CN112051851B - Autonomous drift control method and system for electric four-wheel drive vehicle under limit working condition - Google Patents

Autonomous drift control method and system for electric four-wheel drive vehicle under limit working condition Download PDF

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CN112051851B
CN112051851B CN202010940050.3A CN202010940050A CN112051851B CN 112051851 B CN112051851 B CN 112051851B CN 202010940050 A CN202010940050 A CN 202010940050A CN 112051851 B CN112051851 B CN 112051851B
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tire
wheel
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CN112051851A (en
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侯晓慧
何承坤
张俊智
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Tsinghua University
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle

Abstract

The invention relates to an autonomous drift control method of an electric four-wheel drive vehicle under a limit working condition, which comprises the following steps: establishing a control reference model which comprises a double-track three-degree-of-freedom vehicle dynamics model of the four-wheel-drive electric vehicle and a tire model considering longitudinal and transverse coupling characteristics; decoupling through a maximum irrelevant primitive control channel, and converting a coefficient matrix of an overdrive system of the electric four-wheel drive vehicle into a square matrix related to virtual control input; solving a square matrix related to virtual control input by adopting an integral fuzzy sliding mode controller to obtain virtual control input; and converting the virtual control input into actual physical input by adopting a control allocation method based on constraint optimization, and transmitting the actual physical input to the actuator and the whole vehicle model. The invention ensures that the automatic driving vehicle has high-level driving capability of a professional driver, can expand the application scene and the dynamic control boundary of the electric four-wheel drive vehicle, and furthest exerts the active safety performance and the dynamic potential of the automatic driving vehicle.

Description

Autonomous drift control method and system for electric four-wheel drive vehicle under limit working condition
Technical Field
The invention relates to the technical field of intelligent driving automobile active safety control, in particular to an autonomous drift control method and system for an electric four-wheel drive vehicle under a limit working condition.
Background
With the rapid development of the automobile industry, the active safety of automobiles is challenged more and more severely, and various manufacturers at home and abroad also develop and apply various vehicle stability control technologies, including anti-lock braking systems, anti-skid driving systems and the like, but the technologies mainly aim at the conventional driving conditions and cannot cope with sudden scenes and extreme driving conditions, such as ice and snow road surfaces, high-speed emergency collision avoidance scenes and the like.
Extending the driving limits of vehicles requires the best possible use of the traction capabilities of the tires. Professional drivers often consciously control wheel locking or slipping during races to reduce the race or avoid obstacles, an operation known as "drift". The essence of the drift is that the vehicle is in a critical stable balance working condition under the over-steering state through accurate control, at the moment, the adhesive force of the front wheels is close to saturation, and the rear wheels reach the adhesive limit. In professional competitions, a rear-drive vehicle is generally selected to reduce the operation difficulty of a driver. The electric four-wheel drive vehicle is used as a new direction of electric vehicle technical development, a driving motor system of the electric four-wheel drive vehicle has the advantages of response speed and control precision, wheel torque can be flexibly distributed, the grip force is stronger, the limit range of the slip angle is larger, more possibilities are provided for the control boundary and effect of vehicle dynamics under the adhesion limit working condition, and meanwhile, higher requirements are provided for a control method.
Therefore, the drifting operation essence of a professional driver is researched, the dynamic characteristics and the control strategy of the electric four-wheel-drive vehicle under the adhesion limit are explored, the automatic driving vehicle has the high-level driving capability of a professional driver, the control potential of the vehicle under the condition of tire locking or slipping can be better explored, and the application scene and the dynamic control boundary of the automatic driving vehicle are expanded to the maximum extent.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide an autonomous drift control method and system for an electric four-wheel-drive vehicle under extreme conditions, which can expand the application scenarios and dynamic control boundaries of the electric four-wheel-drive vehicle, and maximize the active safety performance and the dynamic potential thereof.
