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CN111123706B - Control method for semi-active suspension system of high-speed train - Google Patents

Control method for semi-active suspension system of high-speed train Download PDF

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CN111123706B
CN111123706B CN201911367342.6A CN201911367342A CN111123706B CN 111123706 B CN111123706 B CN 111123706B CN 201911367342 A CN201911367342 A CN 201911367342A CN 111123706 B CN111123706 B CN 111123706B
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sliding mode
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CN111123706A (en
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何静
刘志天
张昌凡
刘建华
毛颂安
徐艳
谌雪媛
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Hunan University of Technology
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Abstract

The invention belongs to the technical field of train control, and discloses a control method of a semi-active suspension system of a high-speed train, which comprises the following steps: s1, establishing a seventy-degree-of-freedom state equation of the high-speed train according to an Euler-Lagrange-based nonlinear dynamics model; s2, designing a sliding mode gain adaptive observer suitable for a nonlinear dynamic structure in order to observe disturbance borne by a train in high-speed operation in real time; and S3, combining the sliding mode variable structure control algorithm with the power integration control algorithm to design the high-speed train suspension controller. The high-speed train suspension control algorithm based on the power integration sliding mode variable structure solves the problem that disturbance in practical engineering is unknown at the upper bound, effectively overcomes the problem of strong coupling in a nonlinear complex system, enables the system to have rapid dynamic response through the power integration algorithm, has the characteristics of strong robustness, high reliability and the like, and well meets the requirement of rapid, safe and stable operation of a high-speed train.

Description

Control method for semi-active suspension system of high-speed train
Technical Field
The invention relates to the field of rail transit operation control, in particular to a control method of a semi-active suspension system of a high-speed train.
Background
Along with the development of national rail transit, a Chinese high-speed train becomes a Chinese name card and bears the important belief that China science and technology goes away, the Chinese high-speed train is well known for stability, the high-stability suspension control system cannot be used for high-performance suspension control, and the suspension system in the running process of the high-speed train is a multi-degree-of-freedom dynamic system and belongs to a multivariable, nonlinear and strongly-coupled complex model.
Aiming at the unknown problem of disturbance upper bound in the practical engineering application, how to design a better observer and how to design a better controller aiming at improving the anti-interference capability of train operation are all the keys of the control technology of the suspension system for high-speed train operation in the practical production application, and the control technology directly influences the reliability of train operation control and the comfort of passengers. The key problem to be solved is how to design an observer to quickly and accurately observe the disturbance in the nonlinear random vibration system, how to design a control algorithm to quickly and effectively inhibit the disturbance in the train operation and ensure the safe and stable operation of the high-speed train, and the technical problems which need to be solved urgently are all the high-speed train design.
Disclosure of Invention
The invention aims to provide a high-speed train control method based on an exponentiation integral sliding mode, which aims to solve the problem that the upper bound of the track disturbance suffered by a high-speed train in the running process of the high-speed train in the practical engineering is unknown, so that the high-speed train can run quickly, safely and stably.
In order to achieve the purpose, the invention provides the following technical scheme:
a control method for a semi-active suspension system of a high-speed train comprises the following steps:
s1: establishing a multi-degree-of-freedom state equation of the high-speed train based on an Euler-Lagrange nonlinear dynamics model;
s2: designing a sliding mode gain adaptive observer suitable for a nonlinear dynamic structure to observe the disturbance borne by the train in real time during high-speed operation;
s3: and combining a sliding mode variable structure control algorithm with an exponentiation integral control algorithm to design a high-speed train semi-active suspension controller.
Step S3: and combining a sliding mode variable structure control algorithm with an exponentiation integral control algorithm to design the high-speed train suspension controller.
Further, the multiple degree of freedom state equation of the high-speed train described in S1 is a seventeen degree of freedom state equation, and specifically includes the following processes:
s11: analyzing the motion state of the semi-active suspension system according to nonlinear dynamics, and establishing a seventeen-degree-of-freedom dynamics mathematical model of the high-speed train, wherein the seventeen-degree-of-freedom dynamics mathematical model comprises a transverse moving and oscillating motion equation of a wheel pair, a transverse moving, side rolling and oscillating motion equation of a bogie and a transverse moving, side rolling and oscillating motion equation of a train body;
s12: equating the seventy-degree-of-freedom dynamics mathematical model to a mathematical model of the traditional nonlinear random dynamics to obtain a model transformation equation of the seventy-degree-of-freedom of the high-speed train;
s13: introducing external interference under various complex train conditions and/or road condition time variation in a train operation environment, establishing an accurate seventy-degree-of-freedom model of the high-speed train, and obtaining an expression of the uncertain external interference of the whole system.
