阅读说明：本技术 一种大风环境下列车主动悬挂系统控制方法 (Train active suspension system control method in strong wind environment ) 是由 李德仓 孟高阳 孟建军 刘瑜 于 2021-09-14 设计创作，主要内容包括：本发明属于车辆控制技术领域,公开了一种大风环境下列车主动悬挂系统控制方法,以解决现有技术列车主动悬挂系统中存在的技术问题,该方法包括利用检测系统实时采集车身振动信号,设计变论域模糊控制器,变论域模糊控制器根据风速、车速以及检测系统实时采集车身振动信号、计算出主动悬挂系统所需作动力,发出控制指令,作动器根据所发出的控制指令,输出相应作动力等步骤,本发明有效的提高了不同风场环境下列车主动悬挂系统的自适应能力与控制精度,使不同风速与车速运行条件下的控制规则得到细化,较好的保证了列车在大风环境中不同运行工况的动力学性能,有利于大风环境下高速列车主动悬挂系统的实际工程应用。(The invention belongs to the technical field of vehicle control, and discloses a control method of an active suspension system of a train in a strong wind environment, which aims to solve the technical problems in the active suspension system of the train in the prior art, and comprises the steps of utilizing a detection system to collect vibration signals of the train body in real time, designing a variable universe fuzzy controller, collecting the vibration signals of the train body in real time according to the wind speed and the train speed and the detection system by the variable universe fuzzy controller, calculating the actuating power required by the active suspension system, sending a control instruction, outputting the corresponding actuating power by an actuator according to the sent control instruction, and the like. The active suspension system is beneficial to the practical engineering application of the active suspension system of the high-speed train in the strong wind environment.)

1. A control method for an active suspension system of a train in a strong wind environment is characterized by comprising the following steps:

step 1, calculating wind loads borne by a vehicle body under different wind speeds and vehicle speeds;

step 2, obtaining a full-size model of the train, and establishing a complete vehicle dynamics simulation model consisting of three rigid bodies of a train body, a bogie frame and a wheel pair by using SIMPACK; according to the dynamic simulation model, applying wind load to the vehicle body, calculating a vehicle body response result, and finishing the vehicle body response rules at different wind speeds and vehicle speeds;

step 3, combining the vehicle body response rules under different wind speed and vehicle speed running conditions with a fuzzy control theory, and designing a variable universe fuzzy controller;

and 4, acquiring the acceleration response result, the wind speed and the vehicle speed of the train detected at each moment, and enabling the active suspension system to output the required actuating power in real time through the variable universe fuzzy controller.

2. The method for controlling the active suspension system of the train in the strong wind environment according to claim 1, wherein the step 1 comprises solving a fluctuating wind speed, a train surface relative wind speed and a wind load model;

a) solving the pulsating wind speed:

the pulsating wind solving equation based on the harmonic synthesis method and the fast Fourier transform technology is as follows:

(j＝1，2，…，n)，(m＝1，2，…，n)，(p＝0，1，…，2N×H-1)

in the formula, Y_χj(delta t) represents the time interval of the fluctuating wind speed at the jth simulated wind speed point, Re represents the real part of an imaginary number, delta t represents a time interval, i represents the imaginary part, N represents the number of wind speed simulated points, delta omega represents a frequency increment, and N represents the number of sampling frequency points;

in the above formula, h_jm(Δ t) is expressed as:

in the formula, phi_mlIs [0,2 π ]]Random phase angles, S, distributed uniformly within_χ(z) power spectrum of the pulsating wind speed, G_jm(ω) represents the j row m term of the correlation coefficient matrix;

in the above formula, G_jmThe expression (ω) is:

wherein R represents the spatial correlation of wind, c represents the exponential decay coefficient, and Delta representsHorizontal separation point distance, f represents wind speed frequency, U_χzRepresents the average wind speed at height Z;

according to the specific change of the wind along the height, the wind speed power spectrum adopts a Kaimal wind speed spectrum, and the expression of the Kaimal wind speed spectrum is as follows:

in the formula, S_χ(z) denotes the Kaimal wind velocity spectrum, σ, at height z_χ、L_χRespectively representing the standard deviation, the integral scale of turbulence, f of each directional component_LχA reduction frequency representing the corresponding directional component;

b) solving the relative wind speed on the surface of the train:

the expression of the relative wind speed on the surface of the train is as follows:

in the formula, beta represents an included angle between the wind direction and the advancing direction of the train, and V represents the train wind which is equal to the running speed of the train and opposite to the running speed of the train.

