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This article discusses the coordinated control problem of multiple high-speed trains operating under the virtual coupling mode. The study proposes a novel distributed model predictive control (DMPC) algorithm to address safety inter-train distance constraints and other independent constraints affecting train dynamics. The algorithm decomposes the centralized control problem into a sequence of local problems and considers potential hypothetical control policies among neighboring trains. The feasibility and effectiveness of the proposed algorithm are confirmed through theoretical analysis and numerical experiments. The article also discusses the modeling and control of high-speed trains operating under the virtual coupling mode, with a focus on designing control strategies that ensure speed consensus, maintain inter-train distances, and avoid collisions. A distributed model predictive control framework is proposed to handle the multiple constraints involved in the cooperative operation of the trains. The article presents a distributed model predictive control problem for coordinating the operation of high-speed trains in a virtual coupling mode. The problem is solved using a distributed serial iterative architecture to reduce communication requirements and increase robustness. The problem is converted into a series of smaller optimization problems that can be solved cooperatively based on information from adjacent trains. The feasibility of the optimization problems is proven, and the proposed approach is more practical for implementation. The article discusses a distributed control protocol for high-speed trains operating under the virtual coupling mode, aiming to achieve speed consensus and maintain safe distances between trains. The effectiveness of the control protocol is supported by a theorem and proof, and numerical simulations comparing the proposed protocol with other control policies are conducted. The simulations consider parameters [Extracted from the article]

This paper deals with the coordinated control of the multiple high-speed train system taking into account the sampled characteristic of the communication information. Considering the inertial lag of the servo motor/braking unit in practical scenarios, a third-order nonlinear model is adopted to capture the dynamics of a train, based on which the coordinated control problem is formulated. By virtue of the back-stepping linearization technique, the sampled-data communication based train coordination is transformed to the stabilization of a linear multiple-input system subject to periodic time-varying input delay. An event-triggering communication framework is constructed to lower the dependence on the communication resource. Furthermore, distributed low-gain feedback control protocols are established that, besides achieving coordination of the train operation, ensure the trains track the predefined train speed profile while avoiding unnecessary information transmission. Numerical experiments are carried out to verify the theoretical results. [ABSTRACT FROM AUTHOR]

This paper studies coordinated control of multiple high-speed trains (HSTs) in the presence of actuator saturation. For HSTs operating on a railway line under the moving block signaling system (MBS) mode, adjacent trains are connected directly through train-to-train (T2T) wireless communication such that each train exchanges information with the adjacent trains that are inside its communication range, making all trains in a railway line a multi-agent system. A state-dependent undirected graph is introduced to depict the underlying communication topology. Coordinated control laws are established that, in addition to achieving consensus among trains at the reference displacement and speed profile and avoiding collision, preserve communication connectivity. It is worth mentioning that, for each train, only the position information from its neighboring trains is needed to cause speed and displacement consensus. The effectiveness of our proposed control laws are verified by simulation study. [ABSTRACT FROM AUTHOR]

In this paper, novel adaptive fault-tolerant control (FTC) algorithms are put forward to address the position and velocity tracking control problems of high-speed trains (HSTs). The basic running resistances, additional resistances and interactive forces between the connected carriages are taken into account. On account of the system uncertainties, a novel multidimensional sliding mode surface with time-varying parameters estimated by the adaptive technique is proposed. And neural networks (NNS) are made use of approximating the additional resistances viewed as a bounded disturbance. Two cases for system parameters are taken into consideration: 1) Parameters with unknown boundary are unknown; 2) Parameters with known upper and lower boundary are unknown. As for the former, the unknown parameters are formulated via formal mathematical expression with indicator functions and the adaptive technique is introduced; As for the latter, a continuous functions related to the above multidimensional sliding mode surface is defined and the adaptive technique with Project functions is designed, which guarantees saturation characteristics of the presented controller. The closed-loop system stability can be proved by means of the Lyapunov theory and the feasibility and effectiveness of the presented control schemes can be revealed via simulation experiments. [ABSTRACT FROM AUTHOR]