The seventy-degree-of-freedom dynamic mathematical model of the high-speed train in the S11 comprises the following steps:
s111: equation of motion for transverse movement of wheel pair
Figure BDA0002338775290000021
S112: equation of motion for wheel set shaking head
Figure BDA0002338775290000022
S113: equation of motion for traversing bogie
Figure BDA0002338775290000023
S114: equation of motion for side rolling of bogie
Figure BDA0002338775290000031
S115: equation of motion for the bogie tilting head
Figure BDA0002338775290000032
S116: equation of motion for lateral movement of vehicle body
Figure BDA0002338775290000033
S117: equation of motion for vehicle body roll
Figure BDA0002338775290000034
S118: equation of motion for shaking head of car body
Figure BDA0002338775290000035
Furthermore, an external generalized interference force term is obtained, wherein m is 1-4, and n is 5-8.
Figure BDA0002338775290000036
Figure BDA0002338775290000037
Further, the model transformation equation determined at S12 is as follows:
Figure BDA0002338775290000041
furthermore, the definition D represents other external interference under complex time variation in the train operation environment, and a more accurate multi-degree-of-freedom model of the high-speed train is obtained. Further transformation of the model transformation equation then gives:
Figure BDA0002338775290000042
further, the system integrated uncertainty external disturbance determined at S13 is as follows:
Figure BDA0002338775290000043
in the formula [ M0]，[C0]，[K0}: sequentially forming an inertia matrix, a damping matrix and a time stiffness matrix of the whole vehicle system, wherein the inertia matrix, the damping matrix and the time stiffness matrix are respectively a 17 multiplied by 17 order square matrix; Δ M represents a change in mass coefficient due to a change in the number of passengers, etc., Δ C represents a change in damping coefficient after long-term operation of the train, and Δ K represents a change in spring coefficient after long-term operation of the train; u: controller output of 17 × 17 steps; [ D ]]Disturbance variables including track irregularity. y ═ yw1，yw2，yw3，yw4，ψ w1，ψw2，ψw3，ψw4，yt1，yt2，φt1，φt2，ψt1，ψt2，yc，φc，ψc]T；
Further, the specific design process of the sliding-mode gain adaptive observer described in S2 includes the following processes:
s21: constructing an observation sliding mode surface of an external interference item according to errors between measurement values and set reference values of various degree-of-freedom sensors of a wheel set, a bogie and a vehicle body of a high-speed train;
s22: constructing a sliding mode variable structure observer of an interference term according to a model transformation equation of seventeen degrees of freedom of the high-speed train;
further, the specific process of S21 is as follows:
s211: error information defining measured values and design reference values
Figure BDA0002338775290000044
S212: sliding mode surface equation for designing observer
Figure BDA0002338775290000045
Still further, the specific design process of the sliding mode variable structure observer in S22 is as follows:
s221: observer for designing sliding mode variable structure
Figure BDA0002338775290000046
S222: in the observer
Figure BDA0002338775290000047
Is estimated as
Figure BDA0002338775290000051
Wherein ν ═ λ1sgn(Sa) Sliding-mode variables, scalar lambda1The gain of the auxiliary system. The sudden increase phenomenon can not occur in the disturbance in the operation of the high-speed train, so that the method can obtain
Figure BDA0002338775290000052
And is
Figure BDA0002338775290000053
And (3) subtracting a sliding mode variable structure observer equation from the converted model conversion equation system to obtain a normal number by substituting:
Figure BDA0002338775290000054
unknown gain factor in the equation
Figure BDA0002338775290000055
Where γ is a positive matrix.
