Virtual Coupling (VC) has emerged as a promising strategy for railway system control, offering significant enhancements to rail-line capacity and addressing uneven transport demand by dynamically minimizing the headway distance between trains when necessary. Consequently, the operating targets of VC rear trains, which can be influenced by operational environments and railway line conditions, must be promptly adjusted based on the spacing from the adjacent leading train. This adjustment ensures operational safety within the train movement authority. Different from traditional continuous prescribed performance control (PPC), this paper proposes a discontinuous reinforced prescribed performance control (RPPC) strategy, which takes into account both actual and converted errors and introduces innovative segmented dynamic control strategies designed for continuous operation time periods and discontinuous impulse points. To prevent unnecessary disruptive switching during transient moments, switching constraints (SCs), based on the speed and position of the rear train, are established. Additionally, leveraging Lyapunov theory, the stability of the closed-loop system for train operation is rigorously proven. Finally, a simulation scenario for a 3-train formation, along with a further data-based semi-physical simulation of a 2-train system, are presented for demonstrating the feasibility of the main results.

Intermittent fault (IF) threatens the safety for modern dynamic systems severely. The target of this work is to swiftly detect the occurrence and relievement of IFs and accurately gauge their severity characterized by unknown values, enabling effective intervention, and rectification if necessary. An innovative adaptive nondeterministic observer-based technique for diagnosing IFs for a class of dynamic systems subject to model uncertainty and external disturbances is proposed. The term “nondeterministic” underscores the dynamic nature of threshold function and gain matrices for IF detection, as well as regressor for IF identification. For the IF detection task, a time-varying threshold function and a pattern-matched gain technique, ensuring both timely and precise IF occurrence and relievement signals, are proposed. For the IF identification task, an event-rectified regressor-based algorithm is proposed to deliver unbiased estimations of IFs. Moreover, a compelling illustrative example applying the proposed whole diagnosis scheme to an analog circuit system is given, showcasing the efficacy in addressing the challenges of diagnosing IFs in dynamic systems affected by uncertainty and disturbance.

This article presents a new safe fault-tolerant control scheme for train formation by designing an adaptive event-triggered reinforced performance technique. The new features and merits of the proposed method are as threefold aspects. 1) The proposed method integrates safety constraints on train position and speed into the control design process. A nonlinear transformation function is employed to convert constrained train states into unconstrained error variables, which simplifies the dealing of nonlinearities arising from safety constraints, ensuring trains maintain safe tracking distances and speeds. 2) by utilizing the projection algorithm, the proposed method estimates and compensates for actuator failures using safety-constrained train position and speed data. This method demonstrates superior tracking accuracy compared to existing fault-tolerant control algorithms, even in scenarios with actuator faults. In addition, considering the continuous control challenges due to the complex physical structure of high-speed trains, an event-triggered approach is introduced to alleviate unnecessary operations and minimize wear and tear on mechanical components caused by frequent updates. 3) In comparison to pioneering so-called prescribed performance control methodology, where the tracking errors are kept within predefined boundary functions regardless of control gains and other parameters, the “reinforced performance” of this work ensures that defined errors are guaranteed to evolve within regions characterized by predefined boundary functions and control parameters simultaneously, correspondingly, the ultimate convergence regions can be adjusted to be arbitrarily small by choosing proper control parameters.

The high-speed train control system is essential to the safety and efficiency of train operation. With the rapid increase of high-speed railway (HSR) operating mileage and development of information technology, the disposal flow and methods in emergency response are still based on dispatchers and drivers’ experience within the “layered” architecture of current system. There is a certain gap between current processing methods and effective resolution, which may even cause the spread of delay along with the railway networks. Therefore, we propose an integration system of operation control and online rescheduling to improve the recovery ability of HSR carrying capacity. We first describe the framework, information flow, and disposal process of the current system and analyze the shortcomings in handling emergencies. Then, the basic concept, system structure, and framework of the integration system are introduced. Finally, taking temporary speed restriction caused by strong wind as an example, we also analyze the principle of why and how the integration system can promote the recovery ability of HSR carrying capacity.

This article is concerned with an expansive errors (EE)-based fuzzy adaptive prescribed performance control of a class of multiple-input and multiple-output nonlinear systems in the presence of unknown interconnection nonlinearities using fuzzy residual approximation technique. Based on an newly defined expansive error, residual nonlinearities’ fuzzy approximation scheme is proposed. The merits of designed control can be presented as twofold: 1) by incorporating an EE-based technique into pioneering prescribed performance control methodology, the defined intermediate and output tracking errors are kept within the regions represented by preassigned functions and control parameters simultaneously and 2) expansive errors-related residual nonlinearity is approximated by fuzzy logic systems, which further ameliorates the output tracking performance by compensating the affects raised by composite inner and interconnection nonlinearities among subsystems. With the designed expansive errors-based fuzzy adaptive prescribed performance control, all the closed-loop signals are kept bounded in the sense of Lyapunov stability theorem. Comparative simulation studies are presented to verify the effectiveness and advantages of theoretical findings.

