CN111098849B - New energy automobile stability control method and system - Google Patents

New energy automobile stability control method and system Download PDF

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CN111098849B
CN111098849B CN201811271410.4A CN201811271410A CN111098849B CN 111098849 B CN111098849 B CN 111098849B CN 201811271410 A CN201811271410 A CN 201811271410A CN 111098849 B CN111098849 B CN 111098849B
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value
centroid
automobile
yaw
ideal value
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CN111098849A (en
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廖少毅
王溥希
徐瑀婧
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City University of Hong Kong CityU
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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, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/02—Control of vehicle driving stability
    • B60W30/045—Improving turning performance
    • 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
    • B60W40/12—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 related to parameters of the vehicle itself, e.g. tyre models
    • B60W40/13—Load or weight
    • B60W2040/1315—Location of the centre of gravity
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/30—Wheel torque
    • 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
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/18—Steering angle
    • 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
    • B60W40/02—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 related to ambient conditions
    • B60W40/06—Road conditions
    • B60W40/064—Degree of grip

Abstract

The invention provides a method and a system for controlling stability of a new energy automobile, wherein the method comprises the following steps: acquiring the rotation angle and the longitudinal speed of a front wheel of an automobile; inputting the rotation angle and the longitudinal speed of the front wheel into a linear two-degree-of-freedom model of the automobile to generate an ideal value of the yaw angular speed and an ideal value of the centroid slip angle; according to the RBF neural network algorithm, the ideal value of the yaw angular velocity and the actual value of the yaw angular velocity, the ideal value of the centroid sideslip angle and the actual value of the centroid sideslip angle, the uncertain interference items of the automobile are bounded; generating the total required yaw moment of the automobile according to the ideal value of the yaw angular velocity, the actual value of the yaw angular velocity, the ideal value of the centroid side deviation angle, the actual value of the centroid side deviation angle and a bounded uncertain interference item; the total required yaw moment is divided into individual wheels, and the division result is output to a moment adjuster. The invention can make the uncertain interference items of the automobile system be bounded, effectively improves the anti-interference capability of the automobile system and ensures the control and driving stability of the automobile.