Preferably, in order to ensure that the sliding mode variable structure observer designed in S22 can reach and can tend to asymptotically stabilize, the sliding mode variable structure observer designed in S22 is proved, and the specific proving process is as follows:
s231, constructing a Lyapunov function for proving existence and accessibility of the sliding-mode observer
Figure BDA0002338775290000056
S232, proving existence and accessibility of the sliding mode observer
Figure BDA0002338775290000057
S233: construction of Lyapunov function demonstrating observer stability
Figure BDA0002338775290000058
S234: proves that the observer tends to be gradually stable
Figure BDA0002338775290000059
Further, the specific design process of the semi-active suspension controller of the high-speed train described in S3 is as follows:
s31: defining an intermediate variable of a model transformation equation of seventeen degrees of freedom of the high-speed train, and converting the intermediate variable into a space state equation;
s32: selecting a proper integral sliding mode surface, and constructing an exponentiation integral sliding mode controller;
further, the spatial state equation establishment process described in S31 is as follows:
s311: state variables defining mathematical models of trains
Setting the ideal track position of the semi-active suspension system as ydWhen the actual position vector is y, the state variable is
x1＝[x1,1,...,x1,n]T＝yd-y
Figure BDA0002338775290000061
S312: space state equation for establishing train model transformation equation
Figure BDA0002338775290000062
Figure BDA0002338775290000063
Definition of
Figure BDA0002338775290000064
Wherein
Figure BDA0002338775290000065
Is a set virtual controller in which
1＜r＝r1/r2＜2，r1And r2Are all positive integers.
Still further, the controller design described in S32 is specifically as follows:
s321: selecting integral sliding mode surface as
Figure BDA0002338775290000066
S322: design of power-increasing integral sliding mode controller
Figure BDA0002338775290000067
In the formula k1＞0，
Figure BDA0002338775290000068
Furthermore, in order to ensure that the exponentiation integral sliding mode controller designed in the S32 can reach and can tend to be asymptotically stable, the exponentiation integral sliding mode controller designed in the S32 is proved, and the specific proving process is as follows:
s331, constructing a Lyapunov equation:
V＝V1+V2
Figure BDA0002338775290000071
Figure BDA0002338775290000072
s332, verifying the stability of the power integration controller
Figure BDA0002338775290000073
Figure BDA0002338775290000074
Combining the above equations yields:
Figure BDA0002338775290000075
in the formula
Figure BDA0002338775290000076
Further, adopt
Figure BDA0002338775290000077
Instead of the function sgn(s), δ is a normal number that goes to zero.
Compared with the prior art, the invention has the beneficial effects that: the high-speed train control method based on the exponentiation integral sliding mode effectively solves the problems of variable disturbance, system nonlinearity, variable strong coupling and the like in a complex system, enables the system to have rapid dynamic response with limited time convergence, has strong robustness and high reliability, well meets the requirement of safe and stable operation of a high-speed train, and is beneficial to practical engineering application of high-speed train operation control.
Drawings
FIG. 1 is a block diagram showing a control method of a semi-active suspension system of a high-speed train in embodiment 1;
FIG. 2 is a comparison simulation diagram of an actual disturbance value and an observed value of a gain adaptive sliding mode disturbance observer in the control method of the semi-active suspension system of the high-speed train in embodiment 2;
fig. 3 is a simulation diagram of lateral displacement of a vehicle body during operation of a high-speed train in the control method of the semi-active suspension system of the high-speed train according to embodiment 2.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 1, a method for controlling a semi-active suspension system of a high-speed train based on an exponentiation integral sliding mode includes the following steps:
the method comprises the first step of establishing a multi-degree-of-freedom state equation of the high-speed train according to an Euler-Lagrange nonlinear dynamics model.
Further, the seventeen-degree-of-freedom dynamics mathematical model of the high-speed train can be expressed as:
the formula (1) refers to transverse movement of the wheel pair:
Figure BDA0002338775290000081
equation (2) is wheel pair pan head motion:
Figure BDA0002338775290000082
formula (3) is the traversing movement of the bogie:
Figure BDA0002338775290000091
equation (4) is the bogie roll motion:
Figure BDA0002338775290000092
formula (5) is the bogie motion of shaking the head:
Figure BDA0002338775290000093
the formula (6) is the transverse movement of the vehicle body:
Figure BDA0002338775290000094
equation (7) is the vehicle body roll motion:
Figure BDA0002338775290000095
formula (8) is the body panning motion:
Figure BDA0002338775290000096
the formula (9) and the formula (10) are terms of external generalized interference force, wherein m is 1-4, and n is 5-8.
