Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation

Speed adjustment braking of Automatic Train Operation (ATO) system is studied in this paper. The characteristics of the train traction and the single phase of speed adjustment during the train braking are firstly described. Then, a mathematical model is introduced with the form of the experimental data. A Fuzzy-PID switching controller is first proposed on the ATO system, thus improving the response of the system and accuracy. Finally, the braking method is shown to be true and comfortable for the passengers through the smooth switch numerical example.

Automatic train operation (ATO) system is one of the important components in advanced train operation control systems. Ideal controllers are expected for the automatic driving function of ATO systems. Aiming at the intelligence requirements of the systems, an NSGA-II-based parameter tuning method for the fuzzy immune PID (FI-PID) controller and a grey model GM(1,1)-based fuzzy grey immune PID (FGI-PID) controller were proposed. Taking a maglev train’s model as the control object and a velocity-time curve as the input, the feasibility of the parameter tuning method for the FI-PID controller and the applicability of the FI-PID controller and the FGI-PID controller for the ATO system were tested. The results showed that the optimized parameters were ideal, the two controllers all showed good performance on the indicators of traceability and comfort level, and the FGI-PID controller performed better than the FI-PID controller. The results exhibited the effectiveness of the proposed methods.

An automatic train operation (ATO) system is a crucial part of the communication-based train control (CBTC) system. It enables calculation and adjustment of train operation curve, which guarantees the efficient operation of urban rail transits. With limited computation resources, the present onboard ATO equipment cannot meet the latency-critical requirements of train speed profile calculation. Edge computing is considered as an effective approach to improve calculation capabilities of computation resource-constrained devices. This paper proposes an edge computing-enabled ATO system. A game-theoretic method is used to optimize the computation offloading process for the proposed system. We formulate the offloading decisions of multiple trains as a multi-user potential game. An optimal computation offloading scheme is obtained when multiple trains achieve the Nash equilibrium as game players. The numerical results certify the effectiveness of our proposed approach.

In this chapter, background material and literature review on the operation of trains and on urban rail train scheduling are presented. In Sect. 2.1, the operation of trains is introduced, where the automatic train operation (ATO) system is explained in detail. In addition, a brief introduction to fixed block signaling systems and moving block signaling systems is also given. An overview of optimal control approaches for the trajectory planning of a single train and multiple trains is provided in Sect. 2.2. Section 2.3 introduces the urban rail transit scheduling problem is introduced. This chapter concludes with a short summary in Sect. 2.4.

The wide availability of microprocessors with much higher computational power capabilities has encouraged many researchers during the last decade to introduce the predictive control techniques to the field of power electronics and motor drives. One technique of predictive control, which is called model predictive control, is introduced in this paper and its application to control a five-phase induction motor is also presented. The mathematical formulation of both model predictive torque control and model predictive speed control algorithms are explained, which shows that the algorithms' equations are intuitive so they can be easily understood and implemented. Since a load torque estimate is needed in the predictive speed controller, an observer based on the minimal-order Gopinath's method is designed to get the load torque value. A comparison between the two techniques is carried out based on a simulation study. Also, the simulation results show the effectiveness of the predictive control in achieving a high dynamic performance control of multiphase induction motor drives.

In the complex heating systems, higher order, large inertia, large delay and uncertainty typically exist. This work introduces generalized predictive PID control algorithm to adjust the system. The purpose of the control algorithm is to achieve a steady state in a shorter time. On the basis of analyzing PID control algorithm and Generalized Predictive Control (GPC), configuring a given performance function index control-weighted sequences to GPC control. Derived form of generalized predictive control algorithm with PID. The Matlab simulation shows that using generalized predictive PID control algorithm can produce better results in shorter time than simple using PID or GPC in the heating system.

The traffic congestion in and around Tokyo has become intensified year after year, and the subway has come to be looked upon as the most effective way of eliminating it, or alleviating it to an appreciable extent at least. The Teito Rapid Transit Authority (TRTA) in Japan has had an automatic train control (ATC) system in operation for many years to ensure safety adequately while fully exploiting its service efficiency. In the face of ever-increasing population and transport demand, the Authority in its quest for higher efficiency and safety in train operation pioneered in developing an automatic train operation (ATO) system and has been testing it on the Hibiya Line since 1962. Under the ATO, all train functions-notch-in, power running, coasting, braking, and stopping--are performed automatically. The motorman simply presses a pushbutton to start the train, which then runs on safely under ATC and stops smoothly, accurately, and automatically at the next station. We are satisfied with the results of tests in regard to the durability, reliability, and accuracy of the system as well as in regard to riding quality. The Authority is convinced that by putting this new system into operation the ATO can carry on passenger service at a shorter headway, with greater safety and accuracy, and can expand traffic capacity to some extent. Further study is now under way to improve the system by combining a centralized traffic control system and an inductive radio system, in order to exercise group control over all the trains on the track.

Intelligent-PID controller design for speed track in automatic train operation system with heuristic algorithms

This paper describes how traffic simulation models in metro lines are widely used in predictive control algorithms for traffic regulation and robustness analysis of timetables. The simulation results are highly dependent on the uncertainty modeling. The two main parameters in the simulation models are running times and dwell times. In lines operated with Automatic Train Operation (ATO) systems, running times are more deterministic than random (consequence of control actions), while dwell times show a higher stochastic behavior due to the influence of passengers and drivers. Typically, simulation models do not include a realistic modeling of dwell time uncertainty, and the confidence on results is affected. This paper focused on the stochastic component of dwell times in order to obtain a realistic model suitable for traffic simulation of metro lines. For this purpose, several statistical studies have been developed considering peak and off-peak hours, incidences, relations with other operation variables, etc. Models have been obtained and validated using data of different days and Metro de Madrid lines.

