Rail transport electric drive with intelligent control system
DOI:
https://doi.org/10.15588/1607-6761-2023-3-1Keywords:
neural network control system;, genetic algorithm;, asynchronous motor;, electric drive;, transient;, DS3;, curvature of the path;, computer vision.Abstract
Purpose. The study of the conditions for the occurrence of frictional self-oscillations, the synthesis of a neuroregulator eliminating self-oscillation, the development of a system for automatic control of the of railway transport speed depending on the curvature of the track on the basis of computer vision technology.
Methodology. Mathematical analysis and modeling.
Findings. The paper presents the results of the development and research of an intelligent control system for the electric drive of a DS3 mainline electric locomotive. The developed systems have a single easily implemented motor speed feedback, which does not create difficulties in physical implementation. It is noted that a common feature of the electric drive of rail transport is a nonlinear load characteristic. It is shown that, under certain combinations of parameters, frictional self-oscillations are possible in the traction electric drive. Effective elimination of frictional self-oscillations is done by synthesizing the system with a neuroregulator. The neural network has three input neurons that receive a vector of input signals in the form of a voltage signal, a signal of the motor speed value of the current and previous energy speed values. The number of neurons in the hidden layer of the system is 20 and there is one output neuron.The control actions for the frequency converter are formed on the output neuron. Neural networks of this type are designated NN3-20-1. The genetic algorithm method is used for all optimization of neural network parameters. The simulation model of the electric drive of rail transport has the integration of a computer vision unit. Increasing the level of automation and safety of rail vehicles is possible on the basis of computer vision. A feature of this structure is the presence of an NN neural regulator in it. NN ensures the specified quality of the transient process over the entire load range and when the operating point is located on a falling section. A system for automatic control of the speed of rail vehicles depending on the curvature of the track has been developed to increase the level of automation and traffic safety. Modeling of the system showed its efficiency, which is manifested in a decrease in the speed of rail vehicles when moving along a section of track with curvature.
Originality. Effective elimination of frictional self-oscillations due to the use of a neuroregulator.
Practical value. A system for automatically adjusting the speed of rail transport depending on the curvature of the track has been developed to increase the level of automation and traffic safety.
References
Wu, B., Xiao, G., An, B., Wu, T., Shen, Q. (2022). Nu-merical study of wheel/rail dynamic interactions for high-speed rail vehicles under low adhesion condi-tions during traction. Engineering Failure Analysis, vol. 137. DOI: https://doi.org/10.1016/j.engfailanal.2022.106266
Shteynvolf, L. (1966). Qualitative theory of fractional oscillations in mechanical gears. «Theory of mecha-nisms and machines». KhSU, Iss. 1. 76–88. (in Ukrainian).
Klepikov V. (1986) O frikcionnyh avtokolebaniyah v elektro-prividah [About frictional selfoscillations in electric drives]. Elektrichestvo, № 4. (in Ukrainian).
Klepikov V. (2014) Dinamika elektromehanicheskih system s nelinejnym treniem: monografija [Dynam-ics of electromechanical systems with nonlinear fric-tion: monograph], Kharkiv: NTU “KhPI”, 408 (in Ukrainian).
Kyrylenko, Y., Kutovyi, Yu., Obruch, I., Kunchenko, T. (2020). Neural network control of a frequency-regulated electric drive of a main electric locomotive. IEEE Problems of Automated Electrodrive. Theory and Practice (PAEP). DOI: 10.1109/PAEP49887.2020.9240880
Obruch, I. V., Kutovyi, Yu. N. (2015). Neural network control system for the electric drive of the АРП14 electric locomotive taking into account the elasticity of the kinematic connections. Journal of the Nation-al Technical University “Kharkiv Polytechnic Insti-tute”, issue 12 (1121), 248–250. (in Ukrainian).
Obruch, I. V. (2019). Synthesis of electromechanical systems with a neural net work and frictional loading. National Technical University "Kharkov Polytech-nic Institute". (in Ukrainian).
K., De Jong. (1985). Genetic algorithms: A 10 year perspective. Proceedings of the First International Conference on Genetic Algorithms and Their Appli-cations. 167 - 177.
EC 62290-1:2014, Railway applications – Urban guided transport management and command/control systems – Part 1: System principles and fundamental concepts, MOD.
FRA Safety Data. Retrieved from: https://safetydata.fra.dot.gov/OfficeofSafety/default.aspx
Stan sprav avarijnosti na transporti v Ukrai`ni za 2019 rik. [State of transport accident cases in Ukraine for 2019] Retrieved from: https://mtu.gov.ua/files/bezpeka/%D0%A1%D1%82%D0%B0%D0%BD%20%D0%B0%D0%B2%D0%B0%D1%80%D1%96%D0%B9%D0%BD%D0%BE%D1%81%D1%82%D1%96%20%D0%BD%D0%B0%20%D1%82%D1%80%D0%B0%D0%BD%D1%81%D0%BF%D0%BE%D1%80%D1%82%D1%96%20%20%D0%B7%D0%B0%202019%20%D1%80%D1%96%D0%BA.pdf (in Ukrainian).
Reinhard, Klette. (2014). Concise computer vision: An Introduction into Theory and Algorithms. Series “Undergraduate Topics in Computer Science”. Lon-don, UK, 429. DOI: 10.1007/978-1-4471-6320-6.
Howse, J., Minichino, J. Learning OpenCV 4 comput-er vision with Python 3: Get to grips with tools, tech-niques, and algorithms for computer vision and ma-chine learning, 3rd Edition, Kindle Edition.
Introduction to OpenCV-Python Tutorials. Retrieved from: https://docs.opencv.org/master/d0/de3/tutorial_py_intro.html
Cheng, Y., Maimone, M. W., Matthies, L. (2006). Visual odometry on the Mars exploration rovers – a tool to ensure accurate driving and science imaging. IEEE Robotics & Automation Magazine, 54–62. DOI: 10.1109/MRA.2006.1638016
Takaoka, Y., Kida, Y., Kagami, S., Mizoguchi, H., Kanade, T. (2004). 3D Map building for a humanoid robot by using visual odometry. IEEE International Conference on Systems, Man and Cybernetics, 4444–4449. DOI: 10.1109/ICSMC.2004.1401231
Fernandez, D., Price, A. (2004). Visual odometry for an outdoor mobile robot. IEEE Conference on Ro-botics, Automation and Mechatronics, 816–821. DOI: 10.1109/RAMECH.2004.1438023
Chhaniyara, S., Bunnun, P., Zweiri, Y., Seneviratne, L., Althoefer, K. (2017). Feasibility of velocity esti-mation for all terrain ground vehicles using an optical flow algorithm. ICARA 2006-Third international conference on autonomous robots and agents. DOI: 99511995302346
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Copyright (c) 2023 Yaroslav Kyrylenko, Serhii Senchenko, Bohdan Vorobiov, Liu Khan, Yaroslav Likhno
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