Robust Fuzzy Predictive Control for Automatic Train Regulation in High-Frequency Metro Lines

This paper addresses the robust automatic train regulation problem in high-frequency metro lines with fuzzy passenger arrival rate. Due to the uncertainty of passenger demand, the passenger arrival rate is assumed to be represented by fuzzy variables. A nonlinear state-space model is formulated to describe the characteristic of metro train operation. To satisfy the real-time requirement of train regulation, a fuzzy constrained predictive control approach is designed to optimize a cost function at each decision epoch subject to safety constraints on the control input. Based on the Lyapunov stability theory and model predictive control method, sufficient conditions for the existence of corresponding state feedback control law are given in a set of linear matrix inequalities. Moreover, for reducing delays caused by the uncertain disturbance, the robust train regulation strategy is designed to guarantee that the practical train timetable tracks the nominal one with respect to certain disturbance attenuation level. The effectiveness of the proposed approach is validated by a number of experiments under real running circumstances of Beijing Metro Yizhuang Line of China.