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CN110001336B - Vehicle ISD suspension first-order ideal model based on ADD real network optimization - Google Patents

Vehicle ISD suspension first-order ideal model based on ADD real network optimization Download PDF

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CN110001336B
CN110001336B CN201910187516.4A CN201910187516A CN110001336B CN 110001336 B CN110001336 B CN 110001336B CN 201910187516 A CN201910187516 A CN 201910187516A CN 110001336 B CN110001336 B CN 110001336B
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suspension
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positive real
vertical
displacement
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CN110001336A (en
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杨晓峰
赵文涛
刘雁玲
沈钰杰
颜龙
徐旭
单琳
杨艺
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Zhejiang Yonglian Auto Parts Co ltd
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Jiangsu University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G17/00—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load
    • B60G17/015—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements
    • B60G17/018—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by the use of a specific signal treatment or control method
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G2600/00—Indexing codes relating to particular elements, systems or processes used on suspension systems or suspension control systems
    • B60G2600/18—Automatic control means
    • B60G2600/187—Digital Controller Details and Signal Treatment

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Abstract

The invention discloses a first-order ideal model of an ISD suspension of a vehicle based on ADD real network optimization, which is formed by connecting elements such as a spring, a damper, an inertial container and the like in series and in parallel. A first-order ideal model is obtained through Laplace transformation of an ISD suspension kinematics equation, addition of ADD control, artificial fish school optimization solution and positive and real judgment of a passive network comprehensive theory. The invention provides a novel ideal suspension model, which can enable the ISD suspension to obtain more ideal comprehensive performance in each frequency domain section.

Description

Vehicle ISD suspension first-order ideal model based on ADD real network optimization
Technical Field
The invention belongs to the field of vehicle suspension system modeling, and particularly relates to modeling of an ISD (inertial-Spring-Damper) suspension system of a vehicle using an Inerter-Spring. The invention relates to a dynamic first-order ideal model of an ISD suspension of a vehicle, in particular to an ISD suspension ideal model combining ADD (addition-drive-Damper) control and real network optimization.
Background
Smith, scholars of Cambridge university, put forward the idea of the inerter-spring inerter in 2003, and designed a rack-and-pinion type inerter-spring and a ball screw type inerter-spring, so that strict correspondence between a mechanical network and a circuit network is realized. The ISD suspension of the vehicle is a novel suspension formed by an inertial container, a spring and a damper for short, the inherent structure of the spring and the damper of the traditional suspension is broken through by the addition of the inertial container, the problem that the suspension performance is limited due to the lack of mass impedance is solved, the low-frequency resonance of the vehicle can be effectively reduced by the inertial container, the vibration isolation performance of the vehicle is improved, and a new direction and a new thought are provided for the suspension vibration control research.
In the second category of electromechanical similarity theory, the mass element corresponds to a capacitive element "grounded", which greatly limits the application of the network integration theory in electrical systems to mechanical systems. And the two-end characteristic element inertial container is provided, so that an RLC (resistance, inductance and capacitance) comprehensive method of a passive network in the electrical theory can be also suitable for a mechanical network system, namely, the passive inertial container, a spring and a damper element are used for realizing passively according to the response characteristic of a system impedance transfer function.
Chinese patent CN108932375A discloses a comprehensive passive implementation method of a vehicle ISD suspension network of a high-order impedance transfer function, which can implement the high-order transfer function in a low-order manner, but the structure of the method is generally the simplest form, and is mostly used for passive implementation, and the performance of the suspension system is not all improved.
At present, the 'excellent performance' and 'simple structure' of a suspension system are still a pair of contradictions, and how to design a novel efficient ISD suspension ideal model is still the focus of research.
Disclosure of Invention
Based on the reasons, the invention provides a first-order ideal model of the vehicle ISD suspension frame based on ADD real network optimization, and the suspension frame model capable of reducing vehicle vibration in the full frequency range is invented by utilizing the excellent performance of the ISD suspension frame in a low frequency range and combining the characteristics of ADD control improvement of medium and high frequency. The ideal model can be used as a suspension model of a large bus and can also be used as an ideal reference model in active control such as adaptive control and sliding mode variable structure control in a small vehicle, so that the comprehensive performance of the active ISD suspension is improved.
