained programming model is established for the robust rescheduling problem. We design a two-stage hybrid heuristic algorithm to solve the proposed model. In the first stage, a hybrid intelligent algorithm combining ant colony optimization and Monte Carlo simulations is used to solve the model initially, and bisection method is embedded into it to improve solving efficiency. Then, the chance-constrained programming model is transformed into its deterministic equivalent form to generate a better solution by utilizing the solution result of the hybrid intelligent algorithm. Finally, we test our method on the realistic dataset of Elizabeth Line to verify its effectiveness and robustness. Compared to the deterministic rescheduling method, our algorithm, with a confidence level of 0.6-0.7, reduces the number of rescheduling times by 22.2%-100% while only increasing delays by 0.5%-3.7%. The experimental results demonstrate that we propose an effective and adjustable method to enhance the robustness of timetables, allowing operators to formulate an ideal timetable with acceptable robustness while minimizing adverse effects on punctuality.

Although transformer-based methods have achieved great success in multi-scale temporal pattern interaction modeling, two key challenges limit their further development: (1) Individual time points contain less semantic information, and leveraging attention to model pair-wise interactions may cause the information utilization bottleneck. (2) Multiple inherent temporal variations (e.g., rising, falling, and fluctuating) entangled in temporal patterns. To this end, we propose Adaptive Multi-Scale Hypergraph Transformer (Ada-MSHyper) for time series forecasting. Specifically, an adaptive hypergraph learning module is designed to provide foundations for modeling group-wise interactions, then a multi-scale interaction module is introduced to promote more comprehensive pattern interactions at different scales. In addition, a node and hyperedge constraint mechanism is introduced to cluster nodes with similar semantic information and differentiate the temporal variations within each scales. Extensive experiments on 11 real-world datasets demonstrate that Ada-MSHyper achieves state-of-the-art performance, reducing prediction errors by an average of 4.56%, 10.38%, and 4.97% in MSE for long-range, short-range, and ultra-long-range time series forecasting, respectively. Code is available at https://github.com/shangzongjiang/Ada-MSHyper.

In High-Speed Railway (HSR) networks with hub stations connecting multiple HSR lines, Train Timetable Rescheduling (TTR) under disruptions (such as speed restrictions caused by high wind) has been a challenging problem, which requires collaborative consideration of the traffic and impacts on all lines. Compared to the first principle model of complex railway networks, data-driven modeling provides a better solution to describe how the performance of one HSR line is affected by a train rescheduling decision made for another lines, but it faces the challenges of incompleteness, imbalance and lack of comprehensiveness of history data as disruptions in railways (e.g. delays, accidents) are relatively rare compared to normal operations. This paper proposes a multi-line rescheduling framework consisting of an interactive railway operation simulation and experiment (iROSE) system, a surrogate model and a heuristic algorithm to enable network-wise optimal rescheduling of multiple lines. To compensate for the limits of incomplete history data, a relatively low-cost but accurate enough surrogate model is developed from simulation data of the realistic but computation-intensive iROSE simulator. To reduce the demand for data and the time on running the costly simulator, a multi-surrogate search method is developed. A data expansion-based knowledge transfer method and joint distribution adaptation and tradaboost are also adopted to further improve the accuracy of the surrogate model. Our extensive experiments show that the proposed method can obtain higher precision fine search models with few simulations and solve the problem of TTR under wind-caused speed restrictions in complex railway networks with multiple lines.Note to Practitioners—This paper was motivated by the Train Timetable Rescheduling problem of complex high-speed railway networks of multiple lines connected via hub stations, in which the delays caused by high-wind speed restrictions on one line may easily affect trains on other lines in the network. Thus the impacts of a local-line TTR decision on other parts of the HSR network should be evaluated appropriately in the sense of precision and real-time, to assist the local dispatcher in making a network-wise decision. However, the incomplete and imbalanced historical data may not accurately capture how the system behaves during disruptions. In order to address these challenges, this paper proposes a data-driven rescheduling optimization framework to allow network-wise optimal decision-making. The proposed framework consists of an on-demand iROSE system, a surrogate model representing the operation performance of the whole railway network, and a heuristic method responsible for the traffic rescheduling of partial HSR lines. The realistic iROSE simulator is able to compensate the imbalanced actual history operation data by giving a precise evaluation of the network’s performance. Then a multi-surrogate search method and a knowledge transfer method are developed to avoid the time-consuming caused by expensive simulation. The developed surrogate model is able to capture the insights of delay propagation in a multi-line HSR network and enable the dispatchers to have a quick and comprehensive evaluation of how a TTR rescheduling decision made for one line affects other lines in the network. As a result, a network-wise better decision on train rescheduling can be made.

