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Ride comfort and safety of vehicles can be increased by active suspension systems. A problem is the detection of disturbances which can generally not be measured until they impact the chassis. Provided guidance and disturbance are known in advance, a controller can use this information to achieve considerably improved behavior. This paper presents an approach in which railway vehicles coupled in a network, in repeated runs over the same track section, learn a disturbance compensation that can almost entirely compensate for stationary disturbances, i.e., disturbances that occur at the same spot in equal measure. Here information on the respective track section is sampled, stored locally at the track, and retrieved by the succeeding vehicle which will use them for an improved compensation for the occurring disturbances and again store information there. This iterative procedure results in an optimal compensation. The algorithm is described and criteria for its design are derived from digital control theory. The procedure was implemented on a testbed for a semi-vehicle with three degrees of freedom. The results of the measurements are displayed and evaluated in this paper.

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