In order to construct the first-order ideal model, the technical scheme adopted by the invention is an optimization scheme based on an ADD real network, and the optimization scheme comprises the following steps:
step 1), establishing an ISD suspension quarter model, and performing Laplace transformation on a kinematic equation:
Figure GDA0002074525240000021
wherein m issIs sprung mass, muIs unsprung mass, k is support spring rate of the suspension, csSemi-active damping coefficient, k, for ADD controltIs the equivalent spring rate of the tire, s is the Rad's variable, ZsIs spring-loaded massOf vertical displacement, ZuIn the form of Laplace transform of vertical displacement of unsprung mass, ZrThe method is characterized in that the method is a Laplace transformation form of vertical input displacement of road surface unevenness, and T(s) is an impedance transfer function expression of a real network system;
step 2), writing an unknown structure into a speed type impedance transfer function form of a first-order real network:
Figure GDA0002074525240000022
wherein A, B, C, D are coefficients which are all non-negative numbers, and C, D is not all 0;
step 3), determining ADD control, wherein a damping coefficient discriminant formula is as follows:
Figure GDA0002074525240000023
wherein, c1、c2Two damping coefficient values;
further, in the step 3), c1And c2Are all larger than 0;
step 4), selecting a displacement input model of the road surface unevenness:
Figure GDA0002074525240000024
wherein u represents a running vehicle speed, Gq(n0) Representing the coefficient of road surface irregularity, w (t) representing white Gaussian noise with mean value zero, zrIs the vertical input displacement of the unevenness of the road surface;
step 5), determining an optimized artificial fish school optimization algorithm;
further, in the step 5), the root mean square value of the acceleration of the vehicle body and the root mean square value X of the dynamic stroke of the suspension are calculated2And the tire dynamic load root mean square value is taken as an optimization target, linear combination conversion is carried out, and a vehicle ISD suspension target of a first-order just-real network is obtainedFunction Y:
Figure GDA0002074525240000031
wherein, X1、X2、X3Respectively is the root mean square value, X, of the vehicle body acceleration, the suspension dynamic stroke and the tire dynamic load of the double-disposable ISD suspension to be optimized1pas、X2pas、X3pasRespectively are the root mean square values of the vehicle body acceleration, the suspension dynamic stroke and the tire dynamic load of the traditional passive suspension, and X1≤X1pas，X2≤X2pas，X3≤X3pas；
Further, in the step 5), the parameter to be optimized is X ═ (a, B, C, D, C)1,c2) And searching a global optimal solution by using the foraging behavior of the fish school:
Figure GDA0002074525240000032
wherein, XnextRepresenting the position of the fish group to move forward one step, and rand () representing the range of values [ -1,1 []Inner random number, Step, represents the maximum Step size of the fish movement, Xi＝(Ai,Bi,Ci,Di,c1i,c2i) For the current state position, Xj＝(Aj,Bj,Cj,Dj,c1j,c2j) Is a random state position, YiAnd YjRespectively representing the concentration of the position, namely the size of the objective function;
and 6), solving the ISD suspension structure by utilizing a passive network comprehensive theory.
The invention has the beneficial effects that: the invention has good inhibition effect on medium-high frequency of the suspension based on ADD control, and obtains a novel efficient suspension ideal model with excellent performance in a full frequency band by combining the characteristic that an ISD suspension system improves low-frequency resonance of a vehicle. The optimal initial parameters of the ISD suspension are rapidly obtained through an artificial fish school optimization algorithm, the concrete structure of the ISD suspension with excellent performance in a frequency domain and a time domain range is obtained through a network just-in-fact comprehensive theory, and the ISD suspension can be used as a novel ISD suspension model or an ideal reference model to create conditions for active control of an active ISD suspension.
Drawings
FIG. 1 is a schematic diagram of a first-order ideal model of an ISD suspension of a vehicle;
FIG. 2 is a schematic diagram of an ISD suspension quarter model of a vehicle;
FIG. 3 is an overall flow chart of the artificial fish swarm algorithm;
FIG. 4 is a schematic diagram of the final structure of the ISD suspension;
FIG. 5 is a performance index plot for the ISD suspension configuration;
wherein, (a) is a vehicle body acceleration response diagram, (b) is a suspension dynamic stroke response diagram, and (c) is a tire dynamic load response diagram.
Detailed Description
The invention will be further described with reference to the accompanying drawings, to which, however, the scope of the invention is not limited.
The invention discloses a vehicle ISD suspension first-order ideal model construction method based on ADD real network optimization, which comprises the following steps: step 1): establishing an ISD suspension quarter model; step 2): determining a speed type impedance transfer function of a first-order real network; step 3): determining an ADD control algorithm; step 4): selecting a displacement input model of the road surface unevenness; step 5): determining an optimized artificial fish school optimization algorithm; step 6): and solving the ISD suspension structure by utilizing a passive network comprehensive theory.
