Data-Driven Distributed Learning Control for High-Speed Trains Considering Quantization Effects and Measurement Bias

The advanced train-to-train (T2T) communication technology, equipped with multiple high-speed trains (MHSTs), has the potential to enable train groups to maintain a stable T2T distance and achieve consensus tracking of MHSTs, thereby enhancing operational safety and efficiency. This study focuses on the data-driven distributed control issue of MHSTs considering quantization effects and measurement bias, employing a learning approach. Firstly, an equivalent linearization model of MHSTs and a transmission model accounting for sensor bias are constructed. Subsequently, a distributed model free adaptive iterative learning control (MFAILC) scheme using quantized signals is proposed. We then prove that the tracking error under the quantizer-based MFAILC is uniformly ultimately bounded, followed by further investigation on the impact of uniform quantizers. Finally, through a series of test conducted on the StarSim hardware-in-loop (HIL) semi-physical platform using quantified indicators, both the learning advantages of MFAILC and the influence of the quantization mechanism and measurement bias on MHSTs are verified.