Anti-snaking damper control method and system based on model prediction

Abstract

The invention discloses an anti-snaking damper control method and system based on model prediction, and the method comprises the steps: building a three-dimensional multi-degree-of-freedom train dynamics model and a model prediction controller based on the connection mode and relative motion relation of all parts of a train, an anti-snaking damper of the train is equivalent to an additional stiffness spring, a node stiffness spring and a structural damper; linear discretization is carried out on the three-dimensional multi-degree-of-freedom train dynamic model, a transverse multi-degree-of-freedom train state prediction model is constructed, and the transverse multi-degree-of-freedom comprises the transverse displacement degree-of-freedom of transverse vibration of a train, the degree-of-freedom of head shaking motion and the degree-of-freedom of side rolling motion; based on the transverse multi-degree-of-freedom train state prediction model and the current state information of the train, a model prediction controller is used for predicting the vibration behavior of the train, and control damping force needed by train operation is obtained; and a vibration reduction control instruction is sent to the structural damping of the additional stiffness spring and the node stiffness spring based on the control damping force, so that the vibration behavior of the train is restrained.

Description

Technical Field

[0001] The present invention relates to the technical field of train vibration reduction control, and in particular to an anti-snaking damper control method and system based on model prediction. Background Art

[0002] With the rapid development of trains, passenger comfort has become an important indicator for evaluating vibration reduction technology. In order to improve passenger comfort experience and driving safety, how to more effectively suppress train vibration has become a hot research topic.

[0003] Traditional passive suspension has reached its limit in terms of improving operational stability, curve negotiability and smoothness. In addition, due to the non-adjustable nature of passive suspension parameters, when the input excitation or external interference exceeds the passive suspension adjustment range, the passive suspension system will cause train instability due to insufficient vibration attenuation ability. In other words, the passive suspension system still has shortcomings in dealing with vibration risks, the vibration reduction effect on the train is poor, and the real-time vibration reduction performance is not ideal. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides an anti-snaking damper control method and system based on model prediction, constructs a prediction model and a model prediction controller, and equivalently simplifies the anti-snaking damper. The model prediction controller is used to predict the future vibration behavior of the train, and based on the prediction results, a vibration reduction control signal is sent to the anti-snaking damper to suppress the train vibration, thereby improving the smoothness of the train operation.

[0005] The first object of the present invention is to provide an anti-snaking damper control method based on model prediction, comprising:

[0006] Based on the connection mode and relative motion relationship of various train components, a three-dimensional multi-degree-of-freedom train dynamics model and model predictive controller are established. The train's anti-snaking damper is equivalent to an additional stiffness spring, a node stiffness spring, and structural damping.

[0007] Linearly discretizing the three-dimensional multi-degree-of-freedom train dynamics model to construct a lateral multi-degree-of-freedom train state prediction model, wherein the lateral multi-degree-of-freedom includes the lateral displacement degree of freedom of the train lateral vibration, the yaw motion degree of freedom, and the roll motion degree of freedom;

[0008] Based on the lateral multi-degree-of-freedom train state prediction model and the current state information of the train, a model predictive controller is used to predict the vibration behavior of the train to obtain the control damping force required for train operation;

[0009] Based on the controlled damping force, a vibration reduction control instruction is issued to the additional stiffness spring, the node stiffness spring and the structural damping to suppress the vibration behavior of the train.

[0010] As a further improvement of the present invention, a model predictive controller is used to predict the vibration behavior of the train to obtain the control damping force required for train operation, including:

[0011] The objective function is designed with the lateral vibration velocity, yaw angular velocity and roll angular velocity of the train body as the control targets, and constraints are set for the lateral vibration velocity, yaw angular velocity and roll angular velocity of the train body;

[0012] Based on the current state information and constraints of the train, the objective function is solved to predict the vibration behavior of the train, and the control damping force is calculated based on the prediction results.