This article investigates the detection and identification of physical faults in devices and false-data-injection attacks in communication networks for multi-agent systems with event-triggered transmission mechanisms and subject to external periodic disturbances. First, a new detection and identification scheme, including a local disturbance decoupling (LDD) observer and a distributed disturbance decoupling (DDD) observer, is proposed. Then, based on zero-assignment and the rank-deficiency of the transfer function matrix at zeros, a co-design method for the LDD observer and DDD observer is proposed, which enables the decoupling of periodic disturbances from the residuals for detection and identification. This new scheme no longer requires the transmission of control signals from the node being monitored or the exchange of information between its neighbors, significantly reducing the communication overhead and enhancing the system's security. Finally, a simulation based on a multi-two-wheeled trolley system is used to verify the effectiveness of the proposed method.

In intercity high-speed railways (HSR) with high speeds and dense traffic, fast decision-making in timetable rescheduling is critical to minimize delays and maintain regular services during disruptions. Different from traditional mathematical programming methods, which are often too computation-intensive for real-time implementation, this paper develops a state-space dynamic model of train traffic with the extension to accommodate multiple trains in the sections between stations. Variations of the state space model are established for scenarios of mild delays and severe delays, respectively. Two automatic rescheduling state feedback controllers are designed to achieve two objectives, to restore the nominal timetable in case of mild delays and to retain regular departure intervals in case of severe delays, respectively. Stability analysis theoretically proves the stability and convergence of the proposed feedback controller and real-time rescheduling algorithm. The proposed rescheduling method indeed is a real-time state feedback controller, and the simulation results of the Beijing-Tianjin intercity HSR show that the proposed method features negligible computation times in the order of microseconds, in contrast to the 56s and 65s required by conventional Mixed-Integer Programming (MIP) for nominal timetable recovery and regular departure interval problems, respectively. The proposed state-space feedback control rescheduling method is quasi-optimal compared to MIP with the added advantage of greater computational efficiency and fast decision-making.

Train Timetable Rescheduling (TTR) is a crucial task in the daily operation of high-speed railways to maintain punctuality and efficiency in the presence of unexpected disturbances. However, it is challenging to promptly create a rescheduled timetable in real time. In this study, we propose a reinforcement-learning-based method for real-time rescheduling of high-speed trains. The key innovation of the proposed method is to learn a well-generalized dispatching policy from a large amount of samples, which can be applied to the TTR task directly. At first, the problem is transformed into a multi-stage decision process, and the decision agent is designed to predict dispatching rules. To enhance the training efficiency, we generate a small yet good-quality action set to reduce invalid explorations. Besides, we propose an action sampling strategy for action selection, which implements forward planning with consideration of evaluation uncertainty, thus improving search efficiency. Extensive experimental results demonstrate the effectiveness and competitiveness of the proposed method. It has been proven that the local policies trained by the proposed method can be applied to numerous problem instances directly, rendering it unnecessary to use human-designed rules.

