Method for constructing uncertain locomotive simulation model system based on dynamic time series diagram

Abstract

The invention provides a method for constructing an uncertain locomotive simulation model system based on a dynamic time series diagram, the method is characterized by the following steps: step1, a basic dynamics model is utilized to construct a underlying environment model; step 2, a dynamic sequential graph model is introduced, and the dynamic sequential graph model is a state transition graph model based on the Hidden Markov Model; step 3, the uncertain parameter learning of the dynamic sequential graph model which is the learning and training of the Hidden Markov Model is completed; step 4, when the dynamic sequence diagram learning is finished, specific uncertain environment factors are input, the uncertain environmental factors include heavy rain and roller skating, and the uncertain environmental factors are regarded as a observation sequence of the model, the observation sequence of the model can simulate the hidden state sequence and determine how to modify the parameters of the basic physical model so that the basic physical model adapts to the locomotive running under the specific environmental factors. The model is used to train the train environment disturbance parameters and to build a perfect train running environment model with the basic model parameter learning so as to the perfect train running environment model is more suitable for the simulation of changeable locomotive running environment.

Description

technical field [0001] The invention relates to a locomotive control method, in particular to a construction method of an uncertain locomotive simulation model system based on a dynamic sequence diagram. Background technique [0002] Train operation control is a typical multi-objective, multi-constraint, nonlinear complex real-time changing process. Therefore, the train maneuvering problem is a nonlinear constrained dynamic optimization problem. In the actual operation control process of the train, it is necessary to consider the constraints such as the safe speed limit of the train, the saturation characteristics of the traction motor, and the maximum coupling force of the workshop. In order to ensure the safe running of railways and improve the efficiency of transportation, many countries and large economies are actively conducting research on high-speed railways. For safety, efficiency, convenience and other considerations, it is necessary to design a simulation system ...

AI Technical Summary

Problems solved by technology

Therefore, the train maneuvering problem is a nonlinear constrained dynamic optimization problem

[0007] However, the current traditional locomotive predictive control model has the following problems: (1) The locomotive operation scenarios that can be simulated are limited and cannot fully meet the actual automatic driving requirements; It will cause the trailer to have braking force output during the conversion stage from traction to cruising, which increases the energy consumption of the train; (3) Some schemes need to solve the optimization problem online in real time, which will cause a large amount of calculation in the controller design process and hardware implementation costs Advanced problems greatly limit its application range and application occasions

Images