[0013] As a further improvement of the present invention, the method further includes: analyzing the control damping force by a frequency domain analysis method or a time domain analysis method, and optimizing the vibration reduction control instruction based on the analysis result to suppress the vibration behavior of the train.

[0014] As a further improvement of the present invention, the three-dimensional multi-degree-of-freedom train dynamics model includes a car body, a frame, a hollow axle, a wheelset and suspension elements, and the suspension elements include a primary suspension and a secondary suspension; wherein, the primary suspension includes a first vertical shock absorber, a primary spring and an axle box pull rod, and the secondary suspension includes a lateral shock absorber symmetrically distributed at the end of the frame, an anti-snaking shock absorber asymmetrically distributed on the side beams of the frame, and a second vertical shock absorber symmetrically distributed on both sides of the frame.

[0015] As a further improvement of the present invention, the dynamic equations of the three-dimensional multi-degree-of-freedom train dynamics model are as follows:

[0016]

[0017] Among them, M represents the mass coefficient matrix; K represents the stiffness coefficient matrix; C represents the damping coefficient matrix; Y L Represents a column vector containing multiple motion state variables of the train; y b 、y t1 、y t2 、y w1 、y w2 、y w3 、y w4 Respectively represent the lateral displacement of the train body, front bogie, rear bogie, left front wheel pair, right front wheel pair, left rear wheel pair, and right rear wheel pair; Respectively represent the shaking motion of the train body, front bogie, rear bogie, left front wheel pair, right front wheel pair, left rear wheel pair, and right rear wheel pair; ρ b , ρ t1 , ρ t2 Represent the rolling motion of the train body, front bogie and rear bogie respectively; Represents Y LThe first-order derivative vector with respect to time, i.e., the velocity vector of each state variable; Represents Y L The second-order derivative vector with respect to time is the acceleration vector of each state variable; TF represents the external force vector, which includes various external forces acting on the train.

[0018] As a further improvement of the present invention, the state space expression of the lateral multi-degree-of-freedom train state prediction model is as follows:

[0019]

[0020]

[0021] Among them, u(t) represents the control quantity; Y(t) represents the state quantity; It is the derivative of the state quantity Y(t) with respect to time t; Z(t) represents the predicted state quantity; C represents the mapping matrix.

[0022] As a further improvement of the present invention, the connection mode and relative motion relationship of the train are determined based on the topological relationship of the train, and the topological relationship of the train is the spatial layout and connection structure between the various components of the train.

[0023] The second object of the present invention is to provide an anti-snaking shock absorber control system based on model prediction, which is used to implement the above method, including a model building unit, a model prediction unit and a model control unit;

[0024] The model building unit establishes a three-dimensional multi-degree-of-freedom train dynamics model and model predictive controller based on the connection mode and relative motion relationship of various train components. The anti-snaking damper of the train is equivalent to an additional stiffness spring, a node stiffness spring, and a structural damper.

[0025] a model prediction unit, configured to linearly discretize a three-dimensional multi-degree-of-freedom train dynamics model to construct a lateral multi-degree-of-freedom train state prediction model; and based on the lateral multi-degree-of-freedom train state prediction model and the train's current state information, to use a model predictive controller to predict the train's vibration behavior to obtain the control damping force required for train operation; wherein the lateral multi-degree-of-freedom includes the lateral displacement degree of freedom, the yaw motion degree of freedom, and the roll motion degree of freedom of the train's lateral vibration;

[0026] The model control unit issues a vibration reduction control instruction to the additional stiffness spring, the node stiffness spring and the structural damping based on the control damping force to suppress the vibration behavior of the train.

[0027] The third object of the present invention is to provide an electronic device, comprising at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the program is executed by the processing unit, the processing unit executes the above method.

