AU2020102015A4 - A vehicle lateral stability control method based on the heuristic algorithm - Google Patents
A vehicle lateral stability control method based on the heuristic algorithm Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/02—Control of vehicle driving stability
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
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Abstract
ABSTRACT
This invention discloses a vehicle lateral stability control method based on a
heuristic algorithm, which includes the following steps: collecting vehicle parameters.
The parameters to be collected include inherent parameters and real-time parameters of
the vehicle. Inherent parameters include the total mass of the vehicle m, the moment of
inertia I, the distances from the front and rear axles to the center of gravity of the
vehicle, f and r, and the covering stiffness of the front and rear wheels, Cf and Cr.
These parameters are inherent to the vehicle and can be collected and stored in advance.
A heuristic algorithm is used to solve the output-feedback controller in this invention,
and the complicated iteration in traditional solving method can be avoided. By
considering the longitudinal speed and tire cornering stiffness of the vehicle as
uncertain parameters, the controller designed in this invention is more robust. The
sideslip angle and raw rate of the vehicle can be controlled in a reasonable level by
implement the calculated yaw moment, and the vehicle lateral stability and handling
performance can also be improved. The dangerous situations during the driving can be
avoided effectively.
FIGURES
Fyr F Y Yp
x
ar A ,
T if
Fig. 1 Linear 2-DOF vehicle model
Measurement of
vehicle parameters
Steering
inu+| Vehicle state- Ks Vhl t atic K"f Controller Control force Acutr
feedback model output-feedback calculations distribution *Atatr
model
Fig. 2 Control system diagram
17
Description
Fyr YpFY
x
ar A
, T if
Fig. 1 Linear 2-DOF vehicle model
Measurement of vehicle parameters
Steering inu+| Vehicle state- Ks Vhl t atic K"f Controller Control force Acutr feedback model output-feedback calculations distribution *Atatr model
Fig. 2 Control system diagram
Technical Field
This invention relates generally to a vehicle stability control method and, more
particularly, to a vehicle lateral stability control method based on a heuristic algorithm.
Description of the Related Arts
With the continuous improvement of people's living standards, the automobile
ownership is rising rapidly. Meanwhile, traffic safety has become a social problem.
China has one of the highest traffic accident rates in the world, which has led to high
attentions to driving safety. The safety protection of automotive can be divided into
passive and active safety according to the difference in its action time. Passive safety
protection utilizes airbags, high-strength vehicle body and other protective measures to
reduce the harm on passengers in accidents. And the active safety protection technology
focuses on improving the stability of vehicle and includes chassis dynamics control,
electronic stability program, and four-wheel steering system. By designing the structure
and control scheme of the automobile based on the dynamics characteristics, the attitude
detection, operating correction, alarm and other ways to actively prevent the emergence
of accidents can be realized. Among them, ABS, ASR and so on are typical
representatives of active safety technologies. In the earlier studies, most researches
focused on passive safety. However, with the development of society, traffic pressure
is becoming larger due to more and more automobiles, and passive safety technology
cannot meet the requirements of automotive safety. Hence the active safety based on
prevention has become an urgent requirement of traffic and one of the most important
research directions about automobile safety performance of this century. The direct yaw
moment control (DYC) is aimed at reducing the impact of drivers' operating on the
safety of vehicle motion. In the various driving conditions of the vehicle, by adjusting
the force of each wheel, the yaw moment is generated to overcome excessive or
insufficient steering. In this way, the vehicle is actively controlled, and the handling
stability is improved under extreme conditions such as high speed and bad roads.
In the existing arts, Chinese Patent CN107215329A discloses a distributed drive
electric vehicle lateral stability control method based on ATSM. This method is also for
the stability of vehicle lateral dynamics, and used one of the second-order sliding mode
variable structure algorithm, the spiral sliding mode control algorithm. However, the
uncertain factors of vehicle parameters are not considered in that method. Hence the
designed controller cannot guarantee the stability when the vehicle parameter changes
greatly.
The purpose of this invention is to overcome the shortcomings of the prior art and
provide a vehicle lateral stability control method based on heuristic algorithm. The
designed method can improve the lateral stability and handling performance of the
automobile, and reduce the possibility of dangerous situations during the driving.
