High-speed Train Control Research Articles

Integral predictive sliding mode control for high-speed trains: A dynamic linearization and input constraint-based data-driven scheme

A control scheme with high reliability and excellent tracking performance is essential for the automatic operation of high-speed trains (HSTs). In this study, a novel discrete-time data-driven predictive sliding mode control (DDPSMC) scheme is proposed for multi-power unit HSTs. Initially, a nonlinear integral terminal sliding mode surface was designed to replace the traditional linear sliding mode function, thereby achieving a rapid system error convergence and alleviating chattering. Then, receding horizon optimization was integrated into predictive control, which allowed the predicted sliding mode state to follow the expected trajectory of a predefined continuous convergence law. This scheme enabled the system to obtain higher output error accuracy and explicitly handle input constraints. Moreover, to enhance robustness, a parameter update law and disturbance delay estimation algorithm were introduced to calculate the control gain and total uncertainty, respectively. Finally, a comparative test of the proposed control scheme was conducted using a CRH380A HST simulation experimental platform in a laboratory setting. Simulation results demonstrate that the velocity error range of each power unit of the HST under the proposed control scheme is within [−0.176 km/h, 0.152 km/h], while the control force and acceleration are within [−55.7 kN, 44.8 kN] and [−0.564 m/s2, 0.496 m/s2], respectively, with stable variation, and other performance indicators are also better than other comparison methods. These results satisfy the safety, stability, and punctuality requirements of the train.

Decentralized learning control for high-speed trains with unknown time-varying speed delays

Treating the multi-point-mass dynamic model of high-speed trains as an interconnected system, this study proposes a decentralized iterative learning control scheme for high-speed trains to achieve the trajectory tracking goal. By making reasonable estimates of the interaction term and compensating for it, the proposed control scheme utilizes only local information from each carriage and does not need any inter-carriage information exchange. The zero-error tracking of the desired trajectory is guaranteed even in a restricted communication environment. Considering unknown time-varying speed delays in the actual high-speed train operations, a modified decentralized iterative learning control scheme is also provided to address the negative impact of speed delays. The convergence of tracking errors is strictly proven by constructing appropriate composite energy functions. Numerical simulations further verify the theoretical results.

An artificial potential field approach for event-triggered cooperative control of multiple high-speed trains with free initial states

ABSTRACT In this paper, a multi-algorithm-fusion-based cooperative tracking control strategy is proposed for high-speed trains to achieve the control objectives of reference speed trajectory tracking and adjacent safety distance maintenance simultaneously. First, the multiple high-speed train (MHST) system is formulated as a multi-agent system (MAS) with a virtual leader. Second, a coordination controller that considers the dynamic tracking performance and input saturation is designed by combining the MAS leader-follower consensus algorithm and the improved artificial potential field (APF) method. In addition, within the train-to-train (T2T) communication network, an event-triggered mechanism is introduced in the continuous state transmission process to address the communication channel capacity protection limitation issue. Third, the closed-loop stability of the MHST system is ensured by the comprehensive analysis of a novel log-type Lyapunov function. Finally, the effectiveness of the proposed event-triggered cooperative tracking control strategy is verified through a numerical example of the MHST system on a typical line.

Interior noise prediction of inter-coach space of high-speed maglev trains based on wavenumber decomposition on aerodynamic excitation

Current research on noise and vibration control of high-speed maglev trains pays more attention to far-field noise, while the level of interior noise has a direct impact on the ride comfort and should be placed equal weight on. In this paper, inter-coach space, one of the main pressure fluctuation sources of a specific type of high-speed maglev train with a design speed of 600 km·h−1, is taken as the research object. The turbulent and acoustic components of wall pressure fluctuations (WPF) are separated based on a wavenumber-frequency analysis approach, and then each component is applied as different forms of source input to the vibroacoustic model, namely, finite element method-boundary element method (FEM-BEM) and statistical energy analysis (SEA) for low- and high-frequency ranges respectively, to investigate the contribution of both components to interior acoustic cavity in all frequency range. It can be seen quantitatively from the results that the amplitude of turbulent component is generally much higher than that of the acoustic one, but it can be vice versa when it comes to the interior response. The conclusion drawn in this paper are able to provide guidance for future researches on more targeted interior noise control of high-speed maglev trains.

