Application And Research Of Generalized Predictive Controller Optimized By Particle Swarm In ATO

The rail transit is playing more and more important role in the development of national economy, with the continuous development of national economy, the contradiction between the backward track traffic and the fast pace of life is becoming more and more obvious ly. Therefore, the demand for rail transit with large capacity, safety, low energy consumption and comfort increasingly outstanding. In recent years, our country has paid more and more attention to the development of rail transportation, the construction of high-speed railway and high speed train is imminent, at the same time, the increase of the train speed also put forward new requirements and challenges for train drivers. The safe, fast and efficient automatic train operation(ATO) system control method has become one of research hot spots.The thesis studies and researches on the basis of the structure and principle of the system of automatic train operatio n, and the force of the train under different working conditions are analyzed in detail. The energy-saving target curve of optimization is generated by the comprehensive of setted line model, CRH2 train parameters and energy-saving train control strategy, the train's main working condition is the combination of constant speed and slither control strategy, mak ing the design more accord with the actual operation of the train, avoiding the train's idealized energy-saving control strategy of train speed constant or idle running traction cruise in the interval, the control strategy is hard to use in the actual train driving. In this thesis, to further demonstrate the feasibility and effective of the energy- saving target curve of optimization by calculating the energy consumption in the whole operation process.Secondly, the thesis make further research on the theory of implicit generalized predicttive control(IGPC) algorithm and chaotic particle swarm optimization(CPSO) algorithm. Due to the gradient optimization of IGPC algorithm doesn't get global optimal solution, this thesis optimize the rolling optimization processes of IGPC by CPSO algorithm, mak ing the optimization results more conducive to the control of the train. A nd Designing the CPSO-IGPC integrated intelligent controller which is suitable for the ATO system, mak ing the running of the train more energy-saving, on time and smoothly;Finally, using the designed target curve as input for the controller, the discrete dynamic model of the train operation as forecast model, with the CPSO-IGPC integrated intelligent controller, based on the MATLAB software as the platform to carry on the simulation, making analysis and compare on the simulation results.After a comprehensive analysis, the energy consumption of generated optimization of energy-saving target curve is lesser than traditional constant speed energy-saving target curve 473.53MJ; In the process of simulation, CPSO-IGPC controller is better than PSO-IGPC controller in follow timely, smaller overshoot and better control performance.