An Adjustment Method for Re-adhesion Control of Electric Rolling Stock Using Particle Swarm Optimization and Its Validation by a Scale Model Railway Vehicle

This paper presents an algorithm for the re-adhesion control of electric rolling stocks. Generally, an engineer manually adjusts parameters used in any re-adhesion control algorithm before a commercial operation. However, it depends heavily on the experience of the engineer. Therefore, developing an automatic adjustment system for re-adhesion control is imperative. To this end, we apply the particle swarm optimization (PSO) algorithm to re-adhesion control. In our approach, a cost function based on the averaged tangential force of an electric bogie is optimized by the PSO algorithm, and the parameters for re-adhesion control are automatically adjusted. In addition, experiments using a scale model railway vehicle are carried out to validate the proposed method.