High-speed Train Control Research Articles

Neural adaptive fault-tolerant control for high-speed trains with input saturation and unknown disturbance

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.

Real-Time Energy-Saving Optimization for Multiple Trains Based on Multiagent Cooperative Control

Nowadays, the energy-saving optimization problem has gradually become a hot spot in the subject of high-speed train control. Traditional static train trajectory planning is designed offline according to a preplanned timetable, but ignored are the uncertainties of parameters because of line conditions and other factors in the process of operation. Thus, the previous operation strategy may not suit the remaining section. The cooperative control strategy for multiple trains was investigated with the aim of conserving energy between successive stations under the constraints of safety and punctuality. First, combined with the train operation strategy under the quasi-moving block system of the Chinese Train Control System, a train cooperative interaction mechanism based on multiagent input is proposed to obtain the real-time running characteristics of trains on the railway via the Radio Block Center (RBC) agent. In the case of unanticipated events of railway line conditions, train operation is often out of accord with the timetable. Therefore, a resilience set related to trip-time deviation and headway variation is introduced to evaluate the robustness of the railway system and achieve balance in the train group. Also, a multigroup parallel differential evolution algorithm is developed to solve the online optimization problem of a high-speed train group according to real-time information. The proposed approach is analyzed by using operational data from the Wuhan–Changsha highspeed railway line in China to assess energy savings and punctuality. Results of the case study show superior performance and effectiveness of the model, and the methodology is illustrated.

Uniform Rolling-Wear-Based Robust Adaptive Control of High-Speed Trains in the Presence of Actuator Differences

Small persistent differences of slip velocities due to different actuation effectiveness of driving motors/braking units may lead to severe nonuniform rolling wear or fatigue damage of a part of actuation wheelsets after long-term operation, which would shorten service life or even endanger operational safety of the high-speed train. How to eliminate the nonuniform rolling wear/fatigue damage of actuation wheelsets using the control method is a very interesting and challenging issue. In this paper, robust adaptive observers are developed to identify uncertain dynamics of the train body and actuation wheelsets, based on which a uniform rolling-wear-based traction/braking control scheme is established. It is shown that with this controller, not only the common objective of traction/braking operation is achieved, but also actuator differences are completely compensated such that the same slip velocity (implying uniform driving load and rolling wear) is ensured for all actuation wheelsets during long-term operation. Both theoretical analysis and numerical simulations validate the effectiveness of the proposed control method.

Low-Cost Adaptive Fault-Tolerant Approach for Semiactive Suspension Control of High-Speed Trains

Excessive vertical and lateral motion of the train body could endanger the operation safety of a high-speed train. The situation is further complicated if actuation faults occur. This paper investigates a semiactive approach for suppressing such motions. By using the structural properties of the system model, a new control scheme is proposed to account for various factors such as input nonlinearities, actuator failures, and external disturbances in the system simultaneously. The resultant control scheme is capable of automatically generating the intermediate control parameters and literally producing the PD-like controller—the whole process does not require precise information on system model or system parameter. Furthermore, unlike traditional PD control, the proposed one has the stability-guaranteed algorithms to self-adjust its PD gains and there is no need for human tuning or trial and error process. Such user-friendly feature is deemed favorable for practical implementation. The effectiveness of the proposed controller is tested using computer simulations in the presence of parametric uncertainties and varying operation conditions.

An extended system-theoretic hazard analysis method for the safety of high-speed railway train control systems

A high-speed railway train control system plays a key role in ensuring the safe operation of the trains. To ensure the safety of high-speed railway train control systems, it is vital to perform hazard analysis on them. In this context, this paper proposes a novel hazard analysis method which extends a previously reported system-theoretic hazard analysis method “system-theoretic process analysis.” The proposed method improves the standard system-theoretic process analysis to capture the temporal relations between inadequate control actions leading to hazards. These temporal relations are crucial for investigating the causes of hazards. To depict the temporal relations of control actions of high-speed railway train control systems, a new temporal logic called “control action temporal logic” is proposed first. Then based on this temporal logic, a new control action relation model is added into the process of system-theoretic process analysis. This model depicts the relations (including temporal relations) between control actions. In order to identify both the inadequate control actions and their temporal relations, an algorithm is designed and used. Finally, the effectiveness of the proposed method is verified through the hazard analysis on the Chinese Train Control System level 3.