In order to achieve the purpose, the invention adopts the following technical scheme: an autonomous drift control method of an electric four-wheel drive vehicle under a limit working condition comprises the following steps: 1) establishing a control reference model which comprises a double-track three-degree-of-freedom vehicle dynamics model of the four-wheel-drive electric vehicle and a tire model considering longitudinal and transverse coupling characteristics; 2) decoupling through a maximum irrelevant primitive control channel, and converting a coefficient matrix of an overdrive system of the electric four-wheel drive vehicle into a square matrix related to virtual control input to obtain a square affine system; 3) solving the affine system by adopting an integral fuzzy sliding mode controller to obtain virtual control input; 4) and converting the virtual control input into actual physical input by adopting a control allocation method based on constraint optimization, and transmitting the actual physical input to the actuator and the whole vehicle model.
Further, the control reference model calculates the longitudinal speed, the transverse speed and the yaw rate corresponding to the steady-state drift of the four-wheel drive electric vehicle in the current running environment, and sends the calculation result to the integral fuzzy sliding mode controller to serve as the target state quantity.
Further, the dual-rail three-degree-of-freedom vehicle dynamics model is expressed as:
Figure GDA0003221386280000021
wherein, VxFor longitudinal vehicle speed, VyIs the lateral vehicle speed, psi is the yaw angle of the vehicle,
Figure GDA0003221386280000022
is the yaw-rate of the vehicle,
Figure GDA0003221386280000023
is the yaw angular acceleration of the vehicle,
Figure GDA0003221386280000024
in the form of a longitudinal acceleration, the acceleration,
Figure GDA0003221386280000025
for lateral acceleration, FxjAnd FyjRespectively representing the tangential and lateral tire ground forces of the wheel, wherein j is 1,2,3,4 respectively representing the left front wheel, the right front wheel, the left rear wheel and the right rear wheel,
Figure GDA0003221386280000026
is a nonlinear term related to the state quantity.
Further, the tire model is represented as:
Figure GDA0003221386280000027
wherein μ is a road surface adhesion coefficient of the tire, FzFor the vertical load of the tire, D and E are the Pacejka tire model parameters, α is the tire slip angle, α iscrIs the critical slip angle of the tire, beta is the mass center slip angle of the vehicle,
Figure GDA0003221386280000028
the most usable for tiresHigh lateral force.
Further, in the step 2), the method specifically comprises the following steps:
overdrive system state equation:
Figure GDA0003221386280000029
wherein
Figure GDA00032213862800000210
Is a state variable of the system and is,
Figure GDA00032213862800000211
is the control input of the system, F (x) is the nonlinear term of the system, n and m are the dimensions of the system state variable and the control input respectively,
Figure GDA00032213862800000212
is a rational number; coefficient matrix
Figure GDA00032213862800000213
And rank (B) ═ n < m, the coefficient matrix B of the overdrive system is converted into a square matrix to obtain the coupling relationship between the input quantities, inputs with similar control effects are put into the same control channel, and finally the system is converted into:
Figure GDA00032213862800000214
the system represented in the formula is a square affine system with control decoupling; k ═ K1_1，K2_1，…，Kn_1]，Ki_1Is a coefficient matrix K and a neutron matrix KiI-1, 2, …, n.
Further, in the step 3), the input solving form of the integral fuzzy sliding mode controller is input v from a nominal control0And robust control input v1The two parts are as follows:
v＝v0+v1；
the virtual control input v is:
Figure GDA0003221386280000031
wherein, K ═ K1_1，K2_1，…，Kn_1]，Ki_1Is a coefficient matrix K and a neutron matrix K i1,2, …, n; f (x) is the nonlinear term of the system; l is a state feedback diagonal matrix; e is the system deviation; x is the number ofd＝[x1d，x2d，…，xnd]TThe control reference model is an ideal value of the system state quantity given by the control reference model; m (x) is a diagonal matrix; u. offIs output by the fuzzy system.
Further, said v0State feedback control rate set to make the system track the ideal system trajectory:
Figure GDA0003221386280000032
wherein xd＝[x1d，x2d，…，xnd]TThe ideal value of the system state quantity given by the reference model; e ═ xd-x, is the system state deviation.
Further, for robust control input v1By designing an integral sliding mode surface in the controller, the system state falls on the sliding mode surface from the initial moment, and an approach stage in a classical sliding mode is eliminated; while substituting v by a fuzzy system1The fuzzy system presents the saturation function characteristic with a nonlinear slope in the boundary layer, and the gs and the g are taken1Δ s is the input variable of the fuzzy system and the output is ufThe nonlinear control law is expressed as:
v1＝-M(x)uf
and setting diagonal elements of the diagonal matrix M (x) as positive numbers and larger than the upper limit value of corresponding system disturbance so as to ensure the convergence of the sliding mode.
