Observer‐Based Model‐Free Iterative Learning of Fault‐Tolerant Control for Subway Train Velocity Tracking Systems

ABSTRACT This article proposes a model‐free adaptive iterative learning fault‐tolerant control (MFAilFTC) scheme for subway train velocity tracking, with actuator faults and over‐velocity constrains. First, the train dynamics is transformed into an iteration‐related dynamic linearization data model. Compared to previous results, the learning strategy we propose shows enhanced performance in trajectory tracking by utilizing the partial form dynamic linearization technique, which significantly accelerates the iteration process. Next, to deal with non‐repetitive uncertainties, we present an iterative output observer to estimate inaccurate outputs ruined by non‐repetitive uncertainties. With the introduction of iterative learning observer (ILO), the tracking accuracy and the ability to suppress non‐repetitive uncertainties are improved. Additionally, the inclusion of the tracking error integral term in the ILO enhances the convergence speed. An over‐velocity constrains mechanism is developed to ensure that the train operates within a safe velocity range. Through rigorous mathematical analysis, it is demonstrated that the MFAilFTC method with the over‐velocity constrains mechanism ensures the train velocity tracking error converges along the iteration axis, indicating safe and reliable operation. Simulation results further validate the effectiveness of the proposed method.