Disturbance Observer-Based Tracking Control of High-Speed Trains Under Adhesion Dynamics

As the anti-skid control and speed tracking control of high-speed trains (HSTs) are interrelated and mutually constrained, it is very challenging to achieve simultaneous tracking of speed and slip ratio. In this paper, it is proposed for the first time that when the traction braking force has been limited to a reasonable range by the adhesion anti-skid condition, the high-precision tracking of the position and velocity curves of HSTs can be achieved, and the tracking error of the slip ratio is acceptable. Firstly, a novel modelling of the adhesion dynamics of HSTs under realistic gusts of wind disturbance is developed, and a model based on strict feedback is obtained by linearising the feedback for high-order non-linear systems. Then, this paper first presents the design scheme of fast terminal sliding mode controller based on disturbance observer for HSTs, which can ensure the anti-disturbance performance and accurate tracking of HSTs. Finally, an example is used to verify the validity of the results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The actual traction/braking force of HSTs is provided by adhesion, and the existing literature on slip ratio tracking essentially sacrifices the tracking performance of the traction/braking system to meet the anti-skid requirements, while considering velocity and position tracking alone may leave the train in an undesired condition such as “idling” or “skidding”. This paper seeks to achieve high accuracy tracking of velocity and position profiles of HSTs under restricting the slip ratio to a reasonable range, which is expected to be an effective method for train tracking controller design. To avoid discomfort to passengers, nonlinearities dynamics are considered and an anti-disturbance controller is designed to compensate the impact of natural wind disturbances. To enhance the practicality, feedback linearization is introduced to reduce the complexity of the controller implementation. The designed fast terminal sliding mode controller also avoids jitter and singularity problems and obtains finite time stability. The stability analysis and numerical simulation have confirmed the effectiveness of the proposed scheme, which still needs to be verified by experiments in the future.

Distributed Adaptive Fault-Tolerant Control for High-Speed Trains Using Multi-Agent System Model

Distributed Adaptive Fault-Tolerant Control for High-Speed Trains Using Multi-Agent System Model

Distributed Learning Control for High-Speed Trains Subject to Operation Safety Constraints.

Trajectory tracking problems related to high-speed trains (HSTs) are fundamental issues that affect the operation safety and ride comfort. This study proposes a distributed learning control scheme based on a multiagent system framework for trajectory tracking of HSTs subject to operation safety constraints. Two different connection modes are considered between the carriages: 1) a soft connection achieved via information exchange and 2) a hard connection enforced via couplers. Both relative displacement and speed constraints are carefully analyzed in the tracking control process. By constructing an appropriate barrier composite energy function, the convergence of the tracking error and satisfaction of the operation safety constraints are rigorously proven for the proposed scheme. The theoretical results are verified using numerical simulations.

Robust adaptive iterative learning control for high-speed trains under non-strictly repeated conditions

In this paper, the speed and position control of high-speed trains (HSTs) under non-strictly repeated conditions is investigated. Considering the modeling accuracy and control cost, a multi-particle dynamics model is established in a single coordinate system. Based on this model, a novel robust adaptive iterative learning control (RAILC) method is proposed. The Lyapunov function is established to prove that the system state converges in a finite time, and the composite energy function (CEF) is constructed to prove that the control error approaches zero along the iteration axis. The application value in engineering practice of the proposed RAILC is verified by two simulation tests. Existing adaptive iterative learning control (AILC) and adaptive terminal sliding mode control (ATSMC) are introduced as control groups to show the system convergence of the RAILC on the iteration axis and time axis.

Adaptive Nonsingular Terminal Sliding Mode Tracking Control for High-speed Trains With Input Constraints and Parametric Uncertainties

Adaptive Nonsingular Terminal Sliding Mode Tracking Control for High-speed Trains With Input Constraints and Parametric Uncertainties

Cooperative Operation Control of Virtual Coupling High-Speed Trains With Input Saturation and Full-State Constraints