Further, in the step 4), a control distribution based on constraint optimization is adopted to convert a virtual control input obtained by the integral fuzzy sliding mode controller into an actual physical input, and an objective function of the virtual control input is as follows:
Figure GDA0003221386280000033
Figure GDA0003221386280000041
in the formula, J1Penalty terms for the objective function taking into account the front wheel attachment utilization, J2For penalty term in the objective function taking into account the dynamics of the actuator, u ═ Fy1，Fy2，Fy3，Fy4，Fx1，Fx2，Fx3，Fx4]TFor system input, k1、k2Is a weight coefficient; w is a weighting matrix for determining the variation proportion of each tire force, and the diagonal term of W is set as the bandwidth omega of each actuator generating tire forceiThe reciprocal of (a), namely:
Figure GDA0003221386280000042
the constraint conditions ensure that the combination of physical inputs of all channels meets the requirement of virtual control input, and the tire force of the front wheel does not exceed the adhesion limit while the adhesion of the rear wheel is saturated.
An autonomous drift control system of an electric four-wheel drive vehicle under a limit working condition comprises a control reference model establishing module, a decoupling module, a solving module and an input module; the control reference model establishing module is used for establishing a control reference model, and comprises a double-track three-degree-of-freedom vehicle dynamic model of the four-wheel-drive electric vehicle and a tire model considering longitudinal and transverse coupling characteristics; the decoupling module is used for decoupling through a maximum irrelevant primitive control channel and converting a coefficient matrix of an overdrive system of the electric four-wheel drive vehicle into a square matrix related to virtual control input to obtain a square affine system; the solving module adopts an integral fuzzy sliding mode controller to solve the affine system to obtain virtual control input; and the input module converts the virtual control input into actual physical input by adopting a control distribution method based on constraint optimization and transmits the actual physical input to the actuator and the whole vehicle model.
Due to the adoption of the technical scheme, the invention has the following advantages: according to the invention, by researching the drifting operation essence of a professional driver, the autonomous drifting control is carried out aiming at the extreme working condition occurring in the driving process of the electric four-wheel drive vehicle. Firstly, an overdrive system is converted into a square affine system by a maximum irrelevant primitive control channel decoupling method, so that solving control input is facilitated; and an integral fuzzy sliding mode controller is used for calculating virtual control input, so that an approach stage in a classical sliding mode is eliminated, and meanwhile, the buffeting of a control system caused by external disturbance and parameter uncertainty can be restrained. And finally, converting virtual control input into actual physical input based on a multi-objective optimization control distribution method under complex constraint, wherein the influence of actuator dynamic response difference and height coupling characteristic on control effect is considered in a distribution strategy. The invention ensures that the automatic driving vehicle has high-level driving capability of a professional driver, can expand the application scene and the dynamic control boundary of the electric four-wheel drive vehicle, and furthest exerts the active safety performance and the dynamic potential of the automatic driving vehicle.
Drawings
FIG. 1 is a flow chart of the control method of the present invention.
FIG. 2 is a graph comparing an actual state of motion of a vehicle to an expected drift state.
Fig. 3 is a graph of the lateral force, longitudinal force, and resultant tire force versus vertical load for each wheel.
Fig. 4 is a motion trajectory of the center of mass of the vehicle.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.
As shown in FIG. 1, the invention provides an autonomous drift control method of an electric four-wheel drive vehicle under a limit working condition, which comprises the following steps:
1) and establishing a control reference model which comprises a double-track three-degree-of-freedom vehicle dynamics model of the four-wheel-drive electric vehicle and a tire model considering longitudinal and transverse coupling characteristics.