Description

New energy automobile stability control method and system
Technical Field
The invention relates to the field of automobiles, in particular to a new energy automobile technology, and specifically relates to a new energy automobile stability control method and system.
Background
In recent years, along with social progress, people's awareness of environmental protection is gradually strengthened, and efficient, safe and environment-friendly new energy automobiles have attracted high attention of various automobile manufacturers.
The existing automobile stability control system does not consider the problems of external interference factors, trembling matrix and the like to influence the stability of the controller, so that the automobile is under-steered or over-steered, the actual running path of the automobile is difficult to keep consistent with the expected path, and the operation stability of the automobile cannot be ensured. The chinese patent application publication No. 107054453a discloses a steering stability control system for an automobile and a control method thereof, the system includes a rack and pinion steering gear, a power assist motor, a worm gear, a steering motor, a double planetary gear mechanism, an electronic control unit ECU, a steering wheel torque sensor, a steering wheel rotation angle sensor, a vehicle speed sensor, a yaw rate sensor, a center of mass yaw angle sensor, a lateral acceleration sensor, and a front wheel rotation angle sensor. The patent does not consider the influence factors of the interference of the automobile and external factors, and is easy to generate larger errors for a complex system and not easy to be applied in practice.
Therefore, how to provide a more timely and accurate new energy vehicle stability control method and system is a problem to be solved urgently.
Disclosure of Invention
Aiming at the problems in the prior art, the control system provided by the invention can quickly apply driving force or braking force to timely and accurately control the yaw angular velocity and the mass center side slip angle of the automobile, so that understeer or oversteer of the automobile is avoided, the actual running path of the automobile is consistent with the expected path, and the operation stability of the automobile is improved.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, the method for controlling the stability of the new energy automobile comprises the following steps:
acquiring the rotation angle and the longitudinal speed of a front wheel of an automobile;
inputting the corner and the longitudinal speed of the front wheel into a linear two-degree-of-freedom model of the automobile to generate an ideal value of the yaw angular speed and an ideal value of the centroid slip angle;
according to an RBF neural network algorithm, the ideal value of the yaw angular velocity, the actual value of the yaw angular velocity, the ideal value of the centroid sideslip angle and the actual value of the centroid sideslip angle, making uncertain interference items of the automobile bounded;
generating a total required yaw moment of the automobile according to the ideal value of the yaw angular velocity, the actual value of the yaw angular velocity, the ideal value of the centroid side slip angle, the actual value of the centroid side slip angle and the bounded uncertain disturbance items;
the total required yaw moment is divided into individual wheels, and the division result is output to a moment adjuster.
Further, the ideal value of the centroid slip angle is expressed as follows:
Figure BDA0001846076910000021
the expression of the ideal value of the yaw rate is as follows:
Figure BDA0001846076910000022
wherein, betadIs an ideal value of the centroid slip angle, gammadK is an ideal value of the yaw rate, K is a stability coefficient, and K is taken to be 2 multiplied by 10-3，βmaxIs the maximum value of the mass center slip angle, delta is the front wheel rotation angle, m is the automobile mass, vxThe longitudinal speed of the automobile and the mu is the road adhesion coefficient; g is the acceleration of gravity, CrIs the form factor of the lateral force curve of the left wheel and the right wheel.
Further, before the uncertain disturbance items of the automobile are bounded according to the RBF neural network, the method further comprises the following steps: and obtaining the actual value of the yaw angular velocity and the actual value of the centroid slip angle of the automobile through an Inertial Measurement Unit (IMU).
Further, the method for limiting the uncertain disturbance terms of the automobile according to the RBF neural network algorithm, the ideal value of the yaw rate, the actual value of the yaw rate, the ideal value of the centroid side deviation angle and the actual value of the centroid side deviation angle comprises the following steps:
calculating a difference between the ideal value of the yaw rate and the actual value of the yaw rate, and a difference between the ideal value of the centroid slip angle and the actual value of the centroid slip angle;
inputting the difference value of the ideal value of the yaw angular velocity and the actual value of the yaw angular velocity and the difference value of the ideal value of the centroid side slip angle and the actual value of the centroid side slip angle into the RBF neural network algorithm to generate uncertain disturbance term parameters;
fitting the uncertain disturbance item parameters by using a minimum parameter method;
optimizing the control rate of the uncertain disturbance item parameters after fitting by using a self-adaptive terminal sliding mode control method;
and utilizing a Lyapunov function (Lyapunov stability function) to make the uncertain disturbance terms bounded according to the optimized control rate.
Further, the uncertain interference term includes: parameter perturbation, modeling error and jitter matrix.
Further, generating a total demanded yaw moment of the automobile according to the ideal value of the yaw rate, the actual value of the yaw rate, the ideal value of the centroid side slip angle, the actual value of the centroid side slip angle and the bounded uncertain disturbance term, comprising:
and inputting the difference value between the ideal value of the yaw angular velocity and the actual value of the yaw angular velocity and the difference value between the ideal value of the centroid side slip angle and the actual value of the centroid side slip angle into a terminal sliding mode control formula to generate the total required yaw moment of the automobile.
In a second aspect, the invention provides a new energy vehicle stability control system, which includes:
the brake unit is used for acquiring the rotation angle and the longitudinal speed of the front wheel of the automobile;
the two-degree-of-freedom unit is used for inputting the corner and the longitudinal speed of the front wheel into a linear two-degree-of-freedom model of the automobile and generating an ideal value of the yaw angular speed and an ideal value of the centroid slip angle;
the neural network unit is used for enabling uncertain interference items of the automobile to be bounded according to an RBF neural network algorithm, the ideal value of the yaw velocity, the actual value of the yaw velocity, the ideal value of the centroid sideslip angle and the actual value of the centroid sideslip angle;
the yaw moment unit is used for generating the total required yaw moment of the automobile according to the ideal value of the yaw angular velocity, the actual value of the yaw angular velocity, the ideal value of the centroid side deviation angle, the actual value of the centroid side deviation angle and the bounded uncertain disturbance term;
and a dividing unit for dividing the total required yaw moment into individual wheels and outputting the division result to the moment adjuster.
In a third aspect, a computer device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the steps of the new energy vehicle stability control method are implemented.
In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the new energy vehicle stability control method.
According to the stability control method, the system, the computer equipment and the computer readable storage medium of the new energy automobile, the ideal value of the yaw angular velocity and the ideal value of the centroid side deviation angular velocity can be obtained through the front wheel turning angle and the longitudinal velocity based on the simplified linear two-degree-of-freedom vehicle model, the RBF neural network self-adjustment control strategy is introduced, the difference value between the actual values of the yaw angular velocity and the centroid side deviation angle and the ideal value is used as the input of the RBF neural network, the self-adjustment approaching and the self-identification are carried out on the unknown interference item in the automobile system, the interference resistance of the automobile system is effectively improved, and the purposes of weakening parameter perturbation, modeling error and array shaking are achieved. And finally, carrying out torque distribution among wheels on the total required yaw moment by a quadratic programming method, so that the actual running path of the automobile is consistent with the expected path, and further improving the running stability of the automobile.
In order to make the aforementioned and other objects, features and advantages of the invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a stability control method of a new energy vehicle in an embodiment of the invention.
Fig. 2 shows the specific steps of step 300 in fig. 1.
Fig. 3 is a schematic structural diagram of an RBF neural network according to an embodiment of the present invention.
Fig. 4 is a schematic structural diagram of a stability control system of a new energy vehicle in an embodiment of the invention.
Fig. 5 is a schematic flow chart of a specific application example of the method for controlling stability of a new energy vehicle according to the present invention.
Fig. 6 is a schematic structural diagram of an electronic device in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
It should be noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of this application and the above-described drawings, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
The embodiment of the invention provides a specific implementation manner of a stability control method of a new energy automobile, and referring to fig. 1, the stability control method of the new energy automobile specifically includes the following steps:
step 100: and acquiring the rotation angle and the longitudinal speed of the front wheel of the automobile.
In step 100, the front wheel steering angle and the longitudinal speed of the automobile are obtained by arranging an ABS drive four-wheel brake on the automobile.
Step 200: and inputting the corner and the longitudinal speed of the front wheel into a linear two-degree-of-freedom model of the automobile to generate an ideal value of the yaw angular speed and an ideal value of the centroid slip angle.
In step 200, specifically: and obtaining an ideal value of the