This paper presents a robust event-triggered model predictive control (MPC) strategy for multiple high-speed trains (MHSTs) with random switching topologies. Due to the complicated operation environment of high-speed railways, the communication topology of MHSTs system is time-varying and changes among a set of directed graphs, which can be characterized as a Markov chain. By adopting the concept of MPC, this paper studies the distributed cooperative leader-following consensus control for MHSTs, in which a novel event-triggered strategy is introduced to determine when information exchange among neighboring trains and control update are executed. Firstly, the leader-following consensus problem of MHSTs system is transformed to the stabilization of a Markov jump system and a sufficient condition for leader-following consensus is derived with stability analysis of the Markov jump system based on the robust event-triggered MPC scheme. Then, the robust event-triggered MPC algorithm which minimizes the objective function is proposed. By optimizing the objective function, the deviation of states and amplitude of the control force are optimized. The effectiveness of the proposed robust event-triggered MPC method on cooperative cruise control of MHSTs is illustrated by numerical examples.

Obtaining accurate point prediction of industrial processes' key variables is challenging due to the outliers and noise that are common in industrial data. Hence the prediction intervals (PIs) have been widely adopted to quantify the uncertainty related to the point prediction. In order to improve the prediction accuracy and quantify the level of uncertainty associated with the point prediction, this article estimates the PIs by using ensemble stochastic configuration networks (SCNs) and bootstrap method. The estimated PIs can guarantee both the modeling stability and computational efficiency. To encourage the cooperation among the base SCNs and improve the robustness of the ensemble SCNs when the training data are contaminated with noise and outliers, a simultaneous robust training method of the ensemble SCNs is developed based on the Bayesian ridge regression and M-estimate. Moreover, the hyperparameters of the assumed distributions over noise and output weights of the ensemble SCNs are estimated by the expectation-maximization (EM) algorithm, which can result in the optimal PIs and better prediction accuracy. Finally, the performance of the proposed approach is evaluated on three benchmark data sets and a real-world data set collected from a refinery. The experimental results demonstrate that the proposed approach exhibits better performance in terms of the quality of PIs, prediction accuracy, and robustness.

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.

In order to improve the operation efficiency of high-speed trains and save communication resource of the railway communication network, this paper proposes an on-demand event-triggered strategy for coordinated control of multiple high-speed trains (MHSTs) subject to communication delays and input constraints. The MHSTs are modeled as a leader-following multi-agent system and each train exchanges their states with its neighboring trains through a communication network. Then, an event-triggered scheme is designed to determine when the states of a train are transmitted to its local controller and neighboring trains. With this strategy fused into the dynamic model of MHSTs, a robust coordinated control scheme consisting an event-triggered processor and a control protocol is presented. By utilizing Lyapunov-Krasovskii theory, a sufficient condition is achieved to guarantee the MHSTs system stability subject to communication delays and input constraints. A co-design algorithm for obtaining the controller gain and the event-triggered weighting matrix is proposed. Finally, numerical examples are carried out to illustrate the effectiveness of the proposed method.

The denial-of-service (DoS) attacks injected in the communication network of high-speed trains can block the transmission of information between trains by preventing trains from sending data and interfering with communication network. In order to solve this problem, this paper investigates the cooperative control strategy for high-speed trains subject to DoS attacks through a resilient control scheme. A secure control framework is proposed to restore the communication network of the multi-train system caused by DoS attacks to the initial communication network by proposing an acknowledgment-based attack detection strategy and a recovery mechanism. Then, distributed controllers with considering input saturation are proposed under this framework by applying the Lyapunov theory and low-gain feedback method. In addition, the speed error and position error constraints can be guaranteed during the operating process of trains. Finally, the effectiveness of the proposed resilient control scheme is evaluated by the simulation example.

In this paper, a wavelength diversity technique is employed in a Brillouin optical time domain reflectometry (BOTDR) using a Brillouin ring laser (BRL) as a local oscillator. In the wavelength diversity technique, multiple wavelengths are injected into the sensing fiber, while the peak power of each wavelength is set below the nonlinear threshold level. This technique significantly maximizes the overall launch pump power, without activating the non-negligible nonlinear effects, which overcomes the limitation of the conventional BOTDR system. The BRL, which is simple and cost-effective, that can be used to reduce the receiver bandwidth in the order of few MHz. In addition, a passive depolarizer is used to reduce the polarization noise. The proposed system is validated experimentally over a 50 km sensing fiber with a 5 m spatial resolution. The experimental results demonstrate a signal-to-noise ratio improvement of 5.1 dB, which corresponds to 180% improvement compared to a conventional BOTDR system.