Automatic Train Regulation of Metro Line with Approximate Dynamic Programming
Date Issued
2011
Date
2011
Author(s)
Sheu, Jih-Wen
Abstract
A rail rapid transit system in an urban area consists of several at-grade, elevated, or underground railway lines with exclusive right-of-way. Since the rail rapid transit system can provide high transportation capacity with high service frequency to meet heavy traffic demands, provide better transportation service quality, as well as provide a safe and comfortable riding environment for passengers, it has been recognized as an effective solution to traffic congestion problems in metropolitan areas. Nowadays, in the cities where there is an existing rail rapid transit system, it is difficult to conceive how the people could function properly without this mode of transportation. In a modern rail rapid transit system, an automatic train control system (ATC) is responsible for ensuring operation safety, maintaining schedule and headway adherence to meet the required service quality and capacity demands, and particularly for recovering normal operation in the face of passenger flow fluctuations and disturbances resulting in delays.
Depending on signaling system design, there are two main types of ATC system: fixed block ATC system and moving block ATC system. The block is a design concept to ensure the safe separation between consecutive trains running along a railway line. In a fixed block ATC system, a railway line is divided into blocks by using track circuits, therewith the safe separations are ensured by inhibiting trains from entering occupied blocks. In a moving block ATC system, instead of deploying track circuits, more precise train positioning can be achieved through a continuous wireless data communication system and the safe separations are ensured by inhibiting other trains from entering the blocks set and moved with trains running along a railway line. No matter what type of ATC system is used in a modern metro, the automatic train regulation (ATR) is definitely the key function of an ATC system in maintaining schedule and headway adherence to ensure reliable operations and required service quality.
In this dissertation, a comprehensive review of the developments of train control system is provided first, based primarily on the performance relevant issues of ATR, which includes stability, punctuality, schedule adherence, capacity, de-carbonization, and energy saving as well. Basically, designing ATR is intrinsically a large scale optimal control problem with high nonlinearity, heavy constraints, high complexity, and stochastic characteristics. Hence conventional linear optimization theories would be not appropriate to deal with such a problem, though it can be found in relevant literature that ATR designs have mostly been approached with linear optimization theories. Thus, some issues remain to be further investigated. In this research work, the environment of train regulation is investigated first, and the models for describing the traffic environment and energy consumption resulting from traffic regulation are developed for designing ATR. Thereby, an ATR design is approached by using adequate optimization techniques, neural networks, and reinforcement learning methods so that the designed ATR can adapt to the nonlinearity and fluctuations of the environment.
Dynamic programming (DP) is widely used to deal with the optimization problem with a small scale system but always suffers from the “curse of dimensionality” problem with a large scale system because of the backward search method underlying DP. Hence the real implementation of DP is rather intractable. Approximate dynamic programming (ADP) is an emerging technique that turns the real implementation of DP from the difficulty of dimensionality by using an adaptive critic mechanism. A critic in an ADP method is designed to learn to predict system performance as guidance for optimizing a regulator performance without using the backward search method. Thus, the optimization problem with a large scale system can be easily handled by using ADP methods. One ADP method, dual heuristic dynamic programming (DHP) has been successfully employed in this research to optimize the regulator design of ATR, where the regulator and the critic are constructed by using a Multilayer-Perceptron Network (MLP) and adapted via a neural network learning process. The verification of the DHP-based ATR design referring to practical operation data of Taipei metro shows a near optimal design of the regulator is achievable. Nevertheless, the influences of traffic modeling error on the performance of the DHP-based ATR are appreciable because the exact mathematical model for describing the environment is hard to access in real implementation. Thus the capability of coping with the effects of modeling errors is an important issue when dealing with an ATR design.
In order to improve the performance of an ATR design in regard to modeling error as well as optimality, an Adaptive Optimal Control (AOC) method is developed in this research work by introducing the Pontryagin’s Minimum Principle (PMP) into a DHP method. The evaluation of ATR designs with field data confirms that the ATR design with AOC method is more robust against traffic modeling error than that with a DHP method and is able to find a more near-optimal solution. This is because the model for predicting the state for critic prediction is no longer necessary in an AOC design.
Considering energy consumption is highly dependent on traffic regulation, a model describing traffic environments associated with energy consumption is developed for designing the ATR with energy saving. With the model, the optimal traffic regulation with higher energy efficiency is attainable with adequate tailoring of a performance index for the optimization. Simulation tests with field data also show considerable energy savings in train regulation can be gained via the use of the ATR design with energy saving.
Subjects
Approximate Dynamic Programming
Automatic Train Regulation
Adaptive Optimal Control
Adaptive Critic Design
Dynamic Programming
Metro
Type
thesis
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