When a high-speed train repeatedly passes through a tunnel, tunnel pressure waves induce pressure fluctuation inside the carriage. The traditional passive control strategy of closing the air ducts for a fixed period may fail to meet the requirements of pressure comfort and air quality. The similar properties of morphologically-similar tunnel pressure waves provide a new perspective for performing quasi-periodic repetitive tasks by iterative learning control (ILC) algorithm. In order to break the limitation of strict repeatability of tracking targets in traditional ILC algorithm, this paper proposes a novel ILC based on condition and performance matching algorithm. By matching the current working condition and expected performance with the condition and performance of the historical database, and processing the matched control input with a variable time scale, the optimal control input can be obtained. The control input and the historical database can be updated based on the error and performance after control. The simulation results show that, compared with the uncontrolled, traditional control and traditional open-loop ILC, the proposed algorithm can effectively suppress the internal pressure fluctuation under morphologically-similar tunnel pressure waves, and has higher control accuracy and faster convergence speed.
To explore the influence of dynamic characteristics of sealing gaps on the pressure fluctuation in train during passing through a tunnel, static air tightness test is conducted to calculate the equivalent leakage area of gaps and to investigate the dynamic characteristic with the variation of pressure difference. The dynamic leakage process of gaps when passing through a tunnel is simulated numerically and the impact of gaps on leakage flow and internal pressure is analyzed. It is found that 1) the equivalent leakage area of gaps has nonlinear characteristics with different pressure difference, 2) when a train under operation condition with positive pressure, the air is more likely to flow out through the holes under positive pressure, 3) the external section of the hole is obviously impacted by the incoming flow, and the stress condition is worse than other parts, 4) the maximum leakage flow of dynamic equivalent leakage hole, the one second change rate, the three second change rate and the amplitude of the pressure inside vehicle under the model are equal to 43.33%, 55.73%, 45.62% and 48.60% of the fixed equivalent leakage hole, respectively. This study can provide a reference for the determination of equivalent leakage area of high-speed train and the optimization of sealing technology.
Conventional iterative learning control (ILC) algorithm is designed for tasks with strictly repetitive characteristics, which is difficult to be satisfied in industrial applications. Thus, the applicational range of ILC is limited. Aiming at solving the problem of controlling the output to a certain range under the morphologically-similar but varying-scale interferences, finitely historical conditions are selected, of which a measurable and time-scale-concerned parameter is selected. Then, some clusters of the conditions are formulated, in which the probability distributions are initialised by the known conditions while the corresponding control inputs are initialised based on the conventional ILC algorithm. Next, based on the ideology of expectation maximization (EM) algorithm, the control inputs and the probability distributions are iteratively updated to make the ILC for suppressing the morphologically-similar and varying-scale interferences possible: (1) the control input is calculated by the prior-probability that current conditions belong to a cluster and the corresponding control inputs for the cluster; (2) the control input will be updated by the prior-probability and the control error; (3) the posterior-probability will be calculated and the cluster will be refreshed. Finally, probability distribution and corresponding control inputs will be gained for the novel ILC algorithm after enough iterations to make the error of the control smaller. By convergence analysis and an applicational simulation, the adaptivity and feasibility of the novel ILC algorithm have been proved.
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