In this embodiment, the two-rail three-degree-of-freedom vehicle dynamics model is:
Figure GDA0003221386280000051
wherein, the coefficient matrix B is:
Figure GDA0003221386280000052
Figure GDA0003221386280000053
in the formula, VxFor longitudinal vehicle speed, VyIs the lateral vehicle speed, psi is the yaw angle of the vehicle,
Figure GDA0003221386280000061
is the yaw-rate of the vehicle,
Figure GDA0003221386280000062
is the yaw angular acceleration of the vehicle,
Figure GDA0003221386280000063
in the form of a longitudinal acceleration, the acceleration,
Figure GDA0003221386280000064
is a lateral accelerationM is the vehicle mass, IzYaw moment of inertia, delta, front wheel angle, L, for a vehicleaAnd LbRespectively, the linear distance between the center of mass and the front/rear axis, LwIs one-half track pitch, FxjAnd FyjRespectively, the tangential and lateral tire ground forces of the wheel, wherein j is 1,2,3,4 respectively, the front left wheel, the front right wheel, the rear left wheel and the rear right wheel, FrollAnd FairRolling resistance and air resistance of the vehicle, respectively:
Froll＝fmg
Figure GDA0003221386280000065
wherein f is a rolling resistance coefficient, g is a gravitational acceleration coefficient, ρ is an air density, and CdThe air resistance coefficient is, and A is the cross-sectional area of the vehicle.
The tire model is as follows:
Figure GDA0003221386280000066
wherein μ is a road surface adhesion coefficient of the tire, FzFor the vertical load of the tire, D and E are Pacejka tire model parameters, obtained by fitting actual tire test data, α is the tire slip anglecrIs the critical slip angle of the tire, beta is the mass center slip angle of the vehicle,
Figure GDA0003221386280000067
maximum lateral force available for the tire, wherein:
Figure GDA0003221386280000068
Figure GDA0003221386280000069
Figure GDA00032213862800000610
the slip angles of the front and rear wheels are as follows:
Figure GDA00032213862800000611
Figure GDA00032213862800000612
the vertical load of each wheel is:
Figure GDA0003221386280000071
wherein L is La+Lb，hgIs the height of the center of mass of the vehicle.
The establishment of the control reference model lays a foundation for subsequent control channel decoupling, and meanwhile, the longitudinal speed, the transverse speed and the yaw angular speed corresponding to steady-state drift of the four-wheel-drive electric vehicle in the current running environment can be calculated according to environmental information such as road attachment coefficients and curvature radius of an expected track, and the calculation result is sent to the integral fuzzy sliding mode controller to serve as the target state quantity.
2) According to the vehicle state equation established in the step 1), under the adhesion limit working condition, the electric four-wheel drive vehicle is an overdrive system with complex nonlinear characteristics, the coefficient matrix B of the overdrive system of the electric four-wheel drive vehicle obtained in the step 1) is converted into a square matrix related to virtual control input through maximum independent primitive control channel decoupling, a square affine system is obtained, and both classical and modern control methods can stabilize the converted system.
The overdrive system state equation is:
Figure GDA0003221386280000072
wherein
Figure GDA0003221386280000073
Is a state variable of the system and is,
Figure GDA0003221386280000074
is the control input of the system, and F (x) is the nonlinear term of the system. Coefficient matrix
Figure GDA0003221386280000075
And rank (b) ═ n < m. Wherein n and m are respectively the dimension of the system state variable and the control input;
Figure GDA0003221386280000076
are rational numbers.
Because the coefficient matrix B of the overdrive system is irreversible, the traditional controller design method cannot be directly used for solving the control input of the overdrive system, and therefore the method converts the coefficient matrix B into a square matrix by a maximum irrelevant primitive control channel decoupling method.
For coefficient matrix B, there must be a reversible transfer matrix
Figure GDA0003221386280000077
Such that:
Figure GDA0003221386280000078
wherein K is composed of n sub-matrixes K with the rank of 1iComposition, KiSatisfies the following conditions:
Figure GDA0003221386280000079
Figure GDA00032213862800000710
thus, by shifting the matrix H, the coefficients in the matrix K can be linearly relatedThe column vectors are grouped into the same sub-matrix KiIn (1). Can be regarded as KiFirst column vector K ofi_1I.e. the primitive vector of the entire matrix, then KiCan be further expressed as:
Figure GDA0003221386280000087
in the formula betai_kIs the kth column vector Ki_kFor primitive Ki_1The scaling factor of (c).