[0028] A fourth object of the present invention is to provide a storage medium storing a computer program executable by an electronic device, wherein when the program runs on the electronic device, the electronic device executes the above method.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The anti-snaking damper is simplified into an equivalent combination of three components: an additional stiffness spring, a node stiffness spring, and structural damping. A model predictive controller is used to predict the future vibration behavior of the train and calculate the control damping force required for train operation. Based on the control damping force, a vibration reduction control signal is sent to the anti-snaking damper to suppress train vibration, thereby improving the smoothness of train operation and enhancing passenger comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a flow chart of the method;

[0032] Figure 2 It is the front view of the train dynamics model in the running direction;

[0033] Figure 3 It is a top view of the train dynamics model;

[0034] Figure 4 Schematic diagram of the train dynamics model;

[0035] Figure 5 Schematic diagram of the model predictive controller;

[0036] Figure 6 This is the control block diagram of the model predictive controller;

[0037] Figure 7 This is a comparison of the time domain response curves of the train at a speed of 150km / h;

[0038] Figure 8 The comparison diagram of the time domain response curve of the train at a speed of 200km / h;

[0039] Figure 9 This is a comparison diagram of the time domain response curve of the train at a speed of 250km / h;

[0040] Figure 10 The comparison diagram of the time domain response curve of the train at a speed of 300km / h;

[0041] Figure 11 A comparison chart of the lateral comfort index of train under time domain response;

[0042] Figure 12 The comparison diagram of the frequency domain response curve of the train at a speed of 150km / h;

[0043] Figure 13 The comparison diagram of the frequency domain response curve of the train at a speed of 200km / h;

[0044] Figure 14 The comparison diagram of the frequency domain response curve of the train at a speed of 250km / h;

[0045] Figure 15 The comparison diagram of the frequency domain response curve of the train at a speed of 300km / h;

[0046] Figure 16 A comparison chart of train lateral stability indicators in frequency domain response. DETAILED DESCRIPTION

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0048] The present invention will be described in further detail below with reference to the accompanying drawings:

[0049] With the development of railways, the demand for passenger comfort and safety has increased. Due to the characteristic of non-adjustable passive suspension parameters in related technologies, when the input excitation or external interference exceeds the adjustment range of the passive suspension, the passive suspension system will cause train instability due to insufficient vibration attenuation ability. In other words, the passive suspension system is still insufficient in dealing with vibration risks, the vibration reduction effect on the train is poor, and the real-time vibration reduction is not ideal.

[0050] Therefore, this embodiment provides an anti-snaking shock absorber control method based on model prediction, the method flow is as follows: Figure 1 As shown, the method includes:

[0051] Based on the connection mode and relative motion relationship of each train component, a three-dimensional multi-degree-of-freedom train dynamics model and model predictive controller are established, and the train's anti-snaking shock absorber is equivalent to an additional stiffness spring, a node stiffness spring and a structural damper; Among them, the connection mode and relative motion relationship of the train are determined based on the topological relationship of the train. The three-dimensional multi-degree-of-freedom train dynamics model is as follows Figures 2-4As shown, the structure of the model predictive controller is as follows Figure 5 As shown;

[0052] Linearly discretizing the three-dimensional multi-degree-of-freedom train dynamics model to construct a lateral multi-degree-of-freedom train state prediction model, wherein the lateral multi-degree-of-freedom includes the lateral displacement degree of freedom of the train lateral vibration, the yaw motion degree of freedom, and the roll motion degree of freedom;

[0053] Based on the lateral multi-degree-of-freedom train state prediction model and the current state information of the train, the model predictive controller is used to predict the vibration behavior of the train to obtain the control damping force required for train operation; the prediction process includes: designing the objective function with the lateral vibration speed, yaw angular velocity and roll angular velocity of the train body as the control target, and setting constraints for the lateral vibration speed, yaw angular velocity and roll angular velocity of the train body; solving the objective function based on the current state information of the train and the constraints to predict the vibration behavior of the train, and calculating the control damping force based on the prediction results; the control block diagram of the model predictive controller is shown in the figure below. Figure 6 shown.

[0054] Based on the controlled damping force, a vibration reduction control instruction is issued to the additional stiffness spring, the node stiffness spring and the structural damping to suppress the vibration behavior of the train.