c) Solving a wind load model;

the equivalent static wind force on the surface of the vehicle body is as follows:

in the formula, the equivalent static wind force F used on the surface of the train^stIncluding resistanceLifting forceAnd torsional momentu, w and v respectively represent a transverse direction, a vertical direction and a longitudinal direction; ρ is the air density; a is the windward area of the vehicle body, and the influence of a bogie and a wheel pair is ignored; h is the height from the center of mass of the vehicle body to the roadbed; c (psi) is aerodynamic coefficient;

the equivalent trembling array wind power at a certain point on the surface of the vehicle body is as follows:

in the formula, the j-point buffeting wind power also comprises resistance, lift force and torsional moment; u. of_j、w_jRepresenting the transverse and vertical components of the pulsating wind at the j point; b is_j、L_jRespectively showing the height and the width of the section of the vehicle body at the jth simulation point.

3. The method for controlling the active suspension system of the train in the strong wind environment according to claim 2, wherein the step 2 comprises the following steps:

a) completing modeling of a dynamic simulation model: firstly, determining various structural parameters and suspension parameters of a train model, inputting the various parameters into a SIMPACK system, and establishing a whole train model consisting of three rigid bodies, namely a train body, a bogie frame and a wheel pair;

b) performing dynamic simulation: applying equivalent wind load to the centroid of the train body, wherein the kinetic equation of the train system in the wind field is as follows:

wherein M, C, K represents the mass matrix, damping matrix and stiffness matrix of the train system respectively,respectively representing the displacement response, velocity response and acceleration response of the train system, F^st、F^bfRespectively representing generalized vectors of equivalent static wind force and buffeting wind force acting on the centroid of the surface of the vehicle body;

c) finishing the response rule of the vehicle body at different wind speeds and vehicle speeds;

obtaining train response results by using SIMPACK, and arranging the speed response range b of the train system_vAcceleration response range b_aAnd the relative force f between the vehicle body and the bogie frame_a；

Setting different wind speeds and vehicle speeds to enable the wind speed U_χzThe speed V is less than or equal to 20m/s and less than or equal to 200Km/h, the response ranges of the vehicle bodies under different wind speeds and vehicle speeds are calculated, the response rules of the vehicle bodies corresponding to the different wind speeds and vehicle speeds are sorted, and the related rules of the expansion factor expression are introduced, wherein the mathematical expression is as follows:

in the formula, alpha_i(U_χz,V)、β_i(U_χz,V)、γ_i(U_χzV) respectively represents the wind speed U_χzWhen the vehicle speed is V, the variable b is input_v、b_aAnd an output variable f_aScaling factor of b_v(U_χz,V)、b_a(U_χz,V)、f_a(U_χzV) represents the response extreme value corresponding to the wind speed and the vehicle speed, b_v(20,200)、b_a(20,200)、f_a(20,200) represents the input variable b at a wind speed of 20m/s and a vehicle speed of 200Km/h_v、b_aAnd an output variable f_aThe response extremum of (c).

4. The method for controlling the active suspension system of the train in the strong wind environment according to claim 3, wherein the step 3 comprises the following steps:

a) establishing a traditional fuzzy controller;

firstly, establishing a double-input single-output fuzzy control system, selecting the response speed and the response acceleration of a vehicle body as input variables of fuzzy control, and selecting actuating power required by an actuator in an active suspension system as output variables of the fuzzy control;

then, the response result under the conditions of the wind speed of 20m/s and the vehicle speed of 200Km/h is used as the domain of input and output variables, so that the whole control system is ensured to have a sufficiently wide detection and feedback interval;

the input and output discourse domains are:

B_v＝b_v/k_v＝[-E₁，E₁]

B_a＝b_a/k_a＝[-E₂，E₂]

