Model-Free Adaptive Predictive Tracking Control for High-Speed Trains Considering Quantization Effects and Denial-of-Service Attacks

In this paper, the problem of model-free adaptive predictive control (MFAPC) under denial-of-service attacks and quantization effects for high-speed trains with unknown models is investigated. Since the system model of the high-speed train is unknown, the data-relational description of a high-speed train system is obtained by using the dynamic linearization technique. Secondly, the challenge of periodic denial-of-service (DoS) attacks in the network channel is considered, and, assuming that the DoS attack obeys the Bernoulli distribution, a model-free adaptive predictive control scheme based on quantized signals is proposed. Then, through rigorous theoretical analyses, it is proven that the tracking error is bounded, and the final bound depends on the desired trajectory. Finally, the correctness of these theoretical analyses is verified through numerical simulation.