In order to ensure the safety and comfort of vehicle in driving, it is necessary to
control the sideslip angle and yaw rate of the vehicle in a given range. When the vehicle
is running at high speed, it needs fast response, good stability and safety. When the
vehicle is running at low speed, it requires not only maintaining lateral stability but also
improving the maneuverability. So, the controlled sideslip angle of vehicle should be
close to zero. In addition, the yaw rate represents the deflection of the vehicle around
the vertical axis and indicates the degree of stability. If the yaw rate reaches a threshold,
the vehicle is more likely have a risk of sliding or swaying. Therefore, these two
parameters, sideslip angle and yaw rate, should be noticed in vehicle operation.
Considering some parameters are difficult to measure during driving in real time, this
invention proposed a yaw moment control method based on output-feedback, and a new
heuristic algorithm is used to solve the output-feedback controller. Considering the
vehicle speed and tire cornering stiffness as uncertain parameters, the required yaw
moment is calculated by the designed controller. By regulating the above two
parameters (sideslip angle and yaw rate) in a suitable range, the vehicle lateral stability
and handling performance can be improved and the possibility of dangerous situations
during the driving can be reduced. Based on the heuristic algorithm, in this invention,
the state-feedback controller gain of the system needs to be solved first, and then it is
used to solve the output-feedback controller gain.
Specifically, the proposed vehicle lateral stability control method based on
heuristic algorithm includes the following steps:
Step 1: Collect the parameters of the vehicle.
The parameters that need to be collected include inherent parameters and real-time
parameters.
Inherent parameters include the total mass of the vehicle m, the moment of inertia
I, the distances from the front and rear axles to the center of gravity of the vehicle,If
and r, and the cornering stiffness of the front and rear wheels, Cf and Cr. These
parameters are inherent to the vehicle and can be collected and stored in advance.
Real-time parameters include the front wheel steering angle8, vehicle longitudinal
velocity vyaw rate r, sideslip angle . In these parameters, by using the steering wheel
angle which collected by the sensor, the front wheel steering angle can be calculated by
the parameters of the steering transmission mechanism. The yaw rate of vehicle can be
measured by the gyroscope. The longitudinal speed and the sideslip angle of the vehicle
body can be calculated by combining the steering wheel comer, yaw rate, the
acceleration of the various direction of vehicle body and the state parameter estimator.
The acceleration of the various direction of the body can be obtained by the gyroscope.
Step 2: Establish the state-feedback control model which contains
uncertainties of the vehicle based on the collected parameters.
In this step, the two-degree-of-freedom (2-DOF) vehicle model is established first
as shown in Fig. 1.
CG represents the center of gravity of the vehicle, m is the total mass of the vehicle,
p is the sideslip angle of the vehicle, r is the yaw rate of the vehicle, r is the
derivative of the yaw rate, Fyf and Fyr are the front and rear lateral tire forces,
respectively. vy and v- are the lateral and longitudinal velocity of vehicle, 9, is the
lateral acceleration of the vehicle, Iz is the moment of inertia, AM is the external yaw
moment, which can be produced by steering and braking systems, 1f and r are the
distances from the front and rear axis to the CG, respectively. Based on the Newton's
law of mechanics, the following equations can be obtained.
mv (Q+r)= F,,+ F IJZ=lfF -lF+AM,
where
Fyf = Cfa, F, = Ca, af = - Lrr -8,a,= l r VXx Vxx
where Cfand Cr are the cornering stiffnesses of the front and rear tires, af and ar are the
tire slip angles of the front and rear tires.
Then choosing the vehicle sideslip angle P and yaw rate r as the state variables,
the state-space model of the vehicle lateral dynamics system can be obtained as
i(t = Ax(t)+ By(t)+B2 u(t)
where
x=[p8 r]T, u(t)= AM, w(t)=5, ~ _ C+C, +C,. - Cf if 2 03 A= x __ _ I B= x B2=1 Cf,CIf C,l + +Crl,2 1 C~l ,if_ I Izvx j
Considering the vehicle longitudinal velocity vx in the above model as the
uncertain parameter, define q 1 =1/v,,q 2 =1/vx , and the vehicle model can be
described as
c(t)= A(q)x(t)+ B (q)w(t)+ B2 u(t)
where
__C+ C' q -1+ Crl, - C, i2 q1 A(q)=C, C 1+C, ,2 B,(q)=L I11 I I12+Cj2C
Assume the longitudinal velocity vx changes in a range, the values of qj will change
in the range [q, q], where i =1, 2. Then the value of q will be in a polytope which
can be described as
qe 0:= Co{piP 2 ,P3,P4 } a p, a E- A
where
A:= (a~a 2 aa 4 ):a> 0, ai=1
pA - p= A -4F , P3-[l - , A4 |~ q2 j _q2 q2 q2
In these formulas, the line above the variable means the maximum of the variable,
the line under the variable means the minimum of the variable. For example, when the
value of vx is maximum in the range, the q is used to represent the minimum of qj.
