2021•IFAC-PapersOnLineOpen access

Design of sliding mode observer for the estimation of train car positions and in-train forces

Hiba Fawzia Bouchama, Michaël Defoort, Denis Berdjag, Jimmy Lauber

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Abstract

For the control design of freight trains, the estimation of in-train forces is crucial to avoid safety issues caused by the possible failure of car couplers under excessive stress. However, the in-train forces and the relative positions of adjacent train cars are not directly measurable. To address this issue, this paper designs estimators of train car positions and in-train forces using only locomotive-based measurements. Using the multi-point train model, a cascade structure of robust sliding mode differentiators is developed. It provides finite-time estimation of train car positions and in-train forces. Simulation results illustrate the feasibility of the proposed approach.

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What this paper is about

For the control design of freight trains, the estimation of in-train forces is crucial to avoid safety issues caused by the possible failure of car couplers under excessive stress. However, the in-train forces and the relative positions of adjacent train cars are not directly measurable. To address this issue, this paper designs estimators of train car positions and in-train forces using only locomotive-based measurements. Using the multi-point train model, a cascade structure of robust sliding mode differentiators is developed. It provides finite-time estimation of train car positions and in-train forces. Simulation results illustrate the feasibility of the proposed approach.

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Available abstract

For the control design of freight trains, the estimation of in-train forces is crucial to avoid safety issues caused by the possible failure of car couplers under excessive stress. However, the in-train forces and the relative positions of adjacent train cars are not directly measurable. To address this issue, this paper designs estimators of train car positions and in-train forces using only locomotive-based measurements. Using the multi-point train model, a cascade structure of robust sliding mode differentiators is developed. It provides finite-time estimation of train car positions and in-train forces. Simulation results illustrate the feasibility of the proposed approach.

Key concepts: Train, Estimator, Observer (physics), Cascade, Mode (computer interface), Control theory (sociology), Differentiator, Engineering

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