[0055] When a train runs on a track, it is subject to vibration interference from all directions, making it difficult to fully simulate all the details of the real situation. For a state prediction model, it is sufficient to describe the train's dynamic behavior, so the model can be appropriately simplified. In addition, the complexity of the model predictive controller is significantly positively correlated with the difficulty of constructing the train's anti-snaking and vibration reduction prediction model. Simplifying the state prediction model can improve the accuracy of the model predictive controller. Based on the above analysis, the train state prediction model only considers the lateral translation, swaying motion, and rolling motion of the front and rear bogies and the car body caused by track irregularities. In other words, a lateral multi-degree-of-freedom train state prediction model is constructed, including the lateral translation, swaying motion, and rolling motion degrees of freedom of the train's lateral vibration.

[0056] The three-dimensional multi-degree-of-freedom train dynamics model includes the car body, frame, hollow axle, wheelset and suspension elements. The suspension elements include primary suspension and secondary suspension. Among them, the primary suspension includes the first vertical shock absorber, primary spring and axle box tie rod. The secondary suspension includes lateral shock absorbers symmetrically distributed at the ends of the frame, anti-snaking shock absorbers asymmetrically distributed on the side beams of the frame, and second vertical shock absorbers symmetrically distributed on both sides of the frame.

[0057] Specifically, a topological structure was constructed based on multibody dynamics theory, and a train dynamics model was established using simulation software such as Simulink. The train dynamics model includes a carbody (one), a frame (two), four hollow axles, four wheelsets (four), and various suspension components, including primary and secondary suspension. The secondary suspension between the carbody and the frame consists of two lateral dampers symmetrically distributed at the frame ends, two anti-snaking dampers asymmetrically distributed on the bogie side beams, and two secondary vertical dampers and high-coil springs (two sets per side) symmetrically distributed on both sides of the bogie. The primary suspension connecting the wheelset and the frame consists of four primary vertical dampers, four sets of primary springs, and axlebox tie rods. A transverse stop device is installed between the carbody and the frame, its main function being to limit lateral displacement of the carbody and thus ensure train operational safety. With these settings, the train dynamics model has a total of seven rigid bodies and 17 degrees of freedom.

[0058] Anti-snaking dampers are arranged between the train bogie and the car body to suppress snaking motion by reducing the shaking motion of the bogie relative to the car body. Active control of the train's anti-snaking dampers can mitigate and suppress lateral impact and vibration on the vehicle, ensuring that the vehicle has sufficient lateral stability to ensure operating quality and ride comfort.

[0059] The anti-snaking damper is simplified into an equivalent combination of three components: an additional stiffness spring, a node stiffness spring, and structural damping. When performing active control based on model prediction, the anti-snaking damper acts as an actuator. The model predictive controller calculates the control damping force required for the train to run smoothly through a control algorithm, and issues control commands to suppress the train's lateral impact, thereby improving the vehicle's lateral stability.

[0060] To study train vibration, three mutually perpendicular coordinate axes—x, y, and z—can be established to correspond to the train's running direction, horizontal direction, and vertical direction, respectively. Train motion has six independent modes of motion: linear motion around the x, y, and z axes (i.e., floating, stretching, and lateral vibration) and rotational motion (nodding, shaking, and rolling vibration). The lateral vibration of a train is caused by lateral translation and shaking motion in the xoy plane, as well as rolling motion around the x-axis. To simplify calculations and improve the accuracy of the model predictive controller, only the three degrees of freedom (lateral translation, shaking, and rolling) that affect the lateral vibration of the train body are considered when establishing a lateral multi-degree-of-freedom train state prediction model.

[0061] Furthermore, after the control damping force is calculated, the control damping force is analyzed by frequency domain analysis or time domain analysis, and the vibration reduction control instructions issued to the additional stiffness spring, the node stiffness spring and the structural damping are optimized based on the analysis results.

[0062] The above method adopts the method of prediction before control, that is, first predicting the future lateral output of the train to determine the optimal control action at the current moment. It can more accurately adjust the additional stiffness spring, node stiffness spring and structural damping, thereby improving the train's driving performance and stability.

