Train To Adjust The Parameters Of The Energy-saving Control Problem And The Numerical Calculation

This thesis mainly deals with the following two aspects of the train control problem: adjusting parameters and implementing numerical calculations. Within a given time a train travels from one station to the next along level track or non-zero gradient track, it is desirable to find a feasible control strategy that minimizes fuel consumption. We consider a train model with discrete control. For a prescribed sequence of fuel supply rates, it has been shown that a strategy of optimal type depends on two real-number parameters, A. and n .The parameter n determines the hold speed for the journey, and the parameter A determines the sizes of the coast-power pairs used to approximate speedholding. These parameters determine the switching points and ultimately determine the distance traveled by the train and the time taken for the journey. Although some algorithms of adjusting parameters have been given, there are still a lot to be improved.On the basis of analyzing the procedure of fundamental calculation,we find the reasons why the procedure of fundamental calculation fail when Aλand μ are inputted to the procedure, and we present an algorithm to solve the problem. Furthermore, a self-adaptive procedure , which candetermine the nature of a strategy: fast or slow, is given based on the procedure of fundamental calculation. When the self-adaptive procedure is combined with the Genetic Algorithm (GA), a complete algorithm of adjusting parameters and implementing calculation is formed. This algorithm is suitable for both level track and non-zero gradient track. Lots of simulating experiments have been proved that the algorithm is quite effective.A constructive algorithm, based on the iterative sequence of parameters and the procedure of fundamental calculation , is presented in this thesis. It should be pointed out that the constructive algorithm does not depend on the nature of the strategy determined by parameters, and that the constructive algorithm is also suitable for both level track and non-zero gradient track theoretically. Besides the two algorithms given above, we also give a algorithm that is based on Simulated Annealing Algorithm (SAA), although for the effective of algorithms it is mostly suitable for level track .