Automatic Stop Control Research Articles

Data-Driven Spatial Adaptive Terminal Iterative Learning Predictive Control for Automatic Stop Control of Subway Train With Actuator Saturation

A data-driven spatial adaptive terminal iterative learning predictive control (SATILPC) scheme with actuator saturation is proposed for automatic stop control of the subway train. Considering the outstanding repetitive operation pattern and the unavailable accurate model of a subway train, the unknown train dynamics is firstly transformed into a nonlinear discrete form in spatial domain via the spatial differential operator. Since the train automatic stop control (TASC) only concentrates on the tracking performances of the terminal position and terminal speed, an iterative dynamic linearization approach considering the terminal operation point is devised to formulate the relationship of the train input and output (I/O) into a linear affine form. Then, a terminal iterative learning prediction mechanism is introduced to reconstruct the developed train data model to forecast the future train behaviors through rolling optimization process. As a result, by removing the constraints on the unimportant points, the optimal control input (braking force) can be obtained by minimizing the objective function through terminal I/O data. Further, actuator saturation is considered to address the passenger comfort and the reliable operation of the train. The proposed SATILPC approach is a pure data-driven iterative learning control scheme and no model information is involved in the whole design processes. By utilizing a newly space-based contraction mapping method, the convergence of the proposed approach is strictly proved. Finally, the simulation results further demonstrate the feasibility of the proposed algorithm.

Position Manipulation Attacks to Balise-Based Train Automatic Stop Control

Balise is a popular wayside device to provide accurate location information for subway station parking by sending telegrams to passing trains. By craftily disturbing wireless signals of balise telegrams, this paper proposes three attacks that may make passengers fall and even cause injury. Concretely, the first attack is to jam telegrams such that balises cannot be detected by a passing train; the second attack changes the location of transmitting telegrams by jamming and replaying; and the third attack is to change the total time of transmitting telegrams. All the attacks exploit the train localization mechanism such that a passing train localizes its position inaccurately and then takes improper control actions. Furthermore, since these attacks are independent, they can be launched at the same time to achieve advanced attacks. As the attacks do not require to tamper with the balises, they can be launched easily. Our simulations demonstrate the effectiveness of the proposed attacks. To defeat these attacks, the received telegrams need be verified by a train based on fidelity of telegram data.

Development of Automatic Parameter Tuning for Train Automatic Stop Control Device

SUMMARYThe adoption of train automatic stop control (TASC) device is expected to increase according to high demand of platform door system. The challenges of TASC are (1) reduction of adjustment man‐hour at TASC introduction and (2) maintenance of stopping position accuracy. These challenges are caused by the difficulty of obtaining vehicle characteristics accurately in real time. To solve the difficulty, a function for automatic parameter tuning for TASC devices has been developed. The function learns vehicle characteristics from real‐time driving data and controls vehicle deceleration based on the learned vehicle characteristics. The developed function was evaluated on a test vehicle, and the results show that it acquired vehicle characteristics accurately and stopped the test vehicle within a stopping accuracy of +/– 35 cm. In addition, it was confirmed that the calculation time was within 70 s. This was less than the target time of 80 s, which is comparable to the average driving time between two stations on a metro line.

Online Learning Algorithms for Train Automatic Stop Control Using Precise Location Data of Balises

For urban metro systems with platform screen doors, train automatic stop control (TASC) has recently attracted significant attention from both industry and academia. Existing solutions to TASC are challenged by uncertain stopping errors and the fast decrease in service life of braking systems. In this paper, we try to solve the TASC problem using a new machine learning technique and propose a novel online learning control strategy with the help of the precise location data of balises installed at stations. By modeling and analysis, we find that the learning-based TASC is a challenging problem, having characteristics of small sample sizes and online learning. We then propose three algorithms for TASC by referring to heuristics, gradient descent, and reinforcement learning (RL), which are called heuristic online learning algorithm (HOA), gradient-descent-based online learning algorithm (GOA), and RL-based online learning algorithm (RLA), respectively. We also perform an extensive comparison study on a real-world data set collected in the Beijing subway. Our experimental results show that our approaches control all stopping errors in the range of ±0.30 m under various disturbances. In addition, our approaches can greatly increase the service life of braking systems by only changing the deceleration rate a few times, which is similar to experienced drivers. Among the three algorithms, RLA achieves the best results, and GOA is a little better than HOA. As online learning algorithms can dynamically reduce stopping errors by using the precise location data from balises, it is a promising technique in solving real-world problems.

Fuzzy制御による列車定位置停止制御

A predictive fuzzy control system that uses rules based on the experience of a skilled human operator is proposed and applied to achieve automatic stop operation for trains. In recent years, automatic train operation (ATO) systems using microcomputers to replace human operators have been developed for new transit systems, including subways, monorails and new public transit systems. Because the train operation is a typical uncertain nonlinear system, improvement of performance indices such as safety, riding comfort of passengers, and accuracy of the stopgap is great challenge to ATO. Up to now, ATO systems have been developed using linear control of the target pattern. However, it is difficult to control a train automatically in a manner similar to control by a human operator using linear control. In this paper, we propose a predictive fuzzy control system that selects the most likely control rule from a set of control rules. The system is described as follows: ”If (u is C → x is A and y is B) then u is C.”. The proposed fuzzy control system is applied to a train automatic stop control system that takes into account passenger comfort, accuracy of a stopgap and running time. Simulation results of this newly developed fuzzy control system indicate that the system can directly adjust system performance as desired in a manner similar to control by a skilled operator and thereby stop the train comfortably and accurately.