[0063] Specifically, the linear wheel-rail relationship is adopted, and the lateral motion equation of a single wheelset is obtained under the simultaneous excitation of track direction and horizontal irregularities. Based on the lateral motion equation of a single wheelset, the influence coefficient method is used to establish the train dynamics model, and the Matlab / Symbol module is used to compile the corresponding program for generating symbolic dynamic equations, and finally the train dynamics model is obtained.

[0064] The dynamic equations of the train dynamics model are as follows:

[0065]

[0066]

[0067]

[0068] Among them, M represents the mass coefficient matrix; K represents the stiffness coefficient matrix; C represents the damping coefficient matrix; Y L Represents a column vector containing multiple motion state variables of the train; y b 、y t1 、y t2 、y w1 、y w2 、y w3 、y w4 Respectively represent the lateral displacement of the train body, front bogie, rear bogie, left front wheel pair, right front wheel pair, left rear wheel pair, and right rear wheel pair; Respectively represent the shaking motion of the train body, front bogie, rear bogie, left front wheel pair, right front wheel pair, left rear wheel pair, and right rear wheel pair; ρ b , ρ t1 , ρ t2 Represent the rolling motion of the train body, front bogie and rear bogie respectively; Represents Y L The first-order derivative vector with respect to time, i.e., the velocity vector of each state variable; Represents Y L The second-order derivative vector with respect to time is the acceleration vector of each state variable; TF represents the external force vector, which includes various external forces acting on the train.

[0069] Furthermore, the dynamic equation of the train dynamics model can also be transformed into the form of a state equation, which is:

[0070]

[0071] In state space, both the current state and the future evolution of the model are explicitly represented by a set of equations.

[0072] Based on the state equation of the above dynamic equation, the state space expression of the model is constructed:

[0073]

[0074] Z=c(Y)

[0075] Among them, the state quantity

[0076] Control quantity u=[f sb1 f sb2 f sb3 f sb4 ]′,

[0077] Taylor expansion is performed on the above expression at any point x, retaining only the first-order terms and ignoring the higher-order terms. The state space expression of the obtained train state prediction model is as follows:

[0078]

[0079] Z(t)=CY(t)

[0080]

[0081] Verification experiment:

[0082] To verify the lateral vibration reduction effect of trains under the control strategy of the model predictive controller (MPC), a six-level spectrum was selected as the track irregularity excitation. The anti-snaking damper active control system and the train passive control system built in Simulink were operated at speeds of 150 km / h, 200 km / h, 250 km / h, and 300 km / h, respectively, and a comparative analysis was performed.

[0083] Use time domain analysis method to verify:

[0084] The time domain response curve of the vehicle body angular acceleration of the power-type centralized EMU under MPC control and passive control is obtained. Figure 7 This is a comparison of the time domain response curves of the train at a speed of 150km / h. Figure 8 This is a comparison of the time domain response curves of the train at a speed of 200km / h. Figure 9 This is a comparison of the time domain response curves of the train at a speed of 250km / h. Figure 10 The comparison diagram of the time domain response curve of the train at a speed of 300km / h.

[0085] An analysis of the time domain curves of the train under different operating speed conditions shows that the amplitude of the time domain curve using the MPC control strategy is generally lower than that under passive control.

[0086] Then, simulation calculations were performed on different vehicle speed levels of 150km / h, 200km / h, 250km / h and 300km / h to obtain the vehicle body lateral comfort index (RMS) at different vehicle speeds, such as Figure 11 As shown. Figure 11 It can be seen that, whether under passive control or MPC control strategy, the RMS index of the car body is lower than 0.007, both reaching the "excellent" comfort level. Under the MPC control strategy, the root mean square value of the car body acceleration is always maintained below 0.003, indicating that the adoption of MPC control can effectively attenuate the vibration behavior of the train.