Wherein, the step 1) is specifically as follows: from the suspension model shown in fig. 2, the kinematic equation is established:
Figure GDA0002074525240000041
wherein m issIs sprung mass, muIs unsprung mass, k is support spring rate of the suspension, csSemi-active damping coefficient, k, for ADD controltFor equivalent spring rate of the tire, zsIs the vertical displacement of the sprung mass,
Figure GDA0002074525240000042
is the vertical velocity of the sprung mass,
Figure GDA0002074525240000043
is the vertical acceleration, z, of the sprung massuIs the vertical displacement of the unsprung mass,
Figure GDA0002074525240000044
is the vertical velocity of the unsprung mass,
Figure GDA0002074525240000045
vertical acceleration of unsprung mass, zrThe vertical input displacement of the road surface unevenness is T(s), and the T(s) is an impedance transfer function of the primary real network of the structure to be solved;
further, the air conditioner is provided with a fan,
Figure GDA0002074525240000046
(zs-zu)、kt(zu-zr) Vertical acceleration of the sprung mass, suspension dynamic travel and tire dynamic load are three dynamic performance indexes of the suspension system;
further, it is subjected to a lagrange transform:
Figure GDA0002074525240000047
wherein s is a Ralstonia variable, ZsIn the form of Laplace transformation of vertical displacement of sprung mass, ZuIn the form of Laplace transform of vertical displacement of unsprung mass, ZrA laplace transform form of vertical input displacement of road surface irregularities;
wherein, the step 2) is specifically as follows: writing the impedance transfer function of the structure to be solved into a first order form:
Figure GDA0002074525240000048
wherein A, B, C, D values are all greater than or equal to 0, and C, D values are not all 0.
Furthermore, according to the RLC (resistance, inductance and capacitance) comprehensive method of the passive network in the electrical theory, the one-time positive real impedance transfer function can be passively realized by using a passive network element, and the invention can be realized by using as few as a passive inertial container, a spring and a damper element in series-parallel connection to express t(s).
Wherein, the step 3) is specifically as follows: ADD control algorithm;
furthermore, the ADD control algorithm mainly improves the suspension performance by controlling the damping characteristic of the shock absorber, and has good improvement effect on all frequency bands except the vicinity of the natural frequency of the sprung mass;
further, the control strategy is as follows: when the acceleration of the vehicle body coincides with the relative velocity direction of the sprung mass and the unsprung mass, the damping coefficient of the control input is c1(ii) a Conversely, when the vehicle body acceleration is in the opposite direction to the relative velocity of the sprung and unsprung masses, the damping coefficient of the control input is c2；
Furthermore, the control principle of the suspension system can enable the suspension system to obtain better performance at medium and high frequencies, the performance is concentrated in the medium and high frequency range of the dynamic stroke of the suspension and the dynamic load of the tire, the current situation that the ISD suspension is not improved in the frequency band is made up, and the damping coefficient c of the ISD suspension issThe following equation is satisfied:
Figure GDA0002074525240000051
wherein, c1、c2Two damping coefficient values;
wherein, the step 4) is specifically as follows: the displacement input model of the road surface unevenness is selected and specific parameters are given as shown in table 1:
Figure GDA0002074525240000052
wherein u represents a running vehicle speed, Gq(n0) Representing the coefficient of road surface irregularity, w (t) representing white Gaussian noise with mean value zero, zrIs the vertical input displacement of the unevenness of the road surface.
Table 1 quarter suspension model parameters.