This paper presents a fuzzy adaptive protective control method for autonomous high-speed trains (HSTs) automatic operation using a new outstretched error feedback design approach. In order to stabilizing the error dynamics with respect to target position and speed profiles of controlled HSTs, nonlinear transformation in prescribed performance control methodology is used to convert running states subject to protective (constrained) information, imposed by automatic train protection (ATP) subsystem, to new coordinates in unconstrained form. By blending a new outstretched error feedback and fuzzy approximation, it is guaranteed above-mentioned errors are kept within regions characterized by error boundary (or prescribed performance) functions and control parameters simultaneously, which can be adjusted to arbitrarily small even without error decreasing boundary functions. Fuzzy approximation is used in compensating unknown running resistances. It is rigorously proved that the resulting closed-loop system is stable in sense of Lyapunov stability in the presence of unknown resistance, containing basis and aerodynamic resistances with uncertain parameters and piecewise continuously slope resistance over varying gradient profile. The proposed control is demonstrated to be effective by comparative simulations of train G1 running on Beijing-Shanghai railway line.

This article addresses the speed-distance trajectory tracking control problem for railway trains to facilitate the effectuation of automation train operation (ATO). By proposing a new virtual parameter learning-based approach, we develop an adaptive control that exhibits twofold new features with comparison to the existing literatures:i), while the nonlinear operational resistance and railway line gradient profile are unknown, the proposed control not only bears a quite computationally inexpensive simplicity in structure but also achieves accurate tracking control with respect to the speed-distance trajectory benefiting by the virtual parameter learning approach and requiring no function approximators with linearized structure, for example, common utilization of neural or fuzzy approximations, to cope with the uncertain dynamic nonlinearities in real-time, andii), by introducing a nonlinear error transformation, the protection enveloping problem, which is generally introduced by the onboard automatic train protection and wayside subsystems, operating independently from the ATO subsystem in practice, are considered explicitly to the control design for ATO for the first time. By invoking Lyapunov stability theorem, the resulting closed-loop system is guaranteed to be globally stable with rigorously analysis and proof. Meanwhile, in order to verify and validate the effectiveness and advantages of theoretical findings, experimental and comparative results, by applying the designed controller to the whole Beijing railway Yizhuang line, are shown.

This paper addresses an error-driven nonlinear feedback design technique to improve the dynamic performance of fuzzy adaptive dynamic surface control (DSC) for a class of uncertain multiple-input-multiple-output nonlinear systems with prescribed tracking performance. The highlight of the error-driven nonlinear feedback technique is that the feedback gain self-regulates versus different levels of output and virtual tracking errors, this reflects the classical control design criterions commendably: relatively high feedback gains can be implemented to guarantee disturbances and uncertainties attenuation and so on to improve the control performance when small tracking errors are measured, and relatively small feedback gains can be implemented to circumvent the problems of actuator and states saturations when large tracking errors are measured. The complexity problem of the traditional backstepping design is circumvented owe to the peculiarity of DSC method. Caused by the compound error functions of nonlinear feedback dynamics, a nonquadratic Lyapunov function is used to deduce the conditions of closed-loop stability. Fuzzy logic systems and error transformation-based method are used in the online learning of completely unknown dynamics and the prescribed performance tracking, respectively. Comparative results are presented to demonstrate the effectiveness and preponderance of the proposed control scheme with comparison to existing ones.

If high-speed trains move under a moving block signaling (MBS) system, it is a quite challengeable and significant issue to cooperate the multiple high-speed trains, such that they are separated safely and governed steadily. As an intelligent, comprehensive, and advanced modern train operation control system, the MBS system will definitely, in the near future, replace the widely used fixed block signaling system due to its modern communication, multi-source positioning, and advanced control method. Due to the involvement of several subsystems and lots of track-side equipment, such as radio block center (RBC), global system for mobile communications-railway (GSM-R), and so on, it is a nontrivial task to develop a reliable MBS system under complex operational environments, particularly without a pivotally cooperative control method for the movement of multiple high-speed trains. This paper addresses the cooperative control for multiple high-speed trains to achieve prescribed performance tracking, i.e., the speed and the position of high-speed trains are guaranteed to be confined to specific speed limitations and allowed distances ratified by automatic train protection and moving authority, respectively. The proposed control requires no prior information of the empirical parameters of the operational resistances and online adjusts by proper adaptation laws. Theoretical analysis and simulation results are given to demonstrate the effectiveness of the proposed control methods.