[0087] Use frequency domain analysis to verify:

[0088] The frequency domain response curves of the vehicle body angular acceleration of the power-type centralized EMU under MPC control and passive control are obtained. Figure 12 This is a comparison of the frequency domain response curves of the train at a speed of 150km / h. Figure 13 The frequency domain response curve comparison diagram of the train at a speed of 200km / h is shown in the figure. Figure 14 This is a comparison of the frequency domain response curves of the train at a speed of 250km / h. Figure 15 The comparison diagram of the frequency domain response curve of the train at a speed of 300km / h.

[0089] Depend on Figures 12-15 The peaks of the passive control system's yaw rate curve are significantly concentrated in the 6Hz-7Hz and 12Hz-15Hz frequency ranges, reaching their peak values between 7Hz and 14Hz. Compared to passive control, the MPC control strategy significantly reduces the yaw rate amplitude within these frequency ranges, demonstrating its effective suppression of vehicle lateral vibration.

[0090] Then, simulation calculations are performed on different vehicle speed levels such as 150km / h, 200km / h, 250km / h and 300km / h to obtain the vehicle body lateral stability index at different speeds, such as Figure 16 As shown. Figure 16 It can be seen that the use of MPC control can effectively attenuate the vibration behavior of the train and effectively improve the lateral stability of the train.

[0091] This embodiment provides an anti-snaking shock absorber control system based on model prediction, which is used to implement the above method, including a model building unit, a model prediction unit and a model control unit;

[0092] The model building unit establishes a three-dimensional multi-degree-of-freedom train dynamics model and model predictive controller based on the connection mode and relative motion relationship of various train components. The anti-snaking damper of the train is equivalent to an additional stiffness spring, a node stiffness spring, and a structural damper.

[0093] a model prediction unit, configured to linearly discretize a three-dimensional multi-degree-of-freedom train dynamics model to construct a lateral multi-degree-of-freedom train state prediction model; and based on the lateral multi-degree-of-freedom train state prediction model and the train's current state information, to use a model predictive controller to predict the train's vibration behavior to obtain the control damping force required for train operation; wherein the lateral multi-degree-of-freedom includes the lateral displacement degree of freedom, the yaw motion degree of freedom, and the roll motion degree of freedom of the train's lateral vibration;

[0094] The model control unit issues a vibration reduction control instruction to the additional stiffness spring, the node stiffness spring and the structural damping based on the control damping force to suppress the vibration behavior of the train.

[0095] This embodiment provides an electronic device, including at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the program is executed by the processing unit, the processing unit performs the above method.

[0096] This embodiment provides a storage medium storing a computer program executable by an electronic device. When the program runs on the electronic device, the electronic device executes the above method.

[0097] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for controlling an anti-snaking shock absorber based on model prediction, characterized in that: include: Based on the connection mode and relative motion relationship of various train components, a three-dimensional multi-degree-of-freedom train dynamics model and model predictive controller are established. The train's anti-snaking damper is equivalent to an additional stiffness spring, a node stiffness spring, and structural damping. Linearly discretizing the three-dimensional multi-degree-of-freedom train dynamics model to construct a lateral multi-degree-of-freedom train state prediction model, wherein the lateral multi-degree-of-freedom includes the lateral displacement degree of freedom of the train lateral vibration, the yaw motion degree of freedom, and the roll motion degree of freedom; Based on the lateral multi-degree-of-freedom train state prediction model and the current state information of the train, a model predictive controller is used to predict the vibration behavior of the train to obtain the control damping force required for train operation; Based on the controlled damping force, a vibration reduction control instruction is issued to the additional stiffness spring, the node stiffness spring and the structural damping to suppress the vibration behavior of the train.

2. The method according to claim 1, characterized in that The model predictive controller is used to predict the vibration behavior of the train to obtain the control damping force required for train operation, including: The objective function is designed with the lateral vibration velocity, yaw angular velocity and roll angular velocity of the train body as the control targets, and constraints are set for the lateral vibration velocity, yaw angular velocity and roll angular velocity of the train body; Based on the current state information and constraints of the train, the objective function is solved to predict the vibration behavior of the train, and the control damping force is calculated based on the prediction results.