Figure BDA0002338775290000097
Figure BDA0002338775290000101
The following table shows the output variable parameter meanings of a seventeen-degree-of-freedom dynamic mathematical model of the high-speed train
Figure BDA0002338775290000102
When j is 1, i is 1-2; when j is 2, i is 3-4.
Further, analysis of the model characteristics shows that the high-speed train multi-degree-of-freedom dynamic mathematical model can be equivalent to a mathematical model of the traditional non-linear random dynamics on the premise that the high-speed train multi-degree-of-freedom model meets the Euler-Lagrange non-linear random dynamics, so that a multi-degree-of-freedom model transformation equation of the high-speed train is obtained, and the obtained equation (11) is as follows:
Figure BDA0002338775290000103
in the formula [ M0]，[C0]，[K0]: sequentially forming an inertia matrix, a damping matrix and a time stiffness matrix of the whole vehicle system, wherein the inertia matrix, the damping matrix and the time stiffness matrix are respectively a 17 multiplied by 17 order square matrix; Δ M represents a change in mass coefficient due to a change in the number of passengers, etc., Δ C represents a change in damping coefficient after long-term operation of the train, and Δ K represents a change in spring coefficient after long-term operation of the train; u: controller output of 17 × 17 steps; [ D ]]Disturbance variables including track irregularity. y ═ yw1，yw2，yw3，yw4，ψw1，ψw2，ψw3，ψw4，yt1，yt2，φt1，φt2，ψt1，ψt2，yc，φc，ψc]T；
Further, the system equation is further converted to obtain the formula (12)
Figure BDA0002338775290000104
The definition D represents other external interference under complex time variation in the train operation environment, and the variable y reflects the traversing displacement, the head shaking and the side rolling angle of a wheel set, a train body and a bogie.
The system synthesis uncertainty after the arrangement in the formula is
Figure BDA0002338775290000105
Secondly, aiming at the problem that the disturbance borne by the train in high-speed operation needs to be observed in real time, the sliding mode gain adaptive observer suitable for the nonlinear dynamic structure is designed;
specifically, by using a high-speed train as a controlled object, the error between the sum y of the outputs of the subsystems of the system and the set reference value eta can be expressed as
Figure BDA0002338775290000111
Defining a slip-form surface, as shown in equation (13):
Figure BDA0002338775290000112
further, to design the following sliding mode variable structure observer, as shown in formula (14):
Figure BDA0002338775290000113
in the formula
Figure BDA0002338775290000114
The estimated value of (c) is shown as follows:
Figure BDA0002338775290000115
wherein ν ═ λ1sgn(Sa) Sliding-mode variables, scalar lambda1The gain of the auxiliary system. The sudden disturbance in the running of the high-speed train can not generate the phenomenon of sudden increase, so the method can obtain
Figure BDA0002338775290000116
And is
Figure BDA0002338775290000117
Is a normal number, and the formula (12) minus the formula (14) is substituted into the formula (15):
Figure BDA0002338775290000118
unknown gain factor in the equation
Figure BDA0002338775290000119
Where γ is a positive matrix.
Specifically, the following Lyapunov function is constructed, as shown in formula (16):
Figure BDA00023387752900001110
the derivative of equation (16) with respect to time t is equation (17):
Figure BDA00023387752900001111
in particular, to make the state estimation error
Figure BDA00023387752900001112
Can be maintained after converging to zero for a finite time, construct the following Lyapunov function, as shown in equation (18):
Figure BDA00023387752900001113
the derivative of equation (18) with respect to time t is equation (19):
Figure BDA0002338775290000121
as can be seen from equation (19), the system satisfies the conditions of existence of the sliding form and accessibility, and when the system is brought into the sliding form state,
Figure BDA0002338775290000122
stably bounded for a finite time, i.e.
Figure BDA0002338775290000123
The observer can track the disturbances in real time.