Figure GDA0003221386280000081
Let each proportionality coefficient betai_kForm transverse vector betai：
Figure GDA0003221386280000088
Then K isiCan be further expressed as:
Ki＝Ki_1βi
thus, K can be further expressed as:
Figure GDA0003221386280000082
the same transformation of the transfer matrix H is performed on the input vector u:
Figure GDA0003221386280000083
wherein
Figure GDA0003221386280000084
And KiAre the same in dimension.
Through the above variation process, the overdrive system state equation can be expressed as:
Figure GDA0003221386280000085
order:
K′＝[K1_1，K2_1，…，Kn_1]
Figure GDA0003221386280000086
v＝[v1 v2 ... vn]T
extracting coupling information in the coefficient matrix B to convert the input variable u 'of the transfer matrix'i_jAnd performing linear combination again, and putting the inputs with similar control effects into the same control channel. Different control channels viThe decoupling of the control channels of the control system is realized by mutual independence. Because v isiOne-to-one correspondence with actual physical inputs cannot be guaranteed, and therefore their names are named as virtual control inputs.
Through the derivation and transformation processes, the state equation of the system is converted into:
Figure GDA0003221386280000091
the system represented by the above formula is a square affine system for control decoupling, and a controller can be set by a modern control theory method to solve the virtual control input v.
3) Solving the affine system by adopting an integral fuzzy sliding mode controller to obtain virtual control input:
the method specifically comprises the following steps: based on the square affine system obtained by conversion in the step 2), an integral fuzzy sliding mode controller is designed, so that the problem of an approach stage in a classical sliding mode control method is avoided, and the robustness of the system is improved. Based on the control reference model in the step 1), the steady state drift of the integral fuzzy sliding mode controller under the preset road surface adhesion coefficient and track radius can be obtainedTarget state quantity
Figure GDA0003221386280000092
Simultaneously receives the actual state quantity fed back in real time by the actuator and the whole vehicle model
Figure GDA0003221386280000093
A closed loop control is formed. And finally, solving the virtual control input v of the square affine system through an integral fuzzy sliding mode controller.
The input v of the integral fuzzy sliding mode controller is input v by the nominal control in the solving form0And robust control input v1The two parts are as follows:
v＝v0+v1
the sliding mode surface s of the integral fuzzy sliding mode controller is as follows:
S＝s0+z
s0＝CTx
wherein s is0Is a linear combination of system state quantities x, CTAre linear combination coefficients. And z is an integral sliding mode term, which is indirectly obtained by the following equation:
Figure GDA0003221386280000094
z(0)＝-s0(x(0))
this gives:
s(0)＝s0(x(0))+z(0)＝0
the explanation is that if the design of the slip form surface is established, the system state can fall on the slip form surface from the initial moment, and the approach stage in the classical slip form is eliminated. When the system is positioned on the sliding mode surface, the motion equation of the system can be obtained by carrying out time derivation on the sliding mode surface:
Figure GDA0003221386280000101
selecting and deriving the lyapunov function yields:
Figure GDA0003221386280000102
Figure GDA0003221386280000103
in order to inhibit the buffeting of the system, a robust control item v is designed by adopting a boundary layer theory1The expression is as follows:
Figure GDA0003221386280000104
wherein ε > 0 is the thickness of the saturation region; m (x) > Δ d, Δ d is the observer disturbance estimation deviation.
However, when the vehicle is at a high instability boundary, external disturbances and parameter uncertainties may still induce control system buffeting. The invention adopts a fuzzy system to replace a saturation function, and the fuzzy system presents the characteristic of the saturation function with a nonlinear slope in a boundary layer. Taking gs and g1Δ s is the input variable of the fuzzy system and the output of the fuzzy system is ufThe nonlinear control law can be expressed as
v1＝-M(x)uf
Fuzzy system input variables gs and g1Δ s are divided into 3 fuzzy sets of negative (N), zero (Z) and positive (P), and output variables are divided into 7 fuzzy sets of Negative Big (NB), Negative Middle (NM), Negative Small (NS), Zero (ZE), Positive Small (PS), Positive Middle (PM) and Positive Big (PB); using a trigonometric function as a fuzzy membership function; applying a Mamdani fuzzy model, wherein the system fuzzy rule is shown in Table 1; and (5) performing output variable defuzzification by using a centroid method.