F_a＝f_a/k_f＝[-F，F]

in the formula, k_v、k_a、f_aInput and output variable quantization factors respectively;

the input variable and the output variable are covered by 7 equal fuzzy sets: big Negative (NB), medium Negative (NM), small Negative (NS), Zero (ZO), small Positive (PS), medium Positive (PM), big Positive (PB), the corresponding control rule table is: in the above table, the corresponding control rules are:

if b is_v＝NB，b_aNB, then f_aNB ═ NB; if b is_v＝NM，b_aWhen NM, then f_aNB, etc. 49 rules;

b) establishing a variable universe fuzzy controller;

combining the vehicle body response rule under the running conditions of different wind speeds and vehicle speeds with a fuzzy control theory, introducing the concept of a telescopic factor to adjust the domain range under the conditions of different wind speeds and vehicle speeds, wherein the control rule is unchanged, and the variable domain equation is as follows:

B_vi＝[-α_i(U_χz，V)E₁，α_i(U_χz，V)E₁]

B_ai＝[-β_i(U_χz，V)E₂，β_i(U_χz，V)E₂]

F_ai＝[-γ_i(U_χz，V)F，γ_i(U_χz，V)F]

in the formula, B_vi、B_aiRespectively representing the interval after the input variable fuzzy theory domain is changed under the conditions of corresponding wind speed and vehicle speed, F_aiAnd (4) showing the interval after the variable fuzzy set discourse domain is changed at the moment i.

5. The method for controlling the active suspension system of the train in the strong wind environment as claimed in claim 4, wherein the step 4 comprises the following steps:

a) obtaining a vehicle body vibration signal detected at each moment, namely a train acceleration response result, and inputting the vehicle body vibration signal, namely the train acceleration response result, serving as an input variable into a variable domain fuzzy controller;

b) acquiring wind speed and vehicle speed data at each moment, generating a scaling factor, and inputting the scaling factor into a variable universe fuzzy controller;

c) the variable universe fuzzy controller controls the actuator to make the active suspension system output the required actuating force in real time.

Technical Field

The invention relates to the technical field of vehicle control, in particular to a control method of an active suspension system of a train in a strong wind environment.

Background

The land is a big thing in China, the amplitude of the land is wide, and the railway operation environment is extremely complex. The strong wind environment seriously threatens the safe operation of the train, aggravates the vibration of the train body and reduces the safety and the stability of the train operation. Therefore, the train active suspension system is often applied to control the safety and stability of the train in the strong wind environment through the theoretical method research of strong wind excitation. The research aiming at the control method of the train active suspension system in the strong wind environment is also paid more and more attention by domestic and foreign scholars.

At present, a train active suspension system based on fuzzy control has been widely researched, however, in consideration of randomness of wind speed and complexity of train response in a strong wind environment, the traditional fuzzy control method has the problems of single control rule and poor adaptive control capability, and therefore, a variable-domain fuzzy control train active suspension system suitable for the strong wind environment is provided.

Disclosure of Invention

The invention aims to solve the technical problems in the active suspension system of the train in the prior art, and provides a control method of the active suspension system of the train, which can improve the running safety and stability of the train in different wind speed environments.

In order to achieve the purpose, the invention adopts the following technical scheme:

a control method for an active suspension system of a train in a strong wind environment comprises the following steps:

step 1, calculating wind loads borne by a vehicle body under different wind speeds and vehicle speeds;

step 2, obtaining a full-size model of the train, and establishing a complete vehicle dynamics simulation model consisting of three rigid bodies of a train body, a bogie frame and a wheel pair by using SIMPACK; according to the dynamic simulation model, applying wind load to the vehicle body, calculating a vehicle body response result, and finishing the vehicle body response rules at different wind speeds and vehicle speeds;

step 3, combining the vehicle body response rules under different wind speed and vehicle speed running conditions with a fuzzy control theory, and designing a variable universe fuzzy controller;

and 4, acquiring the acceleration response result, the wind speed and the vehicle speed of the train detected at each moment, and enabling the active suspension system to output the required actuating power in real time through the variable universe fuzzy controller.