Furthermore, considering the tire cornering stiffness in the above model as the
uncertain parameters, they can be defined as the following form
Cf = Cf0 + ACI Cr = C 0 + AC
where Co, Cro are the nominal values of Cf, Cr, and AC1 ,ACr are the uncertain part
of C, Cr. AC 1 , AC, are defined as AC, = pC, ACr = p,C, , where P,IPr are
time-varying coefficients and satisfy Ipf 11 , |P, r1, Cf, C, are the maximum of
uncertainties.
Based on the above analysis, the uncertain vehicle system can be formatted as
4 4 i(t)= ja(q)(A.+AA)x(t)+ a,(q)(B1 +AB 1,)w(t)+B2u(t) M= j=1
where
4 ACf+ ACC,l - ACf /I m m A=A+AA, A A =aAA1 = ACt, - ACfIf AC/l f + AC/l2
, ACf 1 Bli =Bfi1 +ABi, AB 1 = aAB1 ,= AMi 4IF
AA, AB, can be expressed as the following form
AA=HfE.(q),AB=HFE(q),H=[I I],F=diag{pf,p,}
Ea(q)=[Eaf(q) Ea(q)] , E(q)=[Ebf(q) 01x2]
-C, -C I, 4 -C, -Cl, q2
Eaf(q)= aEa,= m m E ,(q)= azE, =1-C if -C,,1 -Cl,. -C ,, Cff Cf/f,
Ebf(q) = ,aEbf(= aiE,, Ea (q) aEi Cf/If =
To obtain better stability and handling performance, the sideslip angle of vehicle
should be close to 0, and the yaw rate should be close to the reference that defined as
r= 1 5 (If+,)K~vx
where Ku is an intrinsic parameter of the vehicle body.
Then select the output variables of system as
z(t)= |3 = Cx(t)+D,(q)w(t) c,.(r -rd),I=
where c6, Cr are the weighting coefficients of the two controlled variables.
C 1 =diag{c,,c,}, D,(q)=[0,-cq / (lf+l,)K]'.
Define the state-feedback controller of the system as uf =Ks -x, the closed-loop
system can be described as
(t)= Afx(t)+ Bw(t), z(t)=Cjx(t)+Dw(t)
where Af=A+B2Kg,
4 4 4 A(q)= a,(q)(A±+AA.),B,(q)= a (q)(Ei+±ABi),D,(q)= a,(q)Di.
Step 3: Solve the state-feedback controller gain.
In step 2, the vehicle dynamics model is established for the controller design. For
this model, the state-feedback controller gain should be calculated. Here the H. gain of
the system is selected to measure output z(t). Define the H. norm of the system as
T;|| = sup 2
where ||T ll. is the H. norm, 1z 2 and ||oll2 represent the 2-norm of z and 6. L 2
represents the set of signals with finite energy. In addition, considering the saturation
of actuator, the control force u should satisfy u112 Um..
Based on the above analysis, the controller gain Ks can be obtained by solving the
following two linear matrix inequalities (LMls).
AX+]B2 W+(AX+B2W)T Bi (C1X)T yH XET] * -I DT 0 ET * * -y I 0 0 <0,
* * * * -I7
mU~axJ W 1 <0, L* X where umax and y. are given positive scalars, e and r are positive scalars which need to
becalculated. Wand X are matrix variables that need to be solved, where Xis a positive
symmetric matrix, and i=1,2,3,4. By solving W and X, the controller gain Ks can be
obtainedbasedon Kf =WX-1.
Step 4: Establish the system model of vehicle for output-feedback controller
design and solve the output-feedback controller gain.
Similar to the step 2, considering the static output-feedback controller as
u = K, -y = K, Cyx(t)
where C,=diag{0, 1} , then the closed-loop system with static output-feedback
controller can be established as
*(t)= A,,x(t)+ B(q)w(t), z(t)= C1 x(t)+D1 (q)w(t),
where Af=A(q)+B 2KsofC,, the other parameters are the same as in step 2.
