An Adjustment Method for Re‐Adhesion Control of Electric Rolling Stock Using Particle Swarm Optimization and its Validation by a Scale Model Railway Vehicle

ABSTRACT 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.