In this paper, the distributed cooperative control of virtual coupling high-speed trains (HSTs) subject to full-state constraints, actuator limitations, dynamical uncertainties and environmental disturbances is investigated. Targeting at the full-state constraints in cooperative operation of HSTs, a distributed nonlinear state-dependent function (DNSDF) is first proposed to convert the state-constrained problem of the leader-following consensus control to the boundedness problem of DNSDF. Then, the distributed control law of each train is designed by combining the command filtering backstepping method and the adaptive neural network approximation technique. Meanwhile, combined with the DNSDF, a novel auxiliary dynamical system (ADS) is designed to compensate for the adverse effects of actuator input saturation, and thus ensure the closed-loop stability of the HSTs system when the state constraints and the input saturation are considered simultaneously. By utilizing the Lyapunov theory, the convergence of the proposed controller is analyzed. Finally, the feasibility and effectiveness of the proposed control scheme are verified by simulations. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work was motivated by the problem of cooperative control for virtual coupling HSTs with different initial states, actuator input saturation, full-state constraints, etc. The proposed approach addresses the train position and speed constraints by applying DNSDF, which can ensure train coordinated operation with relative braking distance and further reduce the tracking interval between trains. Specifically, the upper bound of the train position constraint varies with the real-time position of the preceding train and the relative speed of the adjacent trains, rather than a constant value. More importantly, an ADS is designed based on the DNSDF, which guarantees the stability of the controller when the input saturation and state constraints exist simultaneously, and avoids the chattering phenomenon of the train control input. The simulation results show that the trains can operate cooperatively at any initial speed within the speed limitations. In future, we will focus on the cooperative control of HSTs with communication delay, and the energy saving optimization cooperative control of HSTs.

Distributed Cooperative Control of Virtual Coupled High-Speed Trains with Consideration of Ride Comfort

Distributed Cooperative Control of Virtual Coupled High-Speed Trains with Consideration of Ride Comfort

History Makes the Future: Iterative Learning Control for High-Speed Trains

History Makes the Future: Iterative Learning Control for High-Speed Trains

Adaptive cooperative control for multiple high-speed trains with uncertainties, input saturations and state constraints

This paper investigates the adaptive cooperative control of multiple high-speed trains, where uncertainties, input saturations and state constraints are considered simultaneously. Within the multi-agent framework, the multi-train system cooperative operation problem is formulated, with the leader train tracks the desired operation trajectory, and the follower trains automatically operate according to the state information of the front train. In order to overcome the parameter uncertainties and unknown external disturbances of the system, a bounded-parameter robust adaptive control (BRAC) method is proposed for speed and position tracking of the leader train. Then a novel state-constraint bounded-parameter robust adaptive controller (SBRAC) is further designed in two steps to avoid the risk of overspeed and collision of the follower trains. The auxiliary dynamic system (ADS) with saturation compensation is introduced to solve input saturations, and the barrier Lyapunov function (BLF) is constructed to actively constrain the speed and position of the trains. Finally, numerical simulations are given for the multi-train system to demonstrate the effectiveness of the theoretical studies.

Sliding-mode Adaptive Control for Multiple High-speed Trains With State Constraints and Input Saturation

Sliding-mode Adaptive Control for Multiple High-speed Trains With State Constraints and Input Saturation

Distributed Event-Triggered Iterative Learning Control for Multiple High-Speed Trains With Switching Topologies: A Data-Driven Approach

This paper studies the distributed data-driven event-triggered model free adaptive iterative learning control (ETMFAILC) of multiple high-speed trains (MHSTs) under iteration-varying topologies, which breaks away from the dependence on the train dynamics. Firstly, the nonlinear MHSTs with unknown dynamics are converted into a linear model. Then, combining the proposed event-based triggering condition and the linear model, the ETMFAILC scheme under the fixed topology is designed. Next, theoretical analysis proves the bounded input bounded output (BIBO) stability of MHSTs. Finally, the study is extended to the switching topologies and the validity of the ETMFAILC is verified by a numerical example.

Spatial Adaptive Iterative Learning Tracking Control for High-Speed Trains Considering Passing Through Neutral Sections

Spatial Adaptive Iterative Learning Tracking Control for High-Speed Trains Considering Passing Through Neutral Sections

Refined disturbance observer based prescribed performance fixed-time control of high-speed EMS trains with track irregularities

High-speed Electromagnetic Suspension (EMS) train is continuously impacted by the irregularity of the track, which worsens the levitation performance of the train. In this paper, a composite control scheme for the EMS is proposed to suppress track irregularities by integrating a Refined Disturbance Observer (RDO) and a Prescribed Performance Fixed-Time Controller (PPFTC). The RDO is designed to estimate precisely the track irregularities and lumped disturbances with uncertainties and exogenous disturbances in the suspension system, and reduce input chattering by applying to the disturbance compensation channel. PPFTC is designed to converge the suspension air gap error to equilibrium point with prescribed performance by completing error conversion, and solve the fast dynamic issue of EMS. And the boundary of overshoot and steady-state is limited in the ranged prescribed. A theoretical analysis is conducted on the stability of the proposed control method. Finally, the effectiveness and reasonability of the proposed composite anti-disturbance control scheme is verified by simulation results.

