Mehmet Turan Soylemez

Bio: Mehmet Turan Soylemez is an academic researcher from Istanbul Technical University. The author has contributed to research in topics: PID controller & Interlocking. The author has an hindex of 13, co-authored 97 publications receiving 890 citations. Previous affiliations of Mehmet Turan Soylemez include University of Manchester.

Coasting point optimisation for mass rail transit lines using artificial neural networks and genetic algorithms

Abstract: Energy consumption of a rail transit system depends on many parameters. One of the most effective methods of reducing energy consumption in a rail transit system is optimising the speed profile of the trains along the route. A new efficient method will be presented for the optimisation of the coasting points for trains in a global manner. The proposed approach includes realistic system modelling using multi-train, multi-line simulation software and application of artificial neural networks (ANN) and genetic algorithms (GA). The simulation software used can model regenerative braking and train performance at low voltages. Using ANN and GA together, optimal coasting points for long line sections covering five stations and two lines are achieved. Simulation software is used for creating training and test data for the ANN. These data are used for training of the ANN. Trained ANNs are then used for estimating energy consumption and travel time for new sets of coasting points. Finally, the outputs of the ANN are optimised to find optimal train coasting points. For this purpose, a fitness function with target travel time, energy consumption and weighting factors is proposed. An interesting observation is that the use of ANN increases the speed of optimisation. The proposed method is used for optimising coasting points for minimum energy consumption for a given travel time on the first 5 km section of Istanbul Aksaray-Airport metro line, where trains operate every 150 s. The section covers five passenger stations, which means four coasting points for each line. It has been demonstrated that an eight input ANNs can be trained with acceptable error margins for such a system.

Pole Assignment for Uncertain Systems

Abstract: From the Publisher: Pole assignment for uncertain systems is a book where many existing as well as new pole assignment methods are described using a common notation and a clear yet efficient language. The problem considered has two strands. First, classical pole assignment problem, and especially, obtaining parametric general solutions to this problem in a computer algebra framework is treated. Secondly, given a general parametric solution to the pole assignment problem, finding robust compensators that D-Stabilise the closed-loop system is considered. A summary of post-Kharitonov results together with how to apply these results to the pole assignment problem for uncertain systems are given to this end.

Robust pole assignment in uncertain systems

Abstract: The concept of 'pole colouring' is introduced as a graphical aid to the problem of observing the closed-loop system pole variations as a function of uncertainty. Some possible cost functions that can be used to measure the performance robustness of the resulting systems are also considered.

Linear systems

Abstract: Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

An Algorithm for Stabilization of Fractional-Order Time Delay Systems Using Fractional-Order PID Controllers

Abstract: This technical note presents a solution to the problem of stabilizing a given fractional-order system with time delay using fractional-order PllambdaDmu controllers. It is based on determining a set of global stability regions in the (kp, Ki, Kd)-space corresponding to the fractional orders lambda and mu in the range of (0, 2) and then choosing the biggest global stability region in this set. This method can be also used to find the set of stabilizing controllers that guarantees prespecified gain and phase margin requirements. The algorithm is simple and has reliable result which is illustrated by an example, and, hence, is practically useful in the analysis and design of fractional-order control systems.

A Survey on Energy-Efficient Train Operation for Urban Rail Transit

Abstract: Due to rising energy prices and environmental concerns, the energy efficiency of urban rail transit has attracted much attention from both researchers and practitioners in recent years. Timetable optimization and energy-efficient driving, as two mainly used train operation methods in relation to the tractive energy saving, make major contributions in reducing the energy consumption that has been studied for a long time. Generally speaking, timetable optimization synchronizes the accelerating and braking actions of trains to maximize the utilization of regenerative energy, and energy-efficient driving optimizes the speed profile at each section to minimize the tractive energy consumption. In this paper, we present a fully comprehensive survey on energy-efficient train operation for urban rail transit. First, a general energy consumption distribution of urban rail trains is described. Second, the current literature on timetable optimization and energy-efficient driving is reviewed. Finally, according to the review work, it is concluded that the integrated optimization method jointly optimizing the timetable and speed profile has become a new tendency and ought to be paid more attention in future research.

Single-Train Trajectory Optimization

Abstract: An energy-efficient train trajectory describing the motion of a single train can be used as an input to a driver guidance system or to an automatic train control system. The solution for the best trajectory is subject to certain operational, geographic, and physical constraints. There are two types of strategies commonly applied to obtain the energy-efficient trajectory. One is to allow the train to coast, thus using its available time margin to save energy. The other one is to control the speed dynamically while maintaining the required journey time. This paper proposes a distance-based train trajectory searching model, upon which three optimization algorithms are applied to search for the optimum train speed trajectory. Instead of searching for a detailed complicated control input for the train traction system, this model tries to obtain the speed level at each preset position along the journey. Three commonly adopted algorithms are extensively studied in a comparative style. It is found that the ant colony optimization (ACO) algorithm obtains better balance between stability and the quality of the results, in comparison with the genetic algorithm (GA). For offline applications, the additional computational effort required by dynamic programming (DP) is outweighed by the quality of the solution. It is recommended that multiple algorithms should be used to identify the optimum single-train trajectory and to improve the robustness of searched results.