Neuro-adaptive fault-tolerant control of high speed trains under traction-braking failures using self-structuring neural networks

This paper develops an adaptive control scheme for position and velocity tracking control of high speed trains under uncertain system nonlinearities and actuator failures. Neural networks with self-structuring capabilities are integrated into control design, where the number of the neurons can be adjusted online automatically, so that not only the problem inherent in the NN with fixed structure is avoided, but also the negative impacts arising from nonlinear in-train forces, traction/braking uncertain dynamics as well as the unknown actuation faults are effectively attenuated. It is shown that the resultant control algorithms are able to achieve high precision train speed and position tracking under varying operation railway conditions, as validated by theoretical analysis and numerical simulations.

Passivity-based cruise control of high speed trains

The cruise control problem of high speed trains (HSTs) is revisited in this paper. Despite the ongoing trend of using Lyapunov-based approaches, the concept of passivity is used as the basis of cruise controller design. To begin with, the Euler–Lagrange modeling of longitudinal motion of HST is introduced. Consequently, passivity properties of the system is investigated and it is shown that the system presents a strictly passive input–output map output. This property is utilized to design a controller based on an energy-shaping method. Since the controller benefits from the passivity property of the train, it is structurally simple and computationally efficient while ensuring asymptotic velocity tracking. In addition, as revealed in our robust analysis, the controller is capable of dealing with bounded perturbations. That is to say, boundedness of velocity tracking errors is guaranteed for sufficiently large control feedback gains. The obtained theoretical results have been verified by numerical simulation.

Reliable dissipative control of high-speed train with probabilistic time-varying delays

ABSTRACTThis paper investigates the reliable dissipative control problem for high-speed trains (HSTs) under probabilistic time-varying sampling with a known upper bound on the sampling intervals. In particular, random variables obeying the Bernoulli distribution are considered to account for the probabilistic time-varying delays. Based on Lyapunov–Krasovskii functional approach which considers full use of the available information about actual sampling pattern, a new set of sufficient condition is established to guarantee that the HST can well track the desired speed and the relative spring displacement between the two neighbouring carriages is asymptotically stable and the corresponding error system is strictly -dissipative. The existence condition of the dissipativity-based reliable sampled-data controller is obtained in terms of a set of linear matrix inequalities which are delay-distribution-dependent, i.e. the solvability of the condition depends on not only the variation range of the delay but also the probability distribution of it. Moreover, different control processes for the HST system can be obtained from the proposed design procedure and hence it can reduce the time and cost. Finally, the effectiveness and benefits of the proposed control law is demonstrated through a numerical example by taking the experimental values of Japan Shinkansen HST.

Fuzzy Constrained Predictive Optimal Control of High Speed Train with Actuator Dynamics

We investigate the problem of fuzzy constrained predictive optimal control of high speed train considering the effect of actuator dynamics. The dynamics feature of the high speed train is modeled as a cascade of cars connected by flexible couplers, and the formulation is mathematically transformed into a Takagi-Sugeno (T-S) fuzzy model. The goal of this study is to design a state feedback control law at each decision step to enhance safety, comfort, and energy efficiency of high speed train subject to safety constraints on the control input. Based on Lyapunov stability theory, the problem of optimizing an upper bound on the cruise control cost function subject to input constraints is reduced to a convex optimization problem involving linear matrix inequalities (LMIs). Furthermore, we analyze the influences of second-order actuator dynamics on the fuzzy constrained predictive controller, which shows risk of potentially deteriorating the overall system. Employing backstepping method, an actuator compensator is proposed to accommodate for the influence of the actuator dynamics. The experimental results show that with the proposed approach high speed train can track the desired speed, the relative coupler displacement between the neighbouring cars is stable at the equilibrium state, and the influence of actuator dynamics is reduced, which demonstrate the validity and effectiveness of the proposed approaches.

