Transactions on Machine Intelligence

Transactions on Machine Intelligence

Dynamic Modeling of Trains for Application in Railway Transportation Automation Systems

Document Type : Original Article

Authors
F. Z. Amirhosseini 1
B. Ghorbani Vaghei 2
H. Bolandi 3
1 Master's Graduate, Control Group, Faculty of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
2 Assistant Professor, Control and Railway Signal Group, Faculty of Railway Engineering, Iran University of Science and Technology, Tehran, Iran
3 Professor, Control Group, Faculty of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
10.47176/TMI.2024.23
Abstract
The current railway transportation industry utilizes automation across various domains such as safety, stability, motion control, and train scheduling to optimize the use of railway resources and facilities. A prerequisite for these automation systems is the ability to predict and simulate train motion, which requires accurate train motion modeling. Factors such as air friction, the complexity of rail routes, the interaction forces between wagons, dynamics of force generation in actuators, and mechanical part friction result in nonlinear equations for train motion dynamics. Experimental values for some of these factors are uncertain due to wear and structural changes in components, while others are unmeasurable, complicating control conditions. This paper describes multi-particle and single-particle dynamic models of train motion. For practical operation, time-varying or unknown parameters in these equations are identified using a recursive least squares algorithm, and the estimated values are applied in a sliding mode control signal to compensate for baseline resistance and route disturbances. The designed sliding mode controller at the core of this system mitigates the effects of uncertainties and accurately tracks the desired speed-location profile. Simulation results presented in this paper demonstrate precise parameter estimation along with favorable tracking outcomes for the speed-location characteristic.
Keywords
Automation
Railway Transportation
Identification
Recursive Least Squares Algorithm
Sliding Mode Control
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Transactions on Machine Intelligence
Volume 7, Issue 1
Winter 2024
Pages 23-37

  • Receive Date 11 December 2023
  • Revise Date 16 February 2024
  • Accept Date 09 March 2024