Furthermore, solve the following LMI
S+{R;+{T+oU <0
where
I 0 0 0 P 07 Q= A B, 0 , ;i=[K 0 -I| LC, 0 -F], 1 1 0 0 C, Dj, 0 -0 0 4
-,H=ANN 1 +A-FEE,+diag{0,-I,0}, E,=[E, E 0], E(q)= aE,
y. is a given positive scalar, K = Kg and the Kg is the state-feedback controller gain
which solved in step 3. L is a positive scalar that need to be solved. Li, F, P are matrices
with appropriate dimensions that need to be solved, and P is a positive symmetric matrix, i =1, 2,3,4.
The output-feedback controller gain can be obtained as
K , = F-1- aL
And the required external yaw moment can be calculated by the following formula
u=AM =K Cx(t)
The external yaw moment can be allocated to four wheels via the moment
distribution system and generated by the tire forces, thus the vehicle lateral stability can
be ensured. This process are shown in Fig. 2.
It should be noticed that the controller gain Ksor is calculated offline. The
parameters that need to be calculated in real time include the front wheel steering angle
6, the vehicle longitudinal velocity vx and the yaw rate r.
The heuristic algorithm is used in this invention to solve the output-feedback
controller, which avoids the complicated iterations in the traditional solution method.
Considering the vehicle longitudinal speed and tire cornering stiffness as uncertain
parameters, the controller designed by this invention is more robust. By solving the
appropriate external yaw moment and implementing it, the sideslip angle and yaw rate
of vehicle can be controlled in a reasonable range, thus the lateral stability and handling
performance of vehicle can be improved, and the possibility of dangerous situations can
be reduced. Considering the unmeasurable state variable, the static output-feedback is
used to design the controller.
Compared with the previous method, this invention has the following advantages:
The foregoing discussion discloses and describes merely exemplary embodiments
of the present invention. One skilled in the art will readily recognize from such
discussion and from the accompanying drawings and claims that various changes,
modifications and variations can be made therein without departing from the spirit and
scope of the invention as defined in the following claims.
Claims (2)
1. A vehicle lateral stability control method based on heuristic algorithm is designed, and includes the following steps: Step 1: Collect the parameters of the vehicle; The parameters that need to be collected include inherent parameters and real-time parameters; Inherent parameters include the total mass of the vehicle m, the moment of inertia I, the distances from the front and rear axles to the center of gravity of the vehicle, If and r, and the cornering stiffness of the front and rear wheels, C and Cr; These parameters are inherent to the vehicle and can be collected and stored in advance; Real-time parameters include the front wheel steering angle 6, vehicle longitudinal velocity vx, yaw rate r, sideslip angles#; In these parameters, by using the steering wheel angle which collected by the sensor, the front wheel steering angle can be calculated by the parameters of the steering transmission mechanism; The yaw rate of vehicle can be measured by the gyroscope; The longitudinal speed and the sideslip angle of the vehicle body can be calculated by combining the steering wheel corner, yaw rate, the acceleration of the various direction of vehicle body and the state parameter estimator; The acceleration of the various direction of the body can be obtained by the gyroscope; Step 2: Establish the state-feedback control model which contains uncertainties of the vehicle based on the collected parameters; In this step, the two-degree-of-freedom (2-DOF) vehicle model is established first as shown in Fig; 1; Based on the Newton's law of mechanics, the following equations can be obtained;
mv( + r)= +
I 1 f= lF -lF,+ AM
where
=C yar Fi r irr -F,= CaF,=a, , ' = 8 -,r - r a = V,r x x
where Cf and Cr are the cornering stiffnesses of the front and rear tires, af and ar are the tire slip angles of the front and rear tires;
Then choosing the vehicle sideslip angle 8 and yaw rate r as the state variables,
the state-space model of the vehicle lateral dynamics system can be obtained as
.(t = Ax(t)+By(t)+ B2 u(t)
where
x=[ r]T, u(t) = AMz, W(t) =
, ~ j C+ C, C1l, - Cf l, C, f - -1+ ____r Cf/f1 2 0] C'l- j, Cfl Clf ++C'12 B~, , 2 2 A I___I__ - x v__ , _ I,~x B2[
Considering the vehicle longitudinal velocity vx in the above model as the
uncertain parameter, define q,=1/v,,q 2 =1/V22, and the vehicle model can be
described as
-(t = A(q)x(t)+B (q)w(t)+ B2 u(t)
