To improve the performance and riding comfort of tilt-controlled vehicles, we developed a next-generation tilt control system that uses a position detecting system, a novel tilt pattern, high performance tilt actuators and other new technical elements. Under this system, the running train position is detected by curvature collation based on GPS signals and the tilting angle target pattern calculated to optimize the riding comfort evaluation index. The pattern thus created is called the JT pattern, according to which the tilt actuators are electro-hydraulically powered. Running tests have proved that low-frequency vibration causes train motion sickness has decreased as a result of this system.

SUMMARYA track inspection car, which measures the shape of railway tracks (hereafter, rail geometry) while it is running on rails, discretizes the measurement results at nearly fixed spatial intervals. However, the distance between the discretized locations (spatial sampling intervals) may shorten or lengthen locally due to slipping or sliding of the car wheel, and this prevents the sampling locations from aligning with those of a dataset obtained with another measuring run. The authors developed an algorithm for approximately aligning the sampling locations of the measurement datasets obtained with different runs. First, they considered this problem as the selection of the series of data corresponding to each supervised data from a training dataset, which was constructed by interpolation in order to minimize the evaluation function of a number sequence representing data points. Next, they used the maximum likelihood method to identify the unknown parameters contained in the evaluation function. This problem uses two features of the evaluation function. The first is that the evaluation function is minimized by dynamic programming, and the obtained optimum sequence is equivalent to a maximum a posteriori (MAP) estimate in the Bayesian framework. The second is that by converting the evaluation function to a general state space representation, the log likelihood of the model that includes the parameters is obtained by a nonlinear filtering method. Also, to simplify the search for the identification, they devised a parameter search procedure for the parameters in the autoregressive (AR) model.

We have developed a new theory for correcting track irregularities by applying their restored waveforms and carried out experimental levelling and lining work. In this theory, the optimum shifting/lifting values are obtained as solutions for a typical quadratic programming problem. Restrictions on shifting rails are formulated as constraint conditions of the optimization problem. Furthermore, in the case of levelling work, a designed cross level can be realized. From experimental work on ballasted and slab track, the effectiveness of this theory has been confirmed. In the range of long wavelengths up to 50 m, the amplitude of track irregularity has decreased.

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