Further, the step 1 comprises solving the fluctuating wind speed, the train surface relative wind speed and the wind load model;

a) solving the pulsating wind speed:

the pulsating wind solving equation based on the harmonic synthesis method and the fast Fourier transform technology is as follows:

(j＝1，2，…，n)，(m＝1，2，…，n)，(p＝0，1，…，2N×n-1)

in the formula, Y_χj(delta t) represents the time interval of the fluctuating wind speed at the jth simulated wind speed point, Re represents the real part of an imaginary number, delta t represents a time interval, i represents the imaginary part, N represents the number of wind speed simulated points, delta omega represents a frequency increment, and N represents the number of sampling frequency points;

in the above formula, h_jm(Δ t) is expressed as:

in the formula (I), the compound is shown in the specification,is [0,2 π ]]Random phase angles, S, distributed uniformly within_χ(z) power spectrum of the pulsating wind speed, G_jm(ω) represents the j row m term of the correlation coefficient matrix;

in the above formula, G_jmThe expression (ω) is:

wherein R represents the spatial correlation of wind, c represents an exponential decay coefficient, Delta represents a horizontal spacing point distance, f represents a wind speed frequency, and U_χzRepresents the average wind speed at height Z;

according to the specific change of the wind along the height, the wind speed power spectrum adopts a Kaimal wind speed spectrum, and the expression of the Kaimal wind speed spectrum is as follows:

in the formula, S_χ(z) denotes the Kaimal wind velocity spectrum, σ, at height z_χ、L_χRespectively representing the standard deviation, the integral scale of turbulence, f of each directional component_LχA reduction frequency representing the corresponding directional component;

b) solving the relative wind speed on the surface of the train:

the expression of the relative wind speed on the surface of the train is as follows:

in the formula, beta represents an included angle between the wind direction and the train advancing direction, V represents train wind which is equal to the train running speed and opposite to the train running speed;

c) solving a wind load model;

the equivalent static wind force on the surface of the vehicle body is as follows:

in the formula, the equivalent static wind force F used on the surface of the train^stIncluding resistance F_u ^stLifting force F_w ^stAnd a torsional moment M_v ^st(ii) a u, w and v respectively represent a transverse direction, a vertical direction and a longitudinal direction; ρ is the air density; a is the windward area of the vehicle body, and the influence of a bogie and a wheel pair is ignored; h is the height from the center of mass of the vehicle body to the roadbed; c (psi) is aerodynamic coefficient;

the equivalent trembling array wind power at a certain point on the surface of the vehicle body is as follows:

in the formula, the j-point buffeting wind power also comprises resistance, lift force and torsional moment; u. of_j、w_jRepresenting the transverse and vertical components of the pulsating wind at the j point; b is_j、L_jRespectively showing the height and the width of the section of the vehicle body at the jth simulation point.

Further, step 2 comprises the steps of:

a) completing modeling of a dynamic simulation model: firstly, determining various structural parameters and suspension parameters of a train model, inputting the various parameters into a SIMPACK system, and establishing a whole train model consisting of three rigid bodies, namely a train body, a bogie frame and a wheel pair;

b) performing dynamic simulation: applying equivalent wind load to the centroid of the train body, wherein the kinetic equation of the train system in the wind field is as follows:

wherein M, C, K represents the mass matrix, damping matrix and stiffness matrix of the train system respectively,x represents the displacement response, velocity response and acceleration response of the train system, respectively, F^st、F^bfRespectively representing generalized vectors of equivalent static wind force and buffeting wind force acting on the centroid of the surface of the vehicle body;

c) finishing the response rule of the vehicle body at different wind speeds and vehicle speeds;

obtaining train response results by using SIMPACK, and arranging the speed response range b of the train system_vAcceleration response range b_aAnd the relative force f between the vehicle body and the bogie frame_a；

Setting different wind speeds and vehicle speeds to ensure that the wind speeds are differentThe speed V is less than or equal to 200Km/h, the body response ranges under different wind speeds and speeds are calculated, the body response rules corresponding to the different wind speeds and speeds are collated, and the expansion factor expression correlation rule is introduced, wherein the mathematical expression is as follows:

in the formula (I), the compound is shown in the specification,respectively represents the wind speed ofWhen the vehicle speed is V, the variable b is input_v、b_aAnd an output variable f_aThe scaling factor of (a) is determined, representing the response extreme value under the corresponding wind speed and vehicle speed, b_v(20,200)、b_a(20,200)、f_a(20,200) represents the input variable b at a wind speed of 20m/s and a vehicle speed of 200Km/h_v、b_aAnd an output variable f_aThe response extremum of (c).