Train timetable rescheduling (TTR) aims to promptly restore the original operation of trains after unexpected disturbances or disruptions. Currently, this work is still done manually by train dispatchers, which is challenging to maintain performance under various problem instances. To mitigate this issue, this study proposes a reinforcement learning-based approach to TTR, which makes the following contributions compared to existing work. First, we design a simple directed graph to represent the TTR problem, enabling the automatic extraction of informative states through graph neural networks. Second, we reformulate the construction process of TTR’s solution, not only decoupling the decision model from the problem size but also ensuring the generated scheme’s feasibility. Third, we design a learning curriculum for our model to handle the scenarios with different levels of delay. Finally, a simple local search method is proposed to assist the learned decision model, which can significantly improve solution quality with little additional computation cost, further enhancing the practical value of our method. Extensive experimental results demonstrate the effectiveness of our method. The learned decision model can achieve better performance for various problems with varying degrees of train delay and different scales when compared to handcrafted rules and state-of-the-art solvers.

To enhance the reliability of digital automation systems in the Industry 4.0 era, this paper investigates a robust fault-tolerant control scheme in the discrete-time domain subject to periodic disturbances, consisting of a fault estimator, dynamic disturbance compensation loop, and fault-tolerant controller. The fault estimator simultaneously estimates both the system states and actuator/sensor faults. The existence and stability conditions of the proposed estimator are given, and a robust design method is proposed to make the state estimates robust to disturbances. To further reduce the estimation errors caused by periodic disturbances, a novel disturbance compensation loop is introduced and is optimized by a joint zero-assignment and pole-optimization method to delicately compensate for the adverse impacts of periodic input disturbances. The proposed robust fault-tolerant controller uses fault estimation to ensure fast recovery in the event of bounded actuator/sensor faults. The proposed scheme is evaluated through simulations of a two-wheeled mobile robot subject to periodic disturbances and simultaneous abrupt inclination angular sensor and ramp actuator faults, where its performance is shown to exceed that of existing methods.

Railway system performances depend on effective dynamic scheduling and train operation control. The fast expansion and increasing complexity of high-speed railway (HSR) networks raise new challenges in maintaining the punctuality and efficiency in daily operations, in particular, in the event of disruption. This paper aims to review the state-of-art in dynamic traffic scheduling, trains operation control, and their integration for safer, more punctuate, efficient, and resilient HSRs, whose origins may trace back to their counterparts in traditional railways. First, the existing two-tier hierarchy of scheduling and control in HSR’s daily operation is introduced. At the higher layer of scheduling, a general model of dynamic train scheduling is discussed, followed by reviewing the scheduling methodologies. At the lower layer of train operation control, recent progress in tracking control of high-speed trains is discussed, with focus on the latest advances in single train control and cooperative control for multiple trains. Then, as the trend of technological progress for future HSRs, the recent development of integrating dynamic scheduling and operation control is introduced, which is made possible by efficient information exchanges among the scheduling subsystem and the train control subsystem. A three-layer integration framework and associated co-optimization methodologies are presented by introducing a co-optimization layer that bridges the separated scheduling and control layers. Finally, this review is concluded with discussions on open questions and possible directions for future research.

Virtually coupled train sets (VCTS) have been proposed to increase the transportation capacity and the flexibility of railway organization. Due to the lack of reliable wireless communications and accurate perceptual information, the promotion of VCTS was challenged. With the development of industrial Internet of Things (IIoT), an IIoT-based VCTS is built in the article based on the popular communication-based train control architecture. Considering the dynamic and complex operation environment, it is difficult to achieve the efficient cooperative control of VCTS. The reason is that the traditional method is frequently trapped into a local optimization. To resolve the problem, we apply reinforcement learning (RL) to obtain an optimal policy for the IIoT-based VCTS, where the traditional artificial potential field (APF) is taken to develop the reward function. RL can thus search the global optimal policy, whereas APF can help RL to reduce the computation complexity. This can substantially increase the efficiency of the proposed approach. Simulation results confirmed that the proposed RL-based cooperative control approach would bring excellent performance in the IIoT-based VCTS.