Dynamic Modeling of Trains for Application in Railway Transportation Automation Systems

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.

کلیدواژه ها:

automation ، Railway transportation ، Identification ، Recursive Least Squares Algorithm ، Sliding mode control