Further, step 3 comprises the steps of:

a) establishing a traditional fuzzy controller;

firstly, establishing a double-input single-output fuzzy control system, selecting the response speed and the response acceleration of a vehicle body as input variables of fuzzy control, and selecting actuating power required by an actuator in an active suspension system as output variables of the fuzzy control;

then, the response result under the conditions of the wind speed of 20m/s and the vehicle speed of 200Km/h is used as the domain of input and output variables, so that the whole control system is ensured to have a sufficiently wide detection and feedback interval;

the input and output discourse domains are:

B_v＝b_v/k_v＝[-E₁，E₁]

B_a＝b_a/k_a＝[-E₂，E₂]

F_a＝f_a/k_f＝[-F，F]

in the formula, k_v、k_a、f_aInput and output variable quantization factors respectively;

the input variable and the output variable are covered by 7 equal fuzzy sets: big Negative (NB), medium Negative (NM), small Negative (NS), Zero (ZO), small Positive (PS), medium Positive (PM), big Positive (PB), the corresponding control rule table is: in the above table, the corresponding control rules are:

if b is_v＝NB，b_aNB, then f_aNB ═ NB; if b is_v＝NM，b_aWhen NM, then f_aNB, etc. 49 rules;

b) establishing a variable universe fuzzy controller;

combining the vehicle body response rule under the running conditions of different wind speeds and vehicle speeds with a fuzzy control theory, introducing the concept of a telescopic factor to adjust the domain range under the conditions of different wind speeds and vehicle speeds, wherein the control rule is unchanged, and the variable domain equation is as follows:

in the formula, B_vi、B_aiRespectively representing the interval after the input variable fuzzy theory domain is changed under the conditions of corresponding wind speed and vehicle speed, F_aiAnd (4) showing the interval after the variable fuzzy set discourse domain is changed at the moment i.

Further, step 4 comprises the steps of:

a) obtaining a vehicle body vibration signal detected at each moment, namely a train acceleration response result, and inputting the vehicle body vibration signal, namely the train acceleration response result, serving as an input variable into a variable domain fuzzy controller;

b) acquiring wind speed and vehicle speed data at each moment, generating a scaling factor, and inputting the scaling factor into a variable universe fuzzy controller;

c) the variable universe fuzzy controller controls the actuator to make the active suspension system output the required actuating force in real time.

The method for controlling the active suspension system of the train in the strong wind environment adopts the variable universe fuzzy controller based on the change of the wind speed and the vehicle speed, solves the problems of strong wind load excitation randomness, large train response variation range and single control rule of the active suspension system in the actual engineering, effectively improves the self-adaptive capacity and the control precision of the active suspension system of the train in different wind field environments, refines the control rule under different wind speed and vehicle speed running conditions, better ensures the dynamic performance of the train in different running working conditions in the strong wind environment, and is beneficial to the actual engineering application of the active suspension system of the high-speed train in the strong wind environment.

Drawings

FIG. 1 is a block diagram of the active suspension system of the train in a strong wind environment.

FIG. 2 is a flow chart of a train active suspension system control method under a strong wind environment.

The reference numerals have the following meanings: 1. a vehicle body; 2. a detection system; 3. a variable universe fuzzy controller; 4. an actuator; 5. a bogie frame; 6. a wheel set; 7. primary suspension; 8. and (5) secondary suspension.

Detailed Description

The invention is further described with reference to the following figures and detailed description.

Fig. 1 shows an active suspension system of a train according to the present invention, which includes a detection system, a variable domain fuzzy controller, and an actuator.

The detection system is used for acquiring a vehicle body vibration signal in real time.

