A map-based model predictive control approach for train operation

TL;DR

This paper introduces a map-based model predictive control method for train operation that anticipates and prevents skidding and sliding caused by rapid changes in traction conditions, enhancing safety and reliability.

Contribution

It develops a novel MPC approach utilizing track maps with local conditions to proactively avoid skidding and sliding during fast traction changes.

Findings

Successfully prevents skidding and sliding in simulations

Handles rapid changes in traction conditions effectively

Improves safety in train operations under variable conditions

Abstract

Trains are a corner stone of public transport and play an important role in daily life. A challenging task in train operation is to avoid skidding and sliding during fast changes of traction conditions, which can, for example, occur due to changing weather conditions, crossings, tunnels or forest entries. The latter depends on local track conditions and can be recorded in a map together with other location-dependent information like speed limits and inclination. In this paper, a model predictive control (MPC) approach is developed. Thanks to the knowledge of future changes of traction conditions, the approach is able to avoid short-term skidding and sliding even under fast changes of traction conditions. In a first step, an optimal reference trajectory is determined by a multiple-shooting approach. In a second step, the reference trajectory is tracked by an MPC setup. The developed method is simulated along a track with fast-changing traction conditions for different scenarios, like changing weather conditions and unexpected delays. In all cases, skidding and sliding is avoided.