Model-Free Adaptive Event-Triggered Predictive Cooperative Control for Multiple Subway Trains Under Data Dropouts

This paper investigates the issue of speed tracking and dynamic headway adjustment for multiple subway trains (MSTs) under data dropouts. First, the nonlinear subway train system is transformed into a disturbance-related full-form dynamic linearization (dFFDL) data model. Second, based on the obtained dFFDL data model and the asynchronous event-triggered mechanism, the model-free adaptive event-triggered predictive cooperative control (MFAETPCC) algorithm is proposed, utilizing only the input/output and disturbance data of MSTs to compensate for data dropouts, reduce the communication and computation burden, and ensure control performance. As a result, theoretical analysis reveals that the speed tracking errors of MSTs are bounded and the distances of adjacent trains are kept within a safe range. Finally, the effectiveness of the proposed MFAETPCC algorithm for MSTs is demonstrated by simulation results.