Thirdly, designing an exponentiation integral sliding mode controller to rapidly eliminate the tracking error in the running process of the high-speed train and proving the stability of the designed controller;
further, the ideal track position of the semi-active suspension system of the high-speed train is defined as yd＝[yd1,...,ydn]Then, the tracking error between the actual vector and the ideal vector is defined as shown in equation (20):
Figure BDA0002338775290000124
the original system equation (12) can be converted into an error equation as shown in equation (21):
Figure BDA0002338775290000125
wherein r is 1 ≦ r1/r2＜2，r1、r2Are all positive integers. Definition of
Figure BDA0002338775290000126
Wherein
Figure BDA0002338775290000127
The method is a set virtual controller, and the definition of a virtual control law is shown as a formula (22):
Figure BDA0002338775290000128
specifically, a sliding mode surface is selected, as shown in formula (23):
Figure BDA0002338775290000129
aiming at a multi-degree-of-freedom dynamics mathematical model of a high-speed train, according to a formula, an power integration sliding mode controller can be designed into a controller shown in a formula (24):
Figure BDA0002338775290000131
in the formula k1＞0，
Figure BDA0002338775290000132
sgn(s) is a sign function;
further, according to the designed controller, stability thereof is now demonstrated;
specifically, the following Lyapunov function is constructed, as shown in formula (25):
Figure BDA0002338775290000133
the derivation of equation (25) yields equations (26) and (27), respectively, as follows:
Figure BDA0002338775290000134
Figure BDA0002338775290000135
the combination of the formulae (26) and (27) gives the formula (28):
Figure BDA0002338775290000141
in the formula
Figure BDA0002338775290000142
From equation (28), the system satisfies the conditions of existence of the sliding mode and accessibility, and when the system enters the sliding mode state, s is 0, and in combination with equation (25), the system makes the error x1And x2Progressively converging to zero, and the tracking error x can be derived1Can be eliminated.
Fig. 1 is a structural block diagram of a control method of a high-speed train semi-active suspension system based on an exponentiation integral sliding mode, which is provided by the invention, and the structure considers the influence of unknown disturbance on train operation in the high-speed train operation on one hand and also considers that the train body transverse movement of the high-speed train is stable in a safe range in a very short time on the other hand, so that the stable operation of the control structure can effectively weaken the influence of disturbance on train operation and effectively improve the control precision of the system.
Example 2
The present embodiment is different from embodiment 1 in that: in order to effectively reduce the buffeting problem caused by sgn(s) function
Figure BDA0002338775290000143
Instead of the function sgn(s), a function is provided, δ being a normal number tending to zero, e.g. 10-5、l0-6、10-7And so on.
As can be known from the simulation result in FIG. 2, the actual disturbance value and the observed value of the gain adaptive sliding mode disturbance observer coincide rapidly after about 0.001 second in each sampling period, which shows that the designed observer tracks the unknown disturbance of the system rapidly and effectively, and provides a strong support for the design of the subsequent controller.
As can be seen from the simulation result in FIG. 3, the high-speed train body transverse movement is converged to a minimum range within a very short time within the sampling period, which indicates that the system has a fast dynamic response characteristic in the whole process.
The following table gives the details of the meaning of the parameters in the present application:
Figure BDA0002338775290000144
Figure BDA0002338775290000151
the high-speed train suspension control algorithm based on the power integration sliding mode variable structure solves the problem that disturbance in practical engineering is unknown at the upper bound, effectively overcomes the problem of strong coupling in a nonlinear complex system, enables the system to have rapid dynamic response through the power integration algorithm, has the characteristics of strong robustness, high reliability and the like, and well meets the requirement of rapid, safe and stable operation of a high-speed train.