where
_ Cf+ Cr q1 -1+ Crir -Cl q2 1Cf1 A(q)= C ________I Ml Cl CfCB12Cl2 +Cf/f2 , B(q)=M fr fl - f ri fLz-q fi I I
Assume the longitudinal velocity vx changes in a range, the values of qi will change
in the range [q, q], where i = 1, 2; Then the value of q will be in a polytope which
can be described as
qE- 0:= C0 (P11p21P31 4j Y ipi,aE- A
where
A:= (aa2 aa4 )a 0, a =1
=j2 2 j2 _2
In these formulas, the line above the variable means the maximum of the variable, the line under the variable means the minimum of the variable; For example, when the
value of vx is maximum in the range, the q is used to represent the minimum of q;
Furthermore, considering the tire cornering stiffness in the above model as the uncertain parameters, they can be defined as the following form
C,=C,0 + AC, C,=C,0+ AC,
where Co, Cro are the nominal values of C, Cr, and AC, AC, are the uncertain part
of C, Cr; AC 1 , AC, are defined as AC, = pC,, ACr= p,C, where p,, p, are
time-varying coefficients and satisfy Ipf 11l, |p,r l1 l, C,, C,. are the maximum of
uncertainties; Based on the above analysis, the uncertain vehicle system can be formatted as 4 4 (t)=Zai(q)(A±+AA)x(t)+ a1 (q)(Bu+ABj)w(t)+B2 u(t) M= i=1
where
4 AC+ AC, ACl, - ACl. m m A=A+AA, AA=ZaiAA= C 2 MAC,, - ACJl, ACf + AC,, AC/-A. qf
Bli=Bli+ XiI, ACM 1 AB®ajBj
Bu =BE1 +AB , , l=i=A~ =
AA, AB, can be expressed as the following form
AA=HfEjq),AB=HfE(q),H=[ I],I,=diag{p,,p,}
Ea(q)=[E(q) E,.(q)] ,E(q)=[Ef(q) 01X2]
4 q2 =1 4 9qr1 q2 Ef(q)= aEa= -Cl -C 2 ,Ea,.(q)= aE.,= _ M=1 fI f f i=1 , , e q,
Cfq,
Ejf(q)= aEbfi = , E.(q)= aiE, E(q) aa,E, i=1 Eif
To obtain better stability and handling performance, the sideslip angle of vehicle
should be close to 0, and the yaw rate should be close to the reference that defined as
= 1 (if,+,)Kv,
where K, is an intrinsic parameter of the vehicle body;
Then select the output variables of system as
z(t) = c, 1= Cx(t)+ D1 (q)w(t) c, (r - rd,I=
where C, c, are the weighting coefficients of the two controlled variables;
C1 = diag{c, c,}, D,(q)=[0, - cq / (lf +lr)K]';
Define the state-feedback controller of the system as us =K -x, the closed-loop
system can be described as
*(t)= Afx(t)+Bjw(t), z(t)=Cx(t)+D 1 w(t)
where Asj=A+B 2Ksg
4 4 4 A(q)= jac(q)(, + A4), B(q)=Lai(q)(i + ABI), DI(q)= a,(q)Di i=1 j=1i1
Step 3: Solve the state-feedback controller gain;
In step 2, the vehicle dynamics model is established for the controller design; For
this model, the state-feedback controller gain should be calculated; Here the H. gain of
the system is selected to measure output z(t); Define the H. norm of the system as
T| = sup 2
where |Ti, is the H. norm, |z|2 and ||5|2 represent the 2-norm of z and 6; L 2
represents the set of signals with finite energy; In addition, considering the saturation
of actuator, the control force u should satisfy [l u112 U.
Based on the above analysis, the controller gain Kfcan be obtained by solving the following two linear matrix inequalities (LMls); AX+B 2 W+(AX+B2 W)T B1, (C1X) T yH XET] * -I DT 0 ET * * -y I 0 0 <0, * * -171 0 * * * * -171]
<0, L* ~X 1 1
where umx and y. are given positive scalars, e and r are positive scalars which need to be calculated; W and Xare matrix variables that need to be solved, where Xis a positive symmetric matrix, and i=1,2,3,4; By solving W and X, the controller gain Kg can be
obtained based on Kf = WX-';
Step 4: Establish the system model of vehicle for output-feedback controller design and solve the output-feedback controller gain; Similar to the step 2, considering the static output-feedback controller as
u= K, -y = KfCyx(t)
where C,=diag{0, 1} , then the closed-loop system with static output-feedback
controller can be established as
x(t)= A,,fx(t)+ B1 (q)w(t), z(t)= Cix(t)+D(q)w(t),
where Af=A(q)+B 2KsfC,, the other parameters are the same as in step 2;
Furthermore, solve the following LMI
QT7Q +,+( +o < 0
where
I 0 0 0 P 07 Q= A EB B2 E= P 0 0 , ;i=[K 0 -I| LC, 0 -F], 1 1 00 C, D L0L 4
-,=ANN'+A-EfE+diag{O,-I,O}, E,=[E E, 0], E(q)= aE,, 1=1
y. is a given positive scalar, K = Kg and the Kg is the state-feedback controller gain which solved in step 3; ) is a positive scalar that need to be solved; Li, F, P are matrices with appropriate dimensions that need to be solved, and P is a positive symmetric matrix, i =1, 2,3,4;
The output-feedback controller gain can be obtained as
K , = F-1- aL
And the required external yaw moment can be calculated by the following formula
u=AM =K Cx(t)
The external yaw moment can be allocated to four wheels via the moment distribution system and generated by the tire forces, thus the vehicle lateral stability can be ensured.