And the variable universe fuzzy controller acquires a vehicle body vibration signal in real time according to the wind speed, the vehicle speed and the detection system and sends a control instruction.

The actuator outputs corresponding actuating force according to the control instruction sent by the variable universe fuzzy controller, so that the vibration of the train body is inhibited, and the comfort and the stability of the train are improved.

As shown in fig. 2, a method for controlling an active suspension system of a train in a strong wind environment includes the following steps:

step 1, calculating wind load of a vehicle body under different wind speeds and vehicle speeds.

By utilizing a harmonic synthesis method and a fast Fourier transform technology, based on a Kaimal wind speed spectrum and a Davenport coherent function, the pulsating wind speed and the relative wind speed on the surface of the train can be solved, and then a wind load model is solved.

a) Solving the pulsating wind speed:

the pulsating wind solving equation based on the harmonic synthesis method and the fast Fourier transform technology is as follows:

(j＝1，2，…，n)，(m＝1，2，…，n)，(p＝0，1，…，M×n-1)

in the formula, Y_χj(delta t) represents the time interval of the fluctuating wind speed at the jth simulated wind speed point, Re represents the real part of an imaginary number, delta t represents a time interval, i represents the imaginary part, N represents the number of wind speed simulated points, delta omega represents a frequency increment, and N represents the number of sampling frequency points;

in the above formula, h_jm(Δ t) is expressed as:

in the formula (I), the compound is shown in the specification,is [0,2 π ]]Random phase angles, S, distributed uniformly within_χ(z) power spectrum of the pulsating wind speed, G_jm(ω) represents the j row m term of the correlation coefficient matrix;

in the above formula, G_jmThe expression (ω) is:

wherein R represents the spatial correlation of wind, c represents an exponential decay coefficient, Delta represents a horizontal separation point distance, f represents a wind speed frequency,representing the average wind speed at height Z.

According to the specific change of the wind along the height, the wind speed power spectrum adopts a Kaimal wind speed spectrum, and the expression of the Kaimal wind speed spectrum is as follows:

in the formula, S_χ(z) denotes the Kaimal wind velocity spectrum, σ, at height z_χ、L_χRespectively representing the standard deviation, the integral scale of turbulence, f of each directional component_LχRepresenting the discounted frequency of the corresponding directional component.

b) Solving the relative wind speed on the surface of the train:

the expression of the relative wind speed on the surface of the train is as follows:

in the formula, beta represents an included angle between the wind direction and the advancing direction of the train, and V represents the train wind which is equal to the running speed of the train and opposite to the running speed of the train.

c) Solving a wind load model:

the equivalent static wind force on the surface of the vehicle body is as follows:

in the formula, the equivalent static wind force F acting on the surface of the train^stIncluding resistance F_u ^stLifting force F_w ^stAnd a torsional moment M_v ^st(ii) a u, w and v respectively represent a transverse direction, a vertical direction and a longitudinal direction; ρ is the air density; a is the windward area of the vehicle body, and the influence of a bogie and a wheel pair is ignored; h is the height from the center of mass of the vehicle body to the roadbed; c (psi) is the aerodynamic coefficient.

The equivalent trembling array wind power at a certain point on the surface of the vehicle body is as follows:

in the formula, the j-point buffeting wind power also comprises resistance, lift force and torsional moment; u. of_j、w_jRepresenting the transverse and vertical components of the pulsating wind at the j point; b is_j、L_jRespectively showing the height and the width of the section of the vehicle body at the jth simulation point.

Step 2, establishing a train simulation model and arranging train response rules;

by using SIMPACK software, a train simulation model can be established, the dynamic simulation can be completed by combining the wind load calculated in the step 1, and the response rule of the train body at different wind speeds and different train speeds can be worked out.

a) Completing modeling of a dynamic simulation model:

determining various structural parameters and suspension parameters of the train model, inputting the various parameters into the SIMPACK system, and establishing a whole train model consisting of three rigid bodies, namely a train body, a bogie frame and a wheel pair.