Figure GDA0002074525240000053
Wherein, the step 5) is specifically as follows: obtaining parameters A, B, C, D, c using an artificial fish swarm optimization algorithm1、c2The value of the parameter determines the performance index of the suspension, namely the mean square root value X of the acceleration of the vehicle body1Root mean square value X of suspension moving stroke2And tire dynamic load root mean square value X3；
Further, the vehicle body acceleration root mean square value, the suspension dynamic travel root mean square value and the tire dynamic load root mean square value are used as optimization targets, linear combination conversion is carried out, and a vehicle ISD suspension target function Y of a first-order just-real network is obtained:
Figure GDA0002074525240000054
wherein, X1、X2、X3Respectively the root mean square value, X, of the vehicle body acceleration, the suspension dynamic travel and the tire dynamic load of the disposable ISD suspension to be optimized1pas、X2pas、X3pasRespectively are the root mean square values of the vehicle body acceleration, the suspension dynamic stroke and the tire dynamic load of the traditional passive suspension, and the suspension performance constraint is X1≤X1pas，X2≤X2pas，X3≤X3pas；
Further, X1pas、X2pas、X3pasTypically fixed, so that the magnitude of the objective function Y is dependent on the RMS value X of the body acceleration1Root mean square value X of suspension moving stroke2And tire dynamic load root mean square value X3The influence of (a);
further, the artificial fish school mainly comprises five steps of fish school initialization, foraging behavior, herding behavior, rear-end collision behavior and random behavior, which are shown in fig. 3;
further, the parameter to be optimized is X ═ (a, B, C, D, C)1,c2) The foraging behavior is an updating process in the global optimum value, is the most main solving behavior of the fish school, and has the following specific algorithm:
Figure GDA0002074525240000061
wherein, XnextRepresenting the position of the fish group to move forward one step, and rand () representing the range of values [ -1,1 []Inner random number, Step, represents the maximum Step size of the fish movement, Xi＝(Ai,Bi,Ci,Di,c1i,c2i) For the current state position, Xj＝(Aj,Bj,Cj,Dj,c1j,c2j) Is a random state position, YiAnd YjRespectively representing the concentration of the position, namely the size of the objective function;
further, table 2 shows the results of parameter optimization:
Figure GDA0002074525240000062
wherein, the step 6) is specifically as follows: according to the optimization result, the impedance transfer function expression of the first-order real network is as follows:
Figure GDA0002074525240000063
further, the obtained one-time impedance transfer function t(s) is subjected to a structural inspection, and whether the most simple-implemented positive judgment condition is satisfied is sequentially inspected according to the existing research result and the simplest principle, and the three elements shown in fig. 4 are the structural schematic diagram of the first-order ideal t(s) of the present invention.
Table 3 shows the corresponding component parameters:
Figure GDA0002074525240000071
simulation verification is performed as follows:
a quarter model of a traditional passive suspension and an ADD control suspension is built through MATLAB/Simulink, and three performance indexes of vehicle body acceleration, suspension dynamic stroke and tire dynamic load are used as evaluation indexes of the performance of the suspension. Since the analysis and solution cannot be directly performed through the frequency response function, the sinusoidal excitation is used as the frequency domain road surface input:
zr(t)＝Amsin(2πft)
wherein A ismThe excitation amplitude is represented, the value is 0.01m, f represents the excitation frequency, and the value range is 0.01-15 Hz.
As shown in fig. 5, (a) is a vehicle body acceleration response map, (b) is a suspension dynamic stroke response map, and (c) is a tire dynamic load response map.
Table 4 is a comparison table of root mean square values of the suspensions:
Figure GDA0002074525240000072
the results show that the performance of the first-order ideal suspension model has obvious advantages, the design purpose is achieved, and the model can be used as an ideal ISD suspension structure model of a large vehicle or an ideal reference model of a small vehicle to improve the active control effect in consideration of the problem of arrangement space.
The examples are preferred embodiments of the present invention, but the present invention is not limited to the embodiments, and modifications, variations and substitutions by those skilled in the art may be made without departing from the spirit of the present invention.