TABLE 1 systematic fuzzy rules
Figure GDA0003221386280000105
By selecting a linear combination coefficient matrix CTSo that C isTK' is positive and the diagonal system of the diagonal matrix m (x) is made positive and greater than the upper limit of the corresponding system perturbation, it can be guaranteed that:
Figure GDA0003221386280000106
unique condition for presence of equal sign:
Figure GDA0003221386280000111
i.e. the sliding mode equations of motion of the system. By the above derivation, the existence and convergence of the sliding mode is demonstrated.
v0State feedback control rate set to make the system track the ideal system trajectory:
Figure GDA0003221386280000112
wherein xd＝[x1d，x2d，…，xnd]TThe ideal value of the system state quantity given by the control reference model in the step 1); e ═ xd-x, is the system state deviation.
V is to be0Substituting the expression of (a) into the system state equation, we can obtain:
Figure GDA0003221386280000113
by designing the state feedback diagonal matrix L, the above equation is Hurwitz polynomial, and the system deviation e is gradually stable and must be converged to 0 within a finite time.
Through the design of the nominal controller and the robust controller, the virtual control input of the system is solved as follows:
Figure GDA0003221386280000114
4) in order to solve the actual physical input, actuators such as driving, braking and steering and the like and dynamic response difference and high coupling characteristics of the generated tire force are considered at the same time, and an online control distribution method based on multi-objective optimization under complex constraint is adopted.
After the virtual control input of the system is obtained through the integral fuzzy sliding mode controller, the virtual control input is converted into actual physical input through a control distribution method based on constraint optimization and is transmitted to an actuator and a whole vehicle model.
When the vehicle is in a high-speed instability boundary in a drifting state, the control performance and efficiency are greatly influenced by the dynamic response difference and the high coupling characteristic of actuating systems such as a driving motor system, a braking system and a steering system, so the transient characteristic difference of an actuator cannot be ignored in the control distribution method. Meanwhile, since the rear wheels reach the adhesion limit during the drifting of the vehicle, it is necessary to reduce the tire adhesion utilization rate of the front wheels as much as possible to increase the control margin of the front wheels and prevent vehicle instability caused by additional operations under sudden conditions.
The objective function for tire force optimized distribution is defined as:
Figure GDA0003221386280000115
Figure GDA0003221386280000121
in the formula, J1Penalty terms for the objective function taking into account the front wheel attachment utilization, J2For penalty term in the objective function taking into account the dynamics of the actuator, u ═ Fy1，Fy2，Fy3，Fy4，Fx1，Fx2，Fx3，Fx4]TFor system input, k1、k2Are weight coefficients. W is a weighting matrix for determining the proportion of change in each tire force. The present invention recognizes that the incremental tire force output should be proportional to the band in which each actuator generates the tire forceWide, thus setting the diagonal term of W to the bandwidth ω of each actuator's tire force generationiThe reciprocal of (a), namely:
Figure GDA0003221386280000122
ωican be determined by the dynamic characteristics of the actuator and the dynamic characteristics of the tire:
Figure GDA0003221386280000123
wherein tau isaTime constant after approximation of the actuator for driving/braking/steering, etc. as a first order system, τwThe specific value of the time constant in the longitudinal direction of the tire is set according to the nature and the variation of the tire force.
The invention provides an autonomous drift control system of an electric four-wheel-drive vehicle under the limit working condition, which comprises a control reference model establishing module, a decoupling module, a solving module and an input module, wherein the control reference model establishing module is used for establishing a control reference model;
the control reference model establishing module is used for establishing a control reference model, and comprises a double-track three-degree-of-freedom vehicle dynamic model of the four-wheel-drive electric vehicle and a tire model considering longitudinal and transverse coupling characteristics;
the decoupling module is used for decoupling through a maximum irrelevant primitive control channel, converting a coefficient matrix of an overdrive system of the electric four-wheel drive vehicle into a square matrix related to virtual control input, and obtaining a square affine system;
the solving module adopts an integral fuzzy sliding mode controller to solve the affine system to obtain virtual control input;
and the input module converts the virtual control input into actual physical input by adopting a control distribution method based on constraint optimization and transmits the actual physical input to the actuator and the whole vehicle model.
