Based On Generalized Predictive Control Locomotive Brake Control Research And Application

In order to meet the ever-increasing demand for the railway transportation, the operation of heavy-haul trains is an effective way to improve transport capacity. Due to the longer train formation and increased weight of the train, train braking is the chief problem to be solved for heavy-haul transport. In order to solve the problem made by nonlinear, time-variation of controlled object and delay of data transmission in the locomotive braking system. The main research works are as follows:For the main controlled object of the system—Brake-cylinder of DK-2 locomotive braking system, which is nonlinear and time-variation, a modeling method based on T-S fuzzy model is adopted. An optimization method based on co-evolutionary genetic algorithm is proposed to optimize parameter of the acquired T-S model. The method predigests structure of the model as well as ensures the accuracy of model.To achieve accurate control of the locomotive braking course which is nonlinear and time-variation, a Generalized Predictive Control (GPC) algorithm based on T-S fuzzy model is proposed. Queuing strategy is introduced in the controller module and actuator module, which uses the multi-model predictive output value and multi-predictive control value to solve the problem of control which is made by data transmission delay in DK-2 locomotive braking system. A improved method is proposed to change the division way of the relative deadline in EDF scheduling algorithm. Data frames are scheduled individually by the improved method according to their importance, which solves the problem which is made by real-time performance of the low priority data frames. The performance of whole system is improved.The method proposed in the paper is proved by digital simulation and practical effect of braking system.