Composite Iterative Learning and Model Reference Adaptive Control for High-Speed Train Speed Tracking

High-speed train speed tracking control is crucial for the efficient operation of modern rail transportation systems. Traditional control methods face challenges in dealing with the complex operating environment encountered by high-speed trains. This paper proposes a new iterative learning control-based model reference adaptive control (ILC-MRAC) fusion control strategy. First, a dynamic reference model is designed and optimized, leading to a hierarchical ILC-MRAC control scheme. Specifically, the upper layer uses MRAC to generate baseline control signals, while the lower layer uses ILC to learn from and compensate for historical errors. A dynamic weight factor is introduced to adapt to non-strictly repetitive conditions. Then, the feedback is added to the reference model to enhance system stability. The stability and convergence of the high-speed trains are analyzed using a newly defined composite energy function. Numerical simulations of high-speed railway trains verify that the proposed control strategy effectively improve speed tracking performance. The maximum tracking error is reduced by 93.33% after 10 iterations, validating the effectiveness and applicability of the proposed strategy method in high-speed train operations.