Optimal transfer coordination for
railway systems
Project members: B. De Schutter, T.J.J. van den Boom
In this project we extend the model predictive control framework
(MPC), which is a very popular controller design method in the process
industry, to railway systems [15,17,18].
Usually MPC uses linear (or nonlinear) discretetime models. However, railway
networks and subway networks cannot adequately be described by such models.
First, we have introduced a modeling framework for railway systems with both
hard and soft connection constraints. A typical example of a hard
connection constraint in a railway context is when a train should give a
guaranteed connection to another train. However, in some cases (e.g., if
there are delays) we could allow a train to depart although not all trains to
which it should give connections according to the schedule have arrived at the
station: if some of these trains have a too large delay, then it is sometimes
better  from a global performance viewpoint  to let the train depart
anyway in order to prevent an accumulation of delays in the network. Of
course, missed connections lead to a penalty due to dissatisfied passengers or
due to compensations that have to be paid. Synchronization constraints that
may be broken (but at a cost) are called soft connection constraints.
We also consider an extra degree of freedom for the control to recover from
delays by letting trains run faster than their nominal speed if necessary. Of
course, this control action will also lead to extra costs (due to increased
energy consumption or faster wear of the material).
Next, we have extended the MPC framework to railway systems while still
retaining the attractive features of conventional MPC. The main aim of the
control is to obtain optimal transfer coordination and/or to recover from
delays in an optimal way by breaking connections and/or letting some trains
run faster than usual (both at a cost). In general the MPC control design
problem for railway systems leads to a nonlinear nonconvex optimization
problem. We have shown that the optimal MPC strategy can be computed using
extended linear complementarity problems or integer programming algorithms.
Other examples of systems with both hard and soft synchronization constraints
for which this approach can be used are subway networks and logistic
operations.
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Up: Traffic and transportation control
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