Collaborative Operation Control Method for Virtual Train Formation with Unknown Train Model Parameters

As passenger flow in urban rail transit increases and the design capacity of existing train control technology tends to become saturated, virtual train formation technology has emerged as one of the research focuses of new urban rail train control technology. This is due to its ability to significantly improve the capacity of existing lines and effectively solve the problem of passenger flow imbalance during specific time spans. During train operation, passenger boarding and alighting may change the train mass and basic resistance parameters, resulting in changes in train characteristics. This can have a significant impact on the safety and stability of virtual formation operation. To solve these problems, this paper designs a distributed adaptive sliding mode controller based on a leader-follower formation control model and fixed-time headway formation interval control strategy. This controller can overcome various uncertainties in the train model parameters and environment, ensuring the consistency, robustness, and stability of virtual train formation operation. According to simulation results, it is concluded that the designed controller can estimate unknown parameters in the train model and dynamically adjust parameters in the controller according to the control strategy, ensuring collaborative control of train formation with unknown model parameters.