In summary, as shown in fig. 2 to fig. 4, the effect of the autonomous drift control method for electric four-wheel-drive vehicle under the extreme condition provided by the present invention after the simulation test is performed is schematically shown. After the autonomous drift control method of the electric four-wheel drive vehicle under the limit working condition is used for controlling, as shown in fig. 2, the motion state of the vehicle can be stabilized in a steady drift state under the current road condition within a certain time; as shown in fig. 3, the longitudinal and lateral tire forces of each wheel can be dynamically adjusted according to the virtual control input command, while ensuring that both wheels of the rear axle are always in the adhesion limit state (the road adhesion coefficient is set to be 0.6), and both wheels of the front axle still have a certain control space; as shown in fig. 4, the vehicle can start to make a steady-state drifting circular motion within a certain time, and the circular radius is the curvature radius of the set desired track (the set road curvature radius is 13 meters).
While the invention has been described with reference to a preferred embodiment and an example thereof, it will be understood by those skilled in the art that various features of the above-described embodiments of the invention may be combined as appropriate to form modifications, and that various other changes and modifications may be made, equivalents may be made, and applications may be made to the above-described embodiments by those skilled in the art without departing from the scope of the invention.

Claims (10)

1. An autonomous drift control method of an electric four-wheel drive vehicle under a limit working condition is characterized by comprising the following steps:
1) establishing a control reference model which comprises a double-track three-degree-of-freedom vehicle dynamics model of the four-wheel-drive electric vehicle and a tire model considering longitudinal and transverse coupling characteristics;
2) decoupling through a maximum irrelevant primitive control channel, and converting a coefficient matrix of an overdrive system of the electric four-wheel drive vehicle into a square matrix related to virtual control input to obtain a square affine system;
3) solving the affine system by adopting an integral fuzzy sliding mode controller to obtain virtual control input;
4) and converting the virtual control input into actual physical input by adopting a control allocation method based on constraint optimization, and transmitting the actual physical input to the actuator and the whole vehicle model.
2. The control method according to claim 1, further comprising: and the control reference model calculates the longitudinal speed, the transverse speed and the yaw angular speed corresponding to the steady-state drift of the four-wheel drive electric vehicle in the current running environment, and sends the calculation result to the integral fuzzy sliding-mode controller as the target state quantity.
3. The control method of claim 1, wherein the two-rail three-degree-of-freedom vehicle dynamics model is expressed as:
Figure FDA0003221386270000011
wherein, VxFor longitudinal vehicle speed, VyIs the lateral vehicle speed, psi is the yaw angle of the vehicle,
Figure FDA0003221386270000012
is the yaw-rate of the vehicle,
Figure FDA0003221386270000013
is the yaw angular acceleration of the vehicle,
Figure FDA0003221386270000014
in the form of a longitudinal acceleration, the acceleration,
Figure FDA0003221386270000015
for lateral acceleration, FxjAnd FyjRespectively representing the tangential and lateral tire ground forces of the wheel, wherein j is 1,2,3,4 respectively representing the left front wheel, the right front wheel, the left rear wheel and the right rear wheel,
Figure FDA0003221386270000016
and B is a coefficient matrix.
4. The control method according to claim 1, wherein the tire model is represented as:
Figure FDA0003221386270000017
wherein μ is a road surface adhesion coefficient of the tire, FzFor the vertical load of the tire, D and E are the Pacejka tire model parameters, α is the tire slip angle, α iscrIs the critical slip angle of the tire, beta is the mass center slip angle of the vehicle,
Figure FDA0003221386270000018
the maximum lateral force available for the tire.
5. The control method according to claim 1, wherein in the step 2), specifically:
overdrive system state equation:
Figure FDA0003221386270000021
wherein
Figure FDA0003221386270000022
Is a state variable of the system and is,
Figure FDA0003221386270000023
is the control input of the system, F (x) is the nonlinear term of the system, n and m are the dimensions of the system state variable and the control input respectively,
Figure FDA0003221386270000024
is a rational number; coefficient matrix
Figure FDA0003221386270000025
And rank (b) ═ n<m, converting the coefficient matrix B of the overdrive system into a square matrix to obtain the coupling relation between the input quantities, and putting the inputs with similar control effects into the same matrixIn the control channel, the system is finally converted into:
Figure FDA0003221386270000026
the system represented in the formula is a square affine system with control decoupling; k ═ K1_1,K2_1,…,Kn_1]，Ki_1Is a coefficient matrix K and a neutron matrix KiI-1, 2, …, n.