b) Performing dynamic simulation:

applying equivalent wind load to the centroid of the train body, wherein the kinetic equation of the train system in the wind field is as follows:

wherein M, C, K represents the mass matrix, damping matrix and stiffness matrix of the train system respectively,x represents the displacement response, velocity response and acceleration response of the train system, respectively, F^st、F^bfRespectively representing the generalized vectors of equivalent static and buffeting winds acting at the centroid of the vehicle body surface.

c) Finishing the response rule of the vehicle body at different wind speeds and vehicle speeds:

obtaining train response results by using SIMPACK, and arranging the speed response range b of the train system_vAcceleration response range b_aAnd the relative force f between the vehicle body and the bogie frame_a。

Setting different wind speeds and vehicle speeds to ensure that the wind speeds are differentThe vehicle speed V is less than or equal to 200Km/h, the steps are repeated, the vehicle body response ranges at different wind speeds and vehicle speeds are calculated, the vehicle body response rules corresponding to the different wind speeds and vehicle speeds are sorted, the expansion factor is introduced to express the relevant rules, and the mathematical expression is as follows:

in the formula (I), the compound is shown in the specification,respectively represents the wind speed ofWhen the vehicle speed is V, the variable b is input_v、b_aAnd an output variable f_aThe scaling factor of (a) is determined, representing the extreme value of the response at the speed corresponding to the wind speed, b_v(20,200)、b_a(20,200)、f_a(20,200) represents the input variable b at a wind speed of 20m/s and a vehicle speed of 200Km/h_v、b_aAnd an output variable f_aThe response extremum of (c).

And 3, combining the vehicle body response rules under different wind speed and vehicle speed running conditions with a fuzzy control theory, and designing a variable universe fuzzy controller. (the operating conditions of different wind speeds and vehicle speeds correspond to those in step 2 c)

By using a traditional fuzzy control theory and combining the vehicle body response rules under different wind speeds and vehicle speeds obtained in the step 2, a traditional fuzzy controller can be established firstly, and then the variable domain fuzzy controller is designed.

a) A conventional fuzzy controller is built.

Establishing a double-input single-output fuzzy control system, selecting the response speed and the response acceleration of a vehicle body as input variables of fuzzy control respectively, and selecting actuating power required by an actuator in an active suspension system as an output variable of the fuzzy control.

The response result under the conditions of the wind speed of 20m/s and the vehicle speed of 200Km/h is used as the domain of input and output variables, and the whole control system is ensured to have a wide enough detection and feedback interval.

The input and output variable fuzzy set universe is:

B_v＝b_v/k_v＝[-E₁，E₁]

B_a＝b_a/k_a＝[-E₂，E₂]

F_a＝f_a/k_f＝[-F，F]

in the formula, k_v、k_a、f_aThe input and output variable quantization factors are respectively.

The input and output variable fuzzy sets are covered by 7 equal fuzzy sets: big Negative (NB), medium Negative (NM), small Negative (NS), Zero (ZO), small Positive (PS), medium Positive (PM), big Positive (PB), the corresponding control rule table is:

in the above table, the corresponding control rules are:

if b is_v＝NB，b_aNB, then f_aNB if b_v＝NM，b_aWhen NM, then f_aNB, etc. 49 rules.

b) And establishing a variable universe fuzzy controller.

Combining the vehicle body response rule under the running conditions of different wind speeds and vehicle speeds with a fuzzy control theory, introducing the concept of a telescopic factor to adjust the domain range under the conditions of different wind speeds and vehicle speeds, wherein the control rule is unchanged, and the variable domain equation is as follows:

in the formula, B_vi、B_aiRespectively representing the interval after the input variable fuzzy theory domain is changed under the conditions of corresponding wind speed and vehicle speed, F_aiAnd (4) showing the interval after the variable fuzzy set discourse domain is changed at the moment i.

Step 4, the active suspension outputs the required actuating power in real time:

a) acquiring a vehicle body vibration signal detected at each moment, namely a train acceleration response node, serving as an input variable, and inputting the input variable into a variable domain fuzzy controller;

b) acquiring wind speed and vehicle speed data at each moment, generating a scaling factor, and inputting the scaling factor into a variable universe fuzzy controller;

c) the variable universe fuzzy controller controls the actuator to make the active suspension system output the required actuating force in real time.