The tracking control problem for high-speed trains (HSTs) with the running resistance, the additional resistance and unknown actuator faults is investigated. First, based on the linearisation principle HSTs are modelled as the linear system with the unmatched time-varying uncertainty resulting from the interactive force among the connected carriages. Then, during the procedure of controller design, the adaptive control is applied to compensating for the unknown actuator faults. Meanwhile, control is used for rejecting and attenuating the effects of the norm-bounded disturbance. In order to obtain good tracking performances, two novel adaptive active fault-tolerant controllers are proposed. The stability of the closed-loop system via two control schemes is proved by the Lyapunov theory and linear matrix inequation. The effectiveness of the presented control scheme is validated by simulation results. [ABSTRACT FROM AUTHOR]

Abstract: To guarantee the position and velocity tracking performance of high speed trains (HSTs) with actuator faults, a composite control algorithm consisting of the disturbance‐observer‐based control (DOBC) and ℋ ∞ control is proposed. Based on the multiple point‐mass model, the dynamics of HSTs is established by a cascade of carriages which are connected by flexible couplers, during the procedure of which, the running resistance, actuator faults and multiple disturbances are taken into account. The multiple disturbances are composed of two parts, one of which is the ramp resistance due to the track slope, the other is unknown gusts which can be modeled as a harmonic disturbance with time‐varying frequency. The unknown gusts is estimated and rejected via the DOBC methodology, meanwhile, the running resistance and the ramp resistance are attenuated by the ℋ ∞ control methodology. According to the Lyapunov stability analysis and LMI‐based algorithms, main results are derived such that the closed‐loop system is asymptotically stable and the desired performance can be guaranteed. Compared with the numeral simulation results with the single ℋ ∞ control method, it is demonstrated that the proposed control methodology is more effective and the system has a higher precision of position and velocity tracking. [ABSTRACT FROM AUTHOR]

The problem of the position and velocity tracking control for high-speed trains (HSTs) subject to unknown basic resistance, extra resistance and actuator faults is investigated. Neural adaptive control strategies based on a novel sliding mode surface technique are presented for three different cases (actuator faults and input saturation are neither considered; only the former is considered, and both are considered) to tackle the problem. For each case, the radial basis function (RBF) neural networks is introduced to approximate the unknown extra resistance consisting of ramp resistance, tunnel resistance, curve resistance, and so on; unknown coefficients of sliding mode surface and dynamics formulation are obtained online via adaptive laws. Simulation results demonstrate the effectiveness of the presented control methodologies. [ABSTRACT FROM AUTHOR]

The operation of high-speed trains is susceptible to perturbations such as strong winds and heavy snowfall, causing them to deviate from operating schedules. A joint mixed-integer linear programming (MILP) model based on the event-activity network is proposed to reschedule the train timetable and platform assignment jointly. Trains traveling in different directions are simultaneously considered in this model, and they can occupy the available reverse platforms in stations. Furthermore, to strengthen the robustness of the rescheduling scheme in case of uncertain perturbations, such as variable temporary speed restrictions caused by changing wind speed, the uncertainty is modeled using scenario-based chance constraints, which are then converted into deterministic constraints. Model predictive control (MPC) divides the entire time horizon into several stages. In each stage, the proposed model is decomposed according to directions. The constraints for trains traveling in different directions are dualized by the Alternating Direction Method of Multipliers (ADMM), which makes the sub-problems decoupled and solvable in parallel in each iteration of ADMM, promoting real-time performance. The real timetable and line data of Beijing–Shanghai HSR are utilized to investigate the effect of the proposed method. Compared to the commonly used strategies, it can significantly reduce delays and the number of affected events. The proposed method also shows relatively high robustness compared to the method in the case of predefined perturbations. It causes fewer conflicts and fewer needs for rescheduling when facing the changing perturbation and gets rescheduling results with higher quality. • This work deals with the joint rescheduling problem for train timetables and platform assignments. • Chance constraints are constructed to deal with the uncertainty of perturbations and converted to deterministic constraints. • The ADMM within the MPC is designed to parallelly solve the sub-problems of each operation direction in each iteration. • The proposed method shows relatively high robustness and outperforms the commonly used rescheduling strategies. [ABSTRACT FROM AUTHOR]