Adaptive Iterative Learning Control for High-Speed Trains With Unknown Speed Delays and Input Saturations

In this paper, an adaptive iterative learning control (AILC) strategy for high-speed trains with unknown speed delays and control input saturations is designed to address speed trajectory tracking problem. The train motion dynamics containing nonlinearities and parametric uncertainties are formulated as a nonlinearly parameterized system. Instead of estimation or modeling of train delays, an unknown time-varying delay term is integrated into the speed on delay analysis by means of Lyapunov–Krasovskii function. Through rigorous analysis, it is confirmed that the proposed AILC mechanism can guarantee $L_{[0, T]}^{2}$ convergence of train speed to the desired profile during operations repeatedly. Case studies with numerical simulations further verify the effectiveness of the proposed approach.

A Novel Approach for Active Adhesion Control of High-Speed Trains Under Antiskid Constraints

Wheel skid is highly undesirable because it could endanger the safe operation of high-speed trains. How to avoid excessive wheel skid via an active adhesion control method represents an interesting and challenging topic of research. In this work, we first introduce the conditions of antiskid operation and formulate it as a constrained tracking control problem, based on which two model-based antiskid slip velocity control laws are developed. Then, by applying two adaptive force observers to estimate the unknown and varying adhesion force and resistance, we develop an adaptive antiskid adhesion control scheme. The novelty of the proposed method is that control errors of the closed-loop system are used to online update the observer parameters, such that the predefined control precision can be ensured with the proposed observer-based adhesion control. To deal with the constrained antiskid control, a barrier Lyapunov function is constructed, and the effectiveness of the proposed control scheme is theoretically authenticated with confirmation by numerical simulation.

Adaptive coordinated control of multiple high-speed trains with input saturation

This paper investigates the adaptive coordinated control of multiple high-speed trains with input saturation and uncertain parameters. The motion of an ordered set of high-speed trains is modeled by a multi-agent system, in which each train adjusts its speed dynamically by communicating with its neighboring trains. For the uncertainties in systems and the possible actuator saturation, based on the LaSalles invariance principle, a new adaptive coordinated control algorithm for each train is designed to track the desired speed, and meanwhile under the control algorithm, the headway distances of each train with its neighboring trains are stabilized at stationary distances in a safety range, which ensures safe and efficient operation of high-speed trains. Numerical examples are given to illustrate the effectiveness of the proposed methods.

Energy Consumption Modeling and Optimization of Traction Control for High-speed Railway Trains

The energy consumption modeling and the optimization solution for the traction control of high-speed trains are addressed in this paper. Based on the analysis of the operation process, an improved train traction analysis with the multi-mass modeling approach is presented, and the corresponding energy consumption model is investigated. By considering the issues of the passenger comfort and traveling time, an optimization algorithm for traction control of the high-speed trains is presented by using a multiobjective-fitness particle swarm optimization technique. Simulations are performed with practical parameters of the CRH EMU (Electric Multiple Unit) and railway line, and the results demonstrate the performance of the proposed solution over the conventional traction calculation method.

Neuro-Adaptive Fault-Tolerant Approach for Active Suspension Control of High-Speed Trains

Excessive lateral and roll motions of a high-speed train might endanger its operational safety. This paper investigates how to suppress those motions via an active-suspension method. By exploiting the structural properties of the system model and the triangular control gain, a new control scheme capable of attenuating immeasurable disturbances, compensating modeling uncertainties, and accommodating actuation faults is developed. Compared with most existing methods, the proposed method does not require precise information on the suspension parameters and the detail system model. Moreover, the magnitude of the actuation fault and the time instant at which the actuation fault occurs are not needed in setting up and implementing the proposed control scheme. The controller is tested and validated via computer simulations in the presence of parametric uncertainties and varying operation conditions.

中国高速铁路列控系统的形式化分析与验证

It is essential that the correctness of high-speed train control systems be guaranteed by formal verification because they are safety-critical. However, as these systems become increasingly more complicated, achieving this goal in practice is difficult, and may even be infeasible. On the other hand, it is more convenient to graphically model a complicated system. A graphical model is fairly intuitive, which has resulted in such models being used extensively in industry. Thus, to improve the reliability of a high-speed train control system, constructing a graphical model for the system and then detecting its bugs by simulation should be very effective. In this paper, we first show how to use Simulink/Stateflow to build graphical models for various combined scenarios of Chinese train control systems (CTCS), in which mode conversion and level upgrade take place simultaneously. This modeling approach can be easily adapted to model other scenarios in CTCS by simply modifying the corresponding parameters. Then, we analyze these graphical models via simulations and show that under some circumstances the trains will stop abnormally. Finally, in order to avoid the inherent incompleteness of simulation, we show how to translate these graphical models into formal models given in HCSP-a formal modeling language for hybrid systems that extends CSP-and subsequently formally prove that abnormal stops can happen in many of the cases in one of the combined scenarios. Formal verification of Simulink/Stateflow diagrams complements simulation and improves the reliability of systems being developed.