For metro systems in over-crowded conditions, when an unexpected disturbance occurs, the operation of trains might be disturbed due to the high frequency and density of the metro traffic. A large number of passengers might be stranded on platforms due to service gaps and the limited free capacity of trains. In this paper, by introducing binary variables as selection indicators for ATO profiles which were preset in on-board ATO systems by metro signal suppliers, we develop a mixed integer programming (MIP) model for a metro train timetable rescheduling problem in order to jointly optimize the total train delay, the number of stranded passengers, and the energy consumption of trains. We formulate the total energy consumption as the difference between the tractive energy consumption and the regenerated energy by considering the mass of in-vehicle passengers. Then, we adopt commercial optimization software CPLEX to solve the proposed model, which can obtain tradeoff solutions in a short time. Finally, three numerical experiments based on real-world operational data are carried out to verify the effectiveness of the proposed method.

The information exchange gap between current operation control and dynamic scheduling in high-speed railway systems (HRSs) still exists, and this gap has hindered the further integrative improvement of HRSs. This paper aims to explore a feasible solution to bridging the information exchange gap for further improving the efficiency of HRSs, with the parallel intelligent systems for integrated HRS operation control and dynamic scheduling first analyzed and constructed using the ACP approach, that is, "artificial systems" (A), "computational experiments," (C) and "parallel execution" (P). Then, on the basis of the constructed parallel intelligent systems, experiments on several typical scenarios in HRSs are conducted to achieve a set of control and management strategies for actual HRSs. Experimental results show that a number of powerful tools provided by the proposed parallel intelligent systems can be utilized not only to study the current HRSs, but also to further undertake research on integrated operation control and dynamic scheduling for HRSs.

This talk presents an observer-based neural adaptive dynamic surface control for MIMO uncertain nonlinear systems based on a nonlinear feedback technique, which tries to combine the merits of high gain feedback and low gain feedback in an easily manner. The core idea of such technique is that the feedback gain holds nonlinear mapping relationship with system states, which is achieved by a continuous differentiable nonlinear gain function. Caused by the compound property of nonlinear gain feedback, a non-quadratic Lyapunov function is designed to prove the closed-loop stability. Comparative results are shown to verify the effectiveness.

Emerging communication-based train control techniques are a critical foundation for automatic or semiautomatic train operation for guaranteed safety, line utilization, operation efficiency, and energy saving toward intelligent rail transportation systems. Multiple-train cooperative control encounters great challenges from train control, communications, interval coordination, and uncertainties in operational environments. This paper introduces cooperative control methods and corresponding stability criterions for multiple trains under moving-block signaling systems. Two coordination scenarios are considered, and corresponding control algorithms are proposed, and their stabilities are established using Lyapunov and invariant-set theorems. The proposed controllers hold the minimal computation complexity, i.e., only one parameter needs online tuning by virtue of an ingenious parameter estimation technique. The methodologies utilize the information of “nearest neighbor trains” through onboard sensors and train-train (T2T) communications but guarantee global deployment and performance of the multiple trains queuing. The control abilities of the algorithms under predecessor following and bidirectional architecture modes are analyzed and demonstrated to be effective via simulation studies.

In an urban rail transit system, issues regarding headway regulation have aroused wide attention. The assurance of headway regularity can decrease train delay times and average passenger waiting times. An integrated control method is proposed to optimize train headway by adjusting the train arrival time at stations. The adjustment of train arrival time is achieved by using an analytical method, and then the speed profile for each train is calculated by a suboptimal method, which has been applied in a practical system. Through simulation, the CPU time for calculating optimal train arrival time and speed profile is analyzed, respectively. The analysis demonstrates that the proposed method satisfies the real-time requirements for solving the headway regulation problem. By adopting the proposed method, the average passenger waiting time and the energy consumption can be decreased. In particular, the proposed method has better performance when the dispatch headway is large.

This paper addresses an on-line approximation-based robust adaptive control problem for the automatic train operation (ATO) system under actuator saturation caused by constraints from serving motors. A robust adaptive control law is proposed, which is proved capable of on-line estimating of the unknown system parameters and stabilizing the closed-loop system. To cope with actuator saturation, another robust adaptive control is proposed for the ATO system, by explicitly considering the actuator saturation nonlinearity other than unknown system parameters, which is also proved capable of stabilizing the closed-loop system. Simulation results are presented to verify the effectiveness of the two proposed control laws.

This paper introduces a framework of parallel control and management for high-speed railway systems (HRSs). First, based on multiagent modeling, an artificial HRS that is consistent with realistic operations of the actual HRS is constructed. Then, different kinds of computational experiments are performed on the artificial HRS, followed by analysis and synthesis with a case. Finally, through an interactive and parallel operation between the actual and artificial HRSs, a set of practical control and management strategies can be achieved for the actual HRS. With the primary objective of ensuring reliability and safety of HRSs, this study could enhance the quality of services and the integrated transportability with other existing modes of transportation systems to provide appropriate recommendations and strategies for forming an overall effective comprehensive transportation system.