Review of energy-efficient train control and timetabling

This article deals with the design of a simple predictive control algorithm applied to a bidirectional DC-DC power converter for the angular speed control of a DC motor. We used the dynamics of a DC motor but mathematically reduced them to arrive at a simple model that is ideal for our purpose, not only to meet the control objective but also to generate reliable data for further analysis. This predictive control approach is based on the discrete time mathematical model of a DC motor. A huge capacitor to emulate an electric vehicle battery was then successfully connected to our experimental platform. Due to the robustness of the proposed control algorithm, the same predictive control scheme provided sufficient information to monitor the battery’s state. On this basis, and due to the system’s efficiency, it was possible to configure a fault detection scheme in our electric car battery emulator using only classical statistical tools. A PIC18F252 microcontroller was used in our experimental platform to implement our predictive control algorithm. It was then appropriately coupled to the power electronics required by the DC-DC converter to drive the DC motor. Our experimental results proved the excellent performance of the control method and also of the health monitoring system. On the other hand, the main difficulty in achieving our main goal was the realization of discrete control, which had to be as simple as possible while maintaining the control objective and while also being capable of generating reliable data for the health monitoring stage. Thus, the primary contribution of this work was the development of the predictive control of the speed of a universal motor, followed by the modification of the experimental design to simulate an electric car battery and the introduction of a novel statistical method for fault detection.

With the development of ATO (Automatic Train Operation) system for heavy-haul trains, advanced control methods to ensure the safe and stable operation of the train have become an urgent demand. To this end, a cascade control method based on MPC-PI (model predictive control and proportional integral) control is proposed. A multi-particle dynamic model of heavy-haul trains is established, and then the model is linearized for control algorithm design. In this cascade control method, the outer loop adopts MPC with large sampling time, and the optimization objectives consist of in-train forces, energy consumption and speed tracking error. The MPC control is formulated as QP (Quadratic Programming) problem, the output of which is used as the initial input of the inner loop. The inner loop adopts PI control with small sampling time, and adjusts the actual train control output in real time for the speed tracking error. The simulation results of 20,000 tons heavy-haul train show that the proposed method has better robustness and anti-interference than MPC and PI control separately. The optimization objective function is formulated with weighted coupler force and energy consumption, and the simulation results are compared with different sets of weights, which prove the effectiveness of the proposed method.

Servo applications often require fast response inner current loop in order to ensure high dynamic performance of the whole system. Predictive control algorithm can achieve fast dynamic response while avoiding overshoot. However, there is an inherent defect that it relies on the accurate mathematical model of the controlled object. An incremental predictive current control algorithm adopting an improved PWM updating strategy was presented. The relationship between system stability and parameter mismatch was analyzed using root locus method. Simulation and experimental results demonstrate that the proposed incremental predictive current control algorithm shows well dynamic performance and the improved updating strategy achieves significantly increased robustness to parameter mismatch, verifying the feasibility of the methods presented.

With the rising energy prices and environment concerns, energy-efficient operation is paid more and more attention in the subway systems, which is one of the effective ways to reduce the operation cost and then improve the operation efficiency. There are many researches for energy-efficient operation on designing a reasonable timetable or optimizing the speed profile, but all of them are studied separately. To achieve a better performance on energy saving, we present an integrated algorithm for optimizing the timetable for the entire route together with the speed profiles between successive stations, which is named as integrated timetable. First, we give an analytical algorithm to get the optimal switching points for each section by using the Pontryagin maximum principle. For dealing with the variable speed limits, we calculate the reserve time, i.e., the difference between the pre-determined trip time and the minimum trip time, and then design an algorithm to distribute it among different sections with constant speed limits. Finally, we extend the algorithm to solve the integrated timetable. We also give some numerical examples to illustrate the validity of the algorithm based on the data from the Beijing YiZhuang subway line in China. It is recorded that the energy consumption for interstation trip can be reduced by 10.3% on average, and the total energy reduction for the entire trip is 14.5% by using the integrated timetable. Besides, it takes about 0.15 seconds to generate the integrated timetable, which means that the algorithm is quick enough to be used in the automatic train operation (ATO) system for real-time control.

A genetic algorithm (GA) is proposed to optimise train movements using appropriate coast control that can be integrated within automatic train operation (ATO) systems. The coast control output for a train changes with the interstation distances and gradient profiles, and the current operating conditions of the mass rapid transit (MRT) system, namely, (i) train schedules, (ii) expected passenger loads and (iii) expected track voltages. The algorithm generates an optimum coast control based on evaluation of the punctuality, riding comfort and energy consumption. Before the train sets off to the designated station, a coast control table is generated that will be referenced by the train at runtime for deciding when to initiate coasting or resume motoring control. Each coast control table is encoded into variable length chromosomes with each gene representing the relative position between stations where coasting should be initiated or terminated. Each generation is evolved from mating of the paired equal-length chromosomes with possibilities of crossover, mutations, gene duplications and gene deletions. The key feature of this method is that it has a solid mathematical foundation. Effectively, the implementation provides good, credible and reasonably fast solutions for this variable dimensional and multiobjective optimisation problem. The algorithm has the potentials for on-line implementation for producing the coast control lookup table for each interstation run before the train sets off. The results, although preliminary, suggest that the method is promising.