Heavy-load locomotive adhesion control method and device based on distributed model predictive control

Abstract

The invention provides a heavy-load locomotive adhesion control method based on distributed model predictive control, which comprises the following steps: acquiring operating parameters in the locomotive operating process, and establishing a locomotive kinetic model according to the operating parameters; constructing a creep rate calculation model according to the relationship among the creep rateof the locomotive, the angular speed of wheels and the speed of the locomotive; a distributed state space model is obtained by combining the locomotive dynamic model, the creep rate calculation modeland the vehicle rail model; acquiring real-time traction torque of a corresponding shaft through an MPC adhesion controller arranged at each shaft of the locomotive, and calculating to obtain a predicted traction torque value and a predicted creep rate value through a distributed state space model and a preset cost function according to the real-time traction torque; and the predicted creep ratevalue is compared with a preset creep rate value, and when the predicted creep rate value is equal to the preset creep rate value, the traction torque of each shaft is adjusted according to the predicted traction torque value.

Description

technical field [0001] The invention relates to the field of locomotive traction control, in particular to a heavy-duty locomotive adhesion control method and device based on distributed model predictive control. Background technique [0002] Heavy-haul railway transportation is an effective way to improve railway freight capacity and realize freight cost reduction and efficiency increase. Heavy-haul railways have the characteristics of heavy trains, heavy vehicle axles, high traffic density and large transport volume. The safe and efficient operation of heavy-haul railways requires heavy-haul locomotives to exert greater traction, and the traction of locomotives depends on the adhesion between the wheels and rails. If the traction is greater than the maximum available adhesion between the wheels and rails, the excess traction will accelerate the wheels to form idling. The available adhesive force decreases rapidly, resulting in a series of serious problems such as excessiv...

AI Technical Summary

Problems solved by technology

[0004] The basis of modern control theory is an accurate object parameter model, and the adhesion between the wheel and rail has the characteristics of nonlinearity, time-varying, strong coupling and uncertainty, and it is difficult to obtain an accurate mathematical model, so the control effect is greatly improved in the face of the adhesion control problem. reduce

At present, domestic locomotive adhesion control methods mainly include combined correction methods (such as image 3 As shown), the orthogonal correlation method and the intelligent adhesion control method; among them, the parameters in the combined correction method need a large number of orbital experiments to determine, when the orbital situation is complex, the combined correction method cannot adjust the parameters, and the adaptability is poor

Therefore, without a large number of experiments, the set parameters may not be able to effectively identify the idling trend, and even affect the normal performance of the traction drive performance of the locomotive; while the orthogonal correlation method is to linearize the model of the electric locomotive and observe the frequency of work The scope is very limited, so it has certain limitations; the intelligent adhesion control method needs a large amount of data to train the model and parameters, and the real-time problem in practical application needs to be solved

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