Virtual sample generation for model-based prognostics and health management of on-board high-speed train control system

In view of class imbalance in data-driven modeling for Prognostics and Health Management (PHM), existing classification methods may fail in generating effective fault prediction models for the on-board high-speed train control equipment. A virtual sample generation solution based on Generative Adversarial Network (GAN) is proposed to overcome this shortcoming. Aiming at augmenting the sample classes with the imbalanced data problem, the GAN-based virtual sample generation strategy is embedded into the establishment of fault prediction models. Under the PHM framework of the on-board train control system, the virtual sample generation principle and the detailed procedures are presented. With the enhanced class-balancing mechanism and the designed sample augmentation logic, the PHM scheme of the on-board train control equipment has powerful data condition adaptability and can effectively predict the fault probability and life cycle status. Practical data from a specific type of on-board train control system is employed for the validation of the presented solution. The comparative results indicate that GAN-based sample augmentation is capable of achieving a desirable sample balancing level and enhancing the performance of correspondingly derived fault prediction models for the Condition-based Maintenance (CBM) operations.

Research on speed control of high-speed trains based on hybrid modeling

With the continuous improvement of train speed, the automatic driving of trains instead of driver driving has become the development direction of rail transit in order to realize traffic automation. The application of single modeling methods for speed control in the automatic operation of high-speed trains lacks exploration of the com-bination of train operation data information and physical model, resulting in low system modeling accuracy, which impacts the effectiveness of speed control and the operation of high-speed trains. To further increase the dynamic modeling accuracy of high-speed train operation and the high-speed train's speed control effect, a high-speed train speed control method based on hybrid modeling of mechanism and data drive is put forward. Firstly, a model of the high-speed train's mechanism was created by analyzing the train's dynamics. Secondly, the improved kernel-principal component regression algorithm was used to create a data-driven model using the actual opera-tion data of the CRH3 (China Railway High-speed 3) high-speed train from Huashan North Railway Station to Xi'an North Railway Station of "Zhengxi High-speed Railway," completing the mechanism model compensation and the error correction of the speed of the actual operation process of the high-speed train, and realizing the hybrid modeling of mechanism and data-driven. Finally, the prediction Fuzzy PID control algorithm was devel-oped based on the natural line and train characteristics to complete the train speed control simulation under the hybrid model and the mechanism model, respectively. In addition, analysis and comparison analysis were conduct-ed. The results indicate that, compared to the high-speed train speed control based on the mechanism model, the high-speed train speed control based on hybrid modeling is more accurate, with an average speed control error reduced by 69.42%. This can effectively reduce the speed control error, improve the speed control effect and oper-ation efficiency, and demonstrate the efficacy of the hybrid modeling and algorithm. The research results can provide a new ideal of multi-model fusion modeling for the dynamic modeling of high-speed train operation, further improve control objectives such as safety, comfort, and efficiency of high-speed train operation, and pro-vide a reference for automatic driving and intelligent driving of high-speed trains.

Disturbance observer-based fuzzy fault-tolerant control for high-speed trains with multiple disturbances

Abstract The fault-tolerant control problem is investigated for high-speed trains with actuator faults and multiple disturbances in this paper. Based on the novel train model resulting from Takagi and Sugeno fuzzy theory, a sliding-mode fault-tolerant control strategy is proposed. The norm bounded disturbances which are composed of interactive forces among adjacent carriages and basis running resistances are rearranged by the fuzzy linearity technique. The modeled disturbances described as an exogenous system are compensated by a disturbance observer. Moreover, a sliding mode surface is constructed, which can transform the stabilization problem of position and velocity into the stabilization problem of position errors and velocity errors, i.e., the tracking problem of position and velocity. Based on the parallel distributed compensation method and the disturbance observer, the fault-tolerant controller is solved. Lyapunov theory is used to prove the stability of the closed-loop system. The feasibility and effectiveness of the proposed fault-tolerant control strategy are illustrated by simulation results.