Event-triggered neural adaptive cooperative control of multiple trains based on minimum learning parameters

This paper investigates the challenges of event-triggered trajectory tracking control for multiple high-speed train nonlinear systems subject to model uncertainty. First, a neural network-based minimum learning parameter (MLP) method is proposed for multi-train nonlinear systems with model uncertainty and integrated into the controller design, eliminating conventional neural network weight selection. Second, to reduce the communication resource burden, a fixed-threshold event-triggered mechanism is developed. Finally, an event-triggered adaptive tracking control approach based on the MLP method is presented within the sliding mode control framework. Theoretical analysis demonstrates that all closed-loop control signals are uniformly ultimately bounded and that Zeno behaviour is avoided. Simulation results verify the effectiveness of the proposed control scheme.