3. The method according to claim 1 or 2, characterized in that The method further includes: analyzing the control damping force by a frequency domain analysis method or a time domain analysis method, and optimizing the vibration reduction control instruction based on the analysis result to suppress the vibration behavior of the train.

4. The method according to claim 1, wherein The three-dimensional multi-degree-of-freedom train dynamics model includes a car body, a frame, a hollow axle, a wheelset, and suspension elements. The suspension elements include a primary suspension and a secondary suspension. The primary suspension includes a first vertical shock absorber, a primary spring, and an axle box tie rod. The secondary suspension includes lateral shock absorbers symmetrically distributed at the ends of the frame, anti-snaking shock absorbers asymmetrically distributed on the side beams of the frame, and second vertical shock absorbers symmetrically distributed on both sides of the frame.

5. The method according to claim 1, wherein The dynamic equations of the three-dimensional multi-degree-of-freedom train dynamic model are as follows: Among them, M represents the mass coefficient matrix; K represents the stiffness coefficient matrix; C represents the damping coefficient matrix; Y L Represents a column vector containing multiple motion state variables of the train; y b 、y t1 、y t2 、y w1 、y w2 、y w3 、y w4 Respectively represent the lateral displacement of the train body, front bogie, rear bogie, left front wheel pair, right front wheel pair, left rear wheel pair, and right rear wheel pair; Respectively represent the shaking motion of the train body, front bogie, rear bogie, left front wheel pair, right front wheel pair, left rear wheel pair, and right rear wheel pair; ρ b , ρ t1 , ρ t2 Represent the rolling motion of the train body, front bogie and rear bogie respectively; Represents Y L The first-order derivative vector with respect to time, i.e., the velocity vector of each state variable; Represents Y L The second-order derivative vector with respect to time is the acceleration vector of each state variable; TF represents the external force vector, which includes various external forces acting on the train.

6. The method according to claim 1, characterized in that The state space expression of the lateral multi-degree-of-freedom train state prediction model is as follows: Z(t)=CY(t) Among them, u(t) represents the control quantity; Y(t) represents the state quantity; It is the derivative of the state quantity Y(t) with respect to time t; Z(t) represents the predicted state quantity; C represents the mapping matrix.

7. The method according to claim 1, characterized in that The connection mode and relative motion relationship of the train are determined based on the topological relationship of the train, and the topological relationship of the train is the spatial layout and connection structure between the various components of the train.

8. An anti-snaking shock absorber control system based on model prediction, used to implement the method according to any one of claims 1 to 7, characterized in that: It includes a model building unit, a model prediction unit and a model control unit; The model building unit establishes a three-dimensional multi-degree-of-freedom train dynamics model and model predictive controller based on the connection mode and relative motion relationship of various train components. The anti-snaking damper of the train is equivalent to an additional stiffness spring, a node stiffness spring, and a structural damper. a model prediction unit, configured to linearly discretize a three-dimensional multi-degree-of-freedom train dynamics model to construct a lateral multi-degree-of-freedom train state prediction model; and based on the lateral multi-degree-of-freedom train state prediction model and the train's current state information, to use a model predictive controller to predict the train's vibration behavior to obtain the control damping force required for train operation; wherein the lateral multi-degree-of-freedom includes the lateral displacement degree of freedom, the yaw motion degree of freedom, and the roll motion degree of freedom of the train's lateral vibration; The model control unit issues a vibration reduction control instruction to the additional stiffness spring, the node stiffness spring and the structural damping based on the control damping force to suppress the vibration behavior of the train.

9. An electronic device, characterized in that: The method comprises at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the program is executed by the processing unit, the processing unit executes the method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The computer program that can be executed by an electronic device is stored therein. When the program is run on the electronic device, the electronic device is enabled to execute the method according to any one of claims 1 to 7.

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