Dynamic Control of High-speed Train Following Operation

Both safety and efficiency should be considered in high-speed train following control. The real-time calculation of dynamic safety following distance is used by the following train to understand the quality of its own following behavior. A new velocity difference control law can help the following train to adjust its own behavior from a safe and efficient steady-following state to another one if the actual following distance is greater than the safe following distance. Meanwhile, the stopping control law would work for collision avoidance when the actual following distance is less than the safe following distance. The simulation shows that the dynamic control of actual inter-train distance can be well accomplished by the behavioral adjustment of the following train, and verifies the effectiveness and feasibility of our presented methods for train following control.

High-speed train control based on multiple-model adaptive control with second-level adaptation

Speed uplift has become the leading trend for the development of current railway traffic. Ideally, under the high-speed transportation infrastructure, trains run at specified positions with designated speeds at appointed times. In view of the faster adaptation ability of multiple-model adaptive control with second-level adaptation (MMAC-SLA), we propose one type of MMAC-SLA for a class of nonlinear systems such as cascaded vehicles. By using an input decomposition technique, the corresponding stability proof is solved for the proposed MMAC-SLA, which synthesises the control signals from the weighted multiple models. The control strategy is utilised to challenge the position and speed tracking of high-speed trains with uncertain parameters. The simulation results demonstrate that the proposed MMAC-SLA can achieve small tracking errors with moderate in-train forces incurred under the control of flattening input signals with practical enforceability. This study also provides a new idea for the control of in-train forces by tracking the positions and speeds of cars while considering power constraints.

Hierarchical Modeling and Verification for High-speed Train Control Center by Time Automation

Chinese Train Control System level three (CTCS-3) is a major technical system in Chinese high-speed rail and Train Control System (TCC) is indispensable component in the CTCS-3. Current researches on TCC are mainly based on the simulation, which cannot ensure that all conditions in TCC are tested. This paper presents a hierarchical modeling method and uses time automation (TA) to model the TCC software. We take the design of the active balise telegram editing, a major part in the TCC software, as an example. At first, the process of the active balise telegram editing is analyzed to obtain a hierarchical diagram containing several layers. Then, TA is employed to build one TA model for each layer. Lastly, we use UPPAAL (a model validation tool, developed by Uppsala University and Aalborg University) to construct a network of the TA models to verify the active balise telegram editing. The verification results demonstrate that this modeling method is feasible and the model can meet the functional requirements of the TCC software.

Modelling and global predictive control for distributed-driven high-speed trains

This paper focuses on the modelling and control problem of a type of the Chinese High–Speed Train. A longitudinal dynamic model for a type of Chinese high–speed train is established based on multi–body spring–mass models, where the couplers connecting adjacent coaches are characterised by linear springs. To guarantee the safe operation of trains under certain velocity requirements, a model predictive controller is proposed to deal with in–train force and driving force constraints. Finally, a numerical simulation is given to demonstrate the effectiveness of the proposed methods.

Optimization of High-Speed Train Control Strategy for Traction Energy Saving Using an Improved Genetic Algorithm

A parallel multipopulation genetic algorithm (PMPGA) is proposed to optimize the train control strategy, which reduces the energy consumption at a specified running time. The paper considered not only energy consumption, but also running time, security, and riding comfort. Also an actual railway line (Beijing-Shanghai High-Speed Railway) parameter including the slop, tunnel, and curve was applied for simulation. Train traction property and braking property was explored detailed to ensure the accuracy of running. The PMPGA was also compared with the standard genetic algorithm (SGA); the influence of the fitness function representation on the search results was also explored. By running a series of simulations, energy savings were found, both qualitatively and quantitatively, which were affected by applying cursing and coasting running status. The paper compared the PMPGA with the multiobjective fuzzy optimization algorithm and differential evolution based algorithm and showed that PMPGA has achieved better result. The method can be widely applied to related high-speed train.