Data-Driven Distributed Iterative Learning Control for Multiple HSTs Under Independent Weighted Communication and Input Saturation

Cooperative control of multiple high-speed trains (MHSTs) is considered as a pivotal technology for enhancing the safety and efficiency of train groups. This study investigates the problem of distributed iterative learning control for MHSTs under the input saturation, employing a data-driven control strategy. Moreover, a novel independent weights-based communication protocol is developed to improve the coordination efficiency of MHSTs. Firstly, a novel linearization method is utilized to transform the dynamic model of MHSTs into an equivalent linearized model associated with saturated inputs. Secondly, based on this model, we develop both a train-to-train (T2T) communication protocol and a distributed model free adaptive iterative learning control (DMFAILC) scheme that are independent of any specific models or structural information about MHSTs. Subsequently, the overall stability is analyzed using the compression mapping method, and the complete tracking of HSTs and the coordination performance of MHSTs are confirmed through Simulink simulations and real-time StarSim hardware-in-the-loop (HIL) semi-physical platforms. Note to Practitioners—The practical challenges that drive this research on cooperative control of MHSTs are the intricate nature of train models and the inherent instability in train operating conditions. Moreover, potential applications include automatic operation of HSTs and coordination of MHSTs. Specifically, the contributions of this work include: (1) eliminating the need for precise models of HSTs; (2) establishing stability conditions for HSTs under saturation conditions; and (3) enhancing coordination efficiency among MHSTs through an independent weight protocol. However, a limitation of this study is that the theoretical findings have not been validated on real HSTs. To address the current lack of practical implementation of the train cooperative control strategy, further investigations will be conducted to incorporate more realistic constraints, such as those related to traction network limitations and optimal scheduling considerations.