Abstract
Model predictive control (MPC) has been widely used in the process industry (e.g. refining and chemical industries). In recent years, it has been applied to automatic train regulation (ATR) as well for its advantages in large-scale complex system control. ATR plays an important role in rail transportation as it can improve efficiency and ensure the service quality of train operations. There is a need for an in-depth understanding of MPC-based ATR methods. This paper aims to summarize the recent works on applications of MPC to ATR and to explore the possible research directions in the future. In particular, we review representative methods based on centralized model predictive control (CMPC) and distributed model predictive control (DMPC).
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