Claims (6)

1.一种基于ADD正实网络优化的车辆ISD悬架一阶理想模型的方法，其特征在于，包括：1. a method for the first-order ideal model of vehicle ISD suspension based on ADD positive real network optimization, is characterized in that, comprises: 步骤(1)：建立ISD悬架四分之一模型：Step (1): Build a quarter model of the ISD suspension:
Figure FDA0003144890240000011
Figure FDA0003144890240000011
 其中，ms为簧载质量，mu为非簧载质量，k为悬架的支撑弹簧刚度，cs为ADD控制的半主动阻尼系数，kt为轮胎等效弹簧刚度，zs为簧载质量的垂向位移，
Figure FDA0003144890240000012
为簧载质量的垂向速度，
Figure FDA0003144890240000013
为簧载质量的垂向加速度，zu为非簧载质量的垂向位移，
Figure FDA0003144890240000014
为非簧载质量的垂向速度，
Figure FDA0003144890240000015
为非簧载质量的垂向加速度，zr为路面不平度的垂向输入位移，T(s)为一次型正实网络的阻抗传递函数；对上述ISD悬架四分之一模型进行拉氏变换得到：where m s is the sprung mass, mu is the unsprung mass, k is the supporting spring stiffness of the suspension, c s is the semi-active damping coefficient controlled by ADD, k t is the equivalent spring stiffness of the tire, and z s is the spring stiffness vertical displacement of the loaded mass,
Figure FDA0003144890240000012
is the vertical velocity of the sprung mass,
Figure FDA0003144890240000013
is the vertical acceleration of the sprung mass, z u is the vertical displacement of the unsprung mass,
Figure FDA0003144890240000014
is the vertical velocity of the unsprung mass,
Figure FDA0003144890240000015
is the vertical acceleration of the unsprung mass, z r is the vertical input displacement of the road surface roughness, and T(s) is the impedance transfer function of the primary positive real network. Transform to get:
Figure FDA0003144890240000016
Figure FDA0003144890240000016
 其中，s为拉氏变量，Zs为簧载质量的垂向位移的拉普拉斯变换形式，Zu为非簧载质量的垂向位移的拉普拉斯变换形式，Zr为路面不平度的垂向输入位移的拉普拉斯变换形式；Among them, s is the Laplace variable, Z s is the Laplace transform form of the vertical displacement of the sprung mass, Z u is the Laplace transform form of the vertical displacement of the unsprung mass, and Z r is the road surface unevenness Laplace-transformed form of the vertical input displacement in degrees; 步骤(2)：一次型正实阻抗传递函数T(s)以速度型阻抗传递函数形式表示如下：Step (2): The first-order positive real impedance transfer function T(s) is expressed as a velocity-type impedance transfer function as follows:
Figure FDA0003144890240000017
Figure FDA0003144890240000017
 其中，A、B、C、D为系数，取值均大于等于0，且C、D不全为0；Among them, A, B, C, D are coefficients, the values are all greater than or equal to 0, and C and D are not all 0; 步骤(3)：设置ADD的控制策略为：当车身加速度与簧载质量和非簧载质量的相对速度方向一致时，控制输入的阻尼系数为c1；反之，当车身加速度与簧载质量和非簧载质量的相对速度方向相反时，控制输入的阻尼系数为c2；阻尼系数cs需满足下式：Step (3): Set the control strategy of ADD as follows: when the body acceleration is consistent with the relative velocity directions of the sprung mass and the unsprung mass, the damping coefficient of the control input is c 1 ; on the contrary, when the body acceleration and the sprung mass and the When the relative velocity directions of the unsprung masses are opposite, the damping coefficient of the control input is c 2 ; the damping coefficient c s must satisfy the following formula:
Figure FDA0003144890240000018
Figure FDA0003144890240000018
 其中，c1、c2为两个阻尼系数值，且满足下式：Among them, c 1 , c 2 are two damping coefficient values, and satisfy the following formula: c1＞0,c2＞0；c 1 >0, c 2 >0; 步骤(4)：选取路面不平度的位移输入模型；Step (4): Select the displacement input model of road surface roughness; 步骤(5)：采用人工鱼群优化算法来获得参数A、B、C、D、c1、c2；Step (5): using artificial fish swarm optimization algorithm to obtain parameters A, B, C, D, c 1 , c 2 ; 步骤(6)：根据优化结果，将获得的参数A、B、C、D、c1、c2代入一次型正实网络的阻抗传递函数T(s)的表达式依次通过最简的正实约束条件即得到车辆ISD悬架一阶理想模型。Step (6): According to the optimization results, substitute the obtained parameters A, B, C, D, c 1 , and c 2 into the expression of the impedance transfer function T(s) of the first-order positive real network through the simplest positive real network in turn. The constraint conditions are the first-order ideal model of the vehicle ISD suspension. 2.根据权利要求1所述的一种基于ADD正实网络优化的车辆ISD悬架一阶理想模型的方法，其特征在于，其中，所述步骤(5)中采用人工鱼群优化算法来获得参数A、B、C、D、c1、c2。