6. The control method according to claim 1, wherein in step 3), the input solution form of the integral fuzzy sliding mode controller is derived from a nominal control input v0And robust control input v1The two parts are as follows:
v＝v0+v1；
the virtual control input v is:
Figure FDA0003221386270000027
wherein, K ═ K1_1,K2_1,…,Kn_1]，Ki_1Is a coefficient matrix K and a neutron matrix Ki1,2, …, n; f (x) is the nonlinear term of the system; l is a state feedback diagonal matrix; e is the system deviation; x is the number ofd＝[x1d,x2d,…,xnd]TThe control reference model is an ideal value of the system state quantity given by the control reference model; m (x) is a diagonal matrix; u. offIs output by the fuzzy system.
7. The control method of claim 6, wherein v is0State feedback control rate set to make the system track the ideal system trajectory:
Figure FDA0003221386270000028
wherein xd＝[x1d,x2d,…,xnd]TThe ideal value of the system state quantity given by the reference model; e ═ xd-x, is the system state deviation.
8. Control method according to claim 6, characterized in that v is input for robust control1By designing an integral sliding mode surface in the controller, the system state falls on the sliding mode surface from the initial moment, and an approach stage in a classical sliding mode is eliminated; while substituting v by a fuzzy system1The fuzzy system presents the saturation function characteristic with a nonlinear slope in the boundary layer, and the gs and the g are taken1Δ s is the input variable of the fuzzy system and the output is ufThe nonlinear control law is expressed as:
v1＝-M(x)uf
and setting diagonal elements of the diagonal matrix M (x) as positive numbers and larger than the upper limit value of corresponding system disturbance so as to ensure the convergence of the sliding mode.
9. The control method according to claim 1, wherein in step 4), the virtual control input obtained by the integral fuzzy sliding-mode controller is converted into the actual physical input by using constraint optimization-based control distribution, and the objective function is as follows:
Figure FDA0003221386270000031
Figure FDA0003221386270000032
in the formula, J1Penalty terms for the objective function taking into account the front wheel attachment utilization, J2For penalty term in the objective function taking into account the dynamics of the actuator, u ═ Fy1,Fy2,Fy3,Fy4,Fx1,Fx2,Fx3,Fx4]TFor system input, FxjAnd FyjRepresenting wheel tangential and lateral tyre ground forces, respectively, FzjRepresents the vertical load of the tire, wherein j is 1,2,3,4 represents the left front wheel, the right front wheel, the left rear wheel and the right rear wheel, respectively; beta is a1,β2,…βnThe transverse vector is obtained after decoupling of a channel B of the coefficient matrix; h is a transition matrix; k is a radical of1、k2Is a weight coefficient; w is a weighting matrix for determining the variation proportion of each tire force, and the diagonal term of W is set as the bandwidth omega of each actuator generating tire forceiThe reciprocal of (a), namely:
Figure FDA0003221386270000033
the constraint conditions ensure that the combination of physical inputs of all channels meets the requirement of virtual control input, and the tire force of the front wheel does not exceed the adhesion limit while the adhesion of the rear wheel is saturated.
10. An autonomous drift control system for an electric four-wheel drive vehicle under extreme operating conditions, comprising: the device comprises a control reference model establishing module, a decoupling module, a solving module and an input module;
the control reference model establishing module is used for establishing a control reference model, and comprises a double-track three-degree-of-freedom vehicle dynamic model of the four-wheel-drive electric vehicle and a tire model considering longitudinal and transverse coupling characteristics;
the decoupling module is used for decoupling through a maximum irrelevant primitive control channel and converting a coefficient matrix of an overdrive system of the electric four-wheel drive vehicle into a square matrix related to virtual control input to obtain a square affine system;
the solving module adopts an integral fuzzy sliding mode controller to solve the affine system to obtain virtual control input;
and the input module converts the virtual control input into actual physical input by adopting a control distribution method based on constraint optimization and transmits the actual physical input to the actuator and the whole vehicle model.
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