Internal pressure control for high-speed trains based on condition matching and performance iteration

Abstract

When a high-speed train passes through tunnels, tunnel pressure waves will cause pressure fluctuation inside carriage. Traditional control strategy of shutting down air ducts for a fixed period may fail to consider both riding comfort and air quality. The similarity of tunnel pressure waves when the same train passes through the same tunnel provides a possibility to solve the problem by iterative learning control (ILC) algorithm. However, the varying amplitude and scale limit the application of conventional ILC. Thus in each iteration, the control inputs of the nearest condition in the historical database will be matched and applied to the control process, after which the control error will be gained and then the control inputs will be updated by the error. Next, the performance will be evaluated to refresh the database to make the control inputs in the database to be optimal. Feasibility analysis and simulations show the feasibility of controlling the fluctuation inside the carriage by matching the database records.