2. the method for the first-order ideal model of vehicle ISD suspension based on ADD positive real network optimization according to claim 1, is characterized in that, wherein, in described step (5), adopt artificial fish swarm optimization algorithm to obtain Parameters A, B, C, D, c 1 , c 2 . 3.根据权利要求2所述的一种基于ADD正实网络优化的车辆ISD悬架一阶理想模型的方法，其特征在于，所述步骤(5)中的人工鱼群优化算法包括：3. the method for a kind of vehicle ISD suspension first-order ideal model based on ADD positive real network optimization according to claim 2, is characterized in that, the artificial fish swarm optimization algorithm in described step (5) comprises: 步骤(5.1)：确定待优化参数为X＝(A,B,C,D,c1,c2)；Step (5.1): determine the parameter to be optimized as X=(A, B, C, D, c 1 , c 2 ); 步骤(5.2)：将车身加速度均方根值、悬架动行程均方根值和轮胎动载荷均方根值作为优化目标，并进行线性组合转换，得到一阶正实网络的车辆ISD悬架目标函数Y：Step (5.2): Take the root mean square value of the body acceleration, the root mean square value of the suspension dynamic travel and the root mean square value of the tire dynamic load as the optimization target, and perform linear combination transformation to obtain the vehicle ISD suspension of the first-order positive real network Objective function Y:
Figure FDA0003144890240000021
Figure FDA0003144890240000021
 其中，X1、X2、X3分别为待优化的一次型ISD悬架的车身加速度、悬架动行程与轮胎动载荷的均方根值，X1pas、X2pas、X3pas分别为传统被动悬架的车身加速度、悬架动行程与轮胎动载荷的均方根值，且X1≤X1pas，X2≤X2pas，X3≤X3pas；Among them, X 1 , X 2 , and X 3 are the root mean square values of the body acceleration, suspension dynamic stroke and tire dynamic load of the one-time ISD suspension to be optimized, respectively, and X 1pas , X 2pas , and X 3pas are the traditional passive The root mean square value of suspension body acceleration, suspension dynamic travel and tire dynamic load, and X 1 ≤ X 1pas , X 2 ≤ X 2pas , X 3 ≤ X 3pas ; 步骤(5.3)：觅食行为的具体算法为：Step (5.3): The specific algorithm of foraging behavior is:
Figure FDA0003144890240000022
Figure FDA0003144890240000022
 其中，Xnext表示鱼群向前进一步的位置，rand()表示取值范围[-1,1]内的随机数，Step表示鱼群移动的最大步长，Xi＝(Ai,Bi,Ci,Di,c1i,c2i)为当前状态位置，Xj＝(Aj,Bj,Cj,Dj,c1j,c2j)为随机状态位置，Yi与Yj分别表示所处位置的浓度，即目标函数Y的大小。Among them, X next represents the further position of the fish school, rand() represents a random number in the value range [-1,1], Step represents the maximum step size of the fish school moving, X i =(A i ,B i ,C i ,D i ,c 1i ,c 2i ) is the current state position, X j =(A j ,B j ,C j ,D j ,c 1j ,c 2j ) is the random state position,Y i and Y j Respectively represent the concentration at the location, that is, the size of the objective function Y. 4.根据权利要求1所述的一种基于ADD正实网络优化的车辆ISD悬架一阶理想模型的方法，其特征在于，其中所述步骤(2)中正实约束条件为：4. the method for a kind of vehicle ISD suspension first-order ideal model based on ADD positive real network optimization according to claim 1, is characterized in that, in wherein said step (2), positive real constraint condition is: A、B、C、D取值均大于0，且C、D不全为0。The values of A, B, C, and D are all greater than 0, and C and D are not all 0. 5.根据权利要求1所述的一种基于ADD正实网络优化的车辆ISD悬架一阶理想模型的方法，其特征在于，其中所述步骤(4)具体为选取路面不平度的位移输入模型：5. the method for a kind of vehicle ISD suspension first-order ideal model based on ADD positive real network optimization according to claim 1, is characterized in that, wherein said step (4) is specifically to select the displacement input model of road surface roughness :
Figure FDA0003144890240000031
Figure FDA0003144890240000031
 其中，u表示行驶车速，Gq(n0)表示路面不平度系数，w(t)表示均值为零的高斯白噪声，zr为路面不平度的垂向输入位移。Among them, u is the driving speed, G q (n 0 ) is the road roughness coefficient, w(t) is Gaussian white noise with zero mean, and z r is the vertical input displacement of road roughness. 6.根据权利要求1所述的一种基于ADD正实网络优化的车辆ISD悬架一阶理想模型的方法，其特征在于，其中所述步骤(4)具体为选取路面不平度的位移输入模型为：zr(t)＝Amsin(2πft),其中，Am表示激励幅值，取值0.01m，f表示激振频率，取值范围为0.01-15Hz。6. a kind of method based on ADD positive real network optimization vehicle ISD suspension first-order ideal model according to claim 1, is characterized in that, wherein said step (4) is specifically to select the displacement input model of road surface roughness is: z r ( t )=Am sin(2πft), where Am represents the excitation amplitude, which is 0.01m, and f represents the excitation frequency, which ranges from 0.01 to 15Hz.
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