An iterative learning-based automatic speed control method for subway trains

Abstract

The invention discloses an iterative learning-based subway train automatic running speed control method. The method comprises the following steps of: 1, establishing a train running dynamic model for an urban railway transit train automatic running speed control system; 2, automatically adjusting a learning gain through output errors and modified functions in an iteration process, and using the learning gain to update the input of a speed controller; and 3, in order to ensure the robustness, for the initial-state errors, of an algorithm, learning an iteration initial state while a controlled quantity is learnt to ensure that the system can restrain to an expected track under any initial condition without requiring the iteration initial state to be accurately located on an expected initial state, so as to finally realize the accurate tracking, for a target speed curve and a target displacement curve, of the train. According to the method, a learning gain initial value and a system state and tracing error of the last iteration are utilized to correct the system initial state of the current iteration, and an iteration initial state correction algorithm is given, so that the convergence, for any system initial state, of a law of learning is ensured.

Description

technical field [0001] The invention relates to a method for controlling the automatic running speed of subway trains based on iterative learning, belonging to the technical field of urban rail traffic control. Background technique [0002] Urban rail transit has the characteristics of fast speed, large transportation volume, less pollution, low energy consumption, and good comfort. It is an important part of urban public transportation. With the rapid development of cities, the construction of rail transit systems has become a large and medium-sized It is one of the important measures for cities to alleviate traffic congestion. The operating characteristics of rail transit determine its extremely high requirements for safety, comfort and energy saving. Compared with non-automated trains, the automatic driving of trains can improve the average speed of train travel and the accuracy of parking on the premise of ensuring safe operation. The automatic driving of trains has bec...

AI Technical Summary

Problems solved by technology

Due to the existence of external noise disturbances, internal nonlinear disturbances, and uncertainties in system parameters during the entire operation of the train, the traditional PID control has limitations that are difficult to overcome. When controlling the speed, there are too many gear switching times, which is not conducive to the stability of the train. running, reducing ride comfort and increasing energy consumption

Most of the research on train operation control methods based on optimization theory selects energy consumption or time as its single performance index, while the train operation process is a multi-objective optimization problem, which needs to meet the requirements of safety, punctuality, comfort, energy saving and other indicators at the same time. It is difficult to obtain a satisfactory control effect if a certain index is optimal as a controller design constraint

The development of intelligent control provides a feasible method for realizing high-performance train automatic control, but there are still many problems to be solved, such as the high-performance on-board fuzzy control system needs to rely on a large amount of known driving experience, and it lacks transplantation on different models and different lines For example, the convergence speed of the neural network control method needs to be improved, and it may fall into a local optimum, and it needs to be further developed to apply it to the actual system.

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