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It is a challenge to maintain a safe and efficient tracking for multiple high-speed trains under the moving block operational mode. In this paper, a novel cooperative tracking control based on a consensus algorithm and artificial potential field theory is proposed to realize the train tracking within a distance range. A tunable artificial potential function is first designed to dynamically adjust the distance between adjacent high-speed trains with real-time train states. By regulating the parameters of the artificial potential function, the safety distance can be adjusted according to the required tolerance deviation of the actual distance. Under the proposed strategy, each high-speed train operates with the desired speed and tracks the preceding one with an adjustable distance range. Numerical train operational cases are investigated to illustrate the effectiveness of the proposed methods., Author(s): Zhiwu Huang [1]; Pingping Wang [1]; Feng Zhou (corresponding author) [2]; Weirong Liu [3]; Jun Peng [3] 1. Introduction Based on an advanced wireless communication technology and positioning technology, [...]

The high-speed train operation process is highly nonlinear and has multiple constraints and objectives, which lead to a requirement for the automatic train operation (ATO) system. In this paper, a hybrid model predictive control (MPC) framework is proposed for the controller design of the ATO system. Firstly, a piecewise linear system with state and input constraints is constructed through piecewise linearization of the high-speed train's nonlinear dynamics. Secondly, the piecewise linear system is transformed into a mixed logical dynamical (MLD) system by introducing the auxiliary binary variables. For the transformed MLD system, a hybrid MPC controller is designed to realize the precise control under hard constraints. To reduce the online computation complexity, the explicit control law is computed offline by employing the mixed-integer linear programming (MILP) technique. Simulation results validate the effectiveness of the proposed method., 1. Introduction With rapid development of high-speed railway, the operation safety, punctuality, and energy consumption of train have received more and more attentions. Nowadays, in China, the high-speed railway generally [...]

Active braking control systems in high-speed trains are vital to ensure safety and are intended to reduce brake distances and prevent the wheels from locking. The slip ratio, which represents the relative difference between the wheel speed and vehicle velocity, is crucial to the design and successful implementation of active braking control systems. Slip ratio estimation and active braking control are challenging owing to the uncertainties of wheel–rail adhesion and system nonlinearities. Therefore, this paper proposes a novel adaptive slip ratio estimation approach for the active braking control based on an improved extended state observer. The extended state observer is developed through the augmentation of the system state-space to estimate the unmeasured train states as well as the model uncertainty. The accurate slip ratio is estimated using the observed extended states. Furthermore, the adaptability of the observer is improved by introducing the beetle antennae search algorithm to determine the optimal observer parameters. Finally, a feedback linearization braking control law is established to stabilize the closed-loop system due to its potential in coping with nonlinearities, which benefits the proven theoretical bounded stability. Experimental results validate the effectiveness of the proposed method. • An active braking control method based on an improved extended state observer is proposed for high-speed trains. • The designed observer is utilized to estimate both the slip ratio and train adhesion. • The adaptability of the extended state observer is improved by introducing the beetle antennae search algorithm to optimize its parameters. • A feedback linearization braking control law is established to counteract the system nonlinearity and uncertainty. [ABSTRACT FROM AUTHOR]

The track settlement has a great influence on the safe operation of high-speed trains. The existing track settlement measurement approach requires sophisticated or expensive equipments, and the real-time performance is limited. To address the issue, an ultra-high resolution track settlement detection method is proposed by using millimeter wave radar based on frequency modulated continuous wave (FMCW). Firstly, by constructing the RCS statistical feature data set of multiple objects in the track settlement measurement environment, a directed acyclic graph-support vector machine (DAG-SVM) based method is designed to solve the problem of track recognition in multi-object scenes. Then, the adaptive chirp-z-transform (ACZT) algorithm is used to estimate the distance between the radar and the track surface, which realizes automatic real-time track settlement detection. An experimental platform has been constructed to verify the effectiveness of the proposed method. The experimental results show that the accuracy of track classification and identification is at least 95%, and the accuracy of track settlement measurement exceeds 0.5 mm, which completely meets the accuracy requirements of the railway system. [ABSTRACT FROM AUTHOR]