FIGURES 27 Aug 2020 2020102015
Fig. 1 Linear 2-DOF vehicle model
Measurement of vehicle parameters
Steering input Vehicle state- Ksf Vehicle static Ksof Controller ΔMz Control force output-feedback Actuators feedback model calculation distribution model
Fig.
2 Control system diagram
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Cited By (4)
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| CN113753080A (en) * | 2021-08-31 | 2021-12-07 | 的卢技术有限公司 | Self-adaptive parameter control method for transverse motion of automatic driving automobile |
| CN114253128A (en) * | 2021-12-22 | 2022-03-29 | 合肥工业大学 | Control parameter optimization method of automatic automobile steering system containing feedback time lag |
| CN117360486A (en) * | 2023-12-08 | 2024-01-09 | 福州大学 | Anti-interference direct yaw moment control method for multi-axis control chassis |
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| CN111824146A (en) * | 2020-06-19 | 2020-10-27 | 武汉理工大学 | Path following model prediction control method, system, device and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| DE19650691C2 (en) * | 1996-12-07 | 1998-10-29 | Deutsch Zentr Luft & Raumfahrt | Method for steering assistance for a driver of a road vehicle |
| US7835846B2 (en) * | 2007-02-28 | 2010-11-16 | Gm Global Technology Operations, Inc. | Nonlinear vehicle yaw/roll/sideslip command interpreter |
| US8725310B2 (en) * | 2009-04-29 | 2014-05-13 | Koninklijke Philips N.V. | Laser diode based self-mixing sensor for a vehicle electronic stability program |
| CN105172790B (en) * | 2015-10-30 | 2017-09-12 | 吉林大学 | A kind of vehicle yaw stability control method based on three-step approach |
| CN109367532B (en) * | 2018-08-20 | 2020-04-17 | 广东工业大学 | Automobile transverse stability control method based on speed dependence |
| CN109849898B (en) * | 2018-12-27 | 2020-06-26 | 合肥工业大学 | Vehicle yaw stability control method based on genetic algorithm hybrid optimization GPC |
-
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113753080A (en) * | 2021-08-31 | 2021-12-07 | 的卢技术有限公司 | Self-adaptive parameter control method for transverse motion of automatic driving automobile |
| CN113753080B (en) * | 2021-08-31 | 2023-09-26 | 的卢技术有限公司 | Self-adaptive parameter control method for transverse movement of automatic driving automobile |
| CN114253128A (en) * | 2021-12-22 | 2022-03-29 | 合肥工业大学 | Control parameter optimization method of automatic automobile steering system containing feedback time lag |
| CN114253128B (en) * | 2021-12-22 | 2023-06-30 | 合肥工业大学 | Control parameter optimization method of automatic steering system of automobile with feedback time lag |
| CN117360486A (en) * | 2023-12-08 | 2024-01-09 | 福州大学 | Anti-interference direct yaw moment control method for multi-axis control chassis |
| CN117369287A (en) * | 2023-12-08 | 2024-01-09 | 福州大学 | Lu Bangchao spiral sliding mode control method for multi-axle vehicle |
| CN117360486B (en) * | 2023-12-08 | 2024-03-08 | 福州大学 | Anti-interference direct yaw moment control method for multi-axis control chassis |
| CN117369287B (en) * | 2023-12-08 | 2024-03-12 | 福州大学 | Lu Bangchao spiral sliding mode control method for multi-axle vehicle |
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| CN111055837A (en) | 2020-04-24 |
| CN111055837B (en) | 2021-05-11 |
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