It should be understood that the above examples are only for clearly illustrating the technical solutions of the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (1)

1. A control method for a semi-active suspension system of a high-speed train is characterized in that an exponentiation integral sliding mode control algorithm is adopted, and the method comprises the following steps:
s1: establishing a multi-degree-of-freedom state equation of the high-speed train based on an Euler-Lagrange nonlinear dynamics model;
step S1 includes the following steps:
s11: analyzing the motion state of the semi-active suspension system according to nonlinear dynamics, and establishing a seventeen-degree-of-freedom dynamics mathematical model of the high-speed train, wherein the seventeen-degree-of-freedom dynamics mathematical model comprises a transverse moving and oscillating motion equation of a wheel pair, a transverse moving, side rolling and oscillating motion equation of a bogie and a transverse moving, side rolling and oscillating motion equation of a train body;
s12: the seventy-degree-of-freedom kinetic mathematical model is equivalent to a mathematical model of the traditional nonlinear random dynamics, and a model transformation equation of the seventy-degree-of-freedom of the high-speed train is obtained;
s13: introducing external interference under various complex train conditions and/or road condition time variation in a train operation environment, establishing an accurate seventy-degree-of-freedom model of the high-speed train, and obtaining an expression of uncertain external interference of the whole system;
the seventy-degree-of-freedom high-speed train mathematical model determined in the step S11 is as follows:
s111: wheel set sideslip equation of motion:
Figure FDA0003562477490000011
s112: wheel set oscillating motion equation:
Figure FDA0003562477490000012
s113: the transverse movement equation of the bogie:
Figure FDA0003562477490000013
s114: bogie roll equation of motion:
Figure FDA0003562477490000021
s115: bogie oscillation equation of motion:
Figure FDA0003562477490000022
s116: the transverse movement equation of the vehicle body:
Figure FDA0003562477490000023
s117: vehicle body roll equation of motion:
Figure FDA0003562477490000024
s118: the shaking motion equation of the vehicle body:
Figure FDA0003562477490000025
the model transformation equation determined at S12 is as follows:
Figure FDA0003562477490000026
the system integrated uncertainty external disturbance determined at S13 is as follows:
Figure FDA0003562477490000027
s2: designing a sliding mode gain adaptive observer suitable for a nonlinear dynamic structure to observe the disturbance borne by the train in real time during high-speed operation;
step S2 includes the following steps:
s21: constructing an observation sliding mode surface of an external interference item according to errors among measured values and set reference values of various degree-of-freedom sensors of a wheel pair, a bogie and a vehicle body of a high-speed train;
s22: constructing a sliding mode variable structure observer of an interference term according to a model transformation equation of seventeen degrees of freedom of the high-speed train;
the specific construction process of the observation sliding mode surface of step S21 is as follows:
s211: error information defining the measured values and the design reference values:
Figure FDA0003562477490000031
s212: designing a sliding mode surface equation of the observer:
Figure FDA0003562477490000032
the specific design process of the sliding mode variable structure observer in the S22 is as follows:
s221: designing a sliding mode variable structure observer:
Figure FDA0003562477490000033
s222: in the observer
Figure FDA0003562477490000034
The estimated values of (c) are:
Figure FDA0003562477490000035
s3: combining a sliding mode variable structure control algorithm with an exponentiation integral control algorithm to design a high-speed train semi-active suspension controller;
step S3 includes the following steps:
s31: defining an intermediate variable of a model transformation equation of seventeen degrees of freedom of the high-speed train, and converting the intermediate variable into a space state equation;
s32: selecting a proper integral sliding mode surface, and constructing an exponentiation integral sliding mode controller;
the spatial state equation conversion process of step S31 is as follows:
s311: state variables defining mathematical models of trains
Semi-active suspension systemHas an ideal track position of ydAnd the actual position vector is y, the state variable is:
x1＝[x1,1,...,x1,n]T＝yd-y
Figure FDA0003562477490000041
s312: establishing a space state equation of a train model transformation equation:
Figure FDA0003562477490000042
Figure FDA0003562477490000043
definition of
Figure FDA0003562477490000044
Wherein
Figure FDA0003562477490000045
Is a set virtual controller, wherein 1<r＝r1/2<2，r1And r2Are all positive integers;
the controller design of S32 is as follows:
s321: selecting an integral sliding mode surface as follows:
Figure FDA0003562477490000046
s322: designing an exponentiation integral sliding mode controller:
Figure FDA0003562477490000047
in the formula k1＞0，
Figure FDA0003562477490000048
sgn(s) is a sign function;
by using
Figure FDA0003562477490000049
Instead of the function sgn(s), δ is a normal number that tends to zero.
CN201911367342.6A 2019-12-26 2019-12-26 Control method for semi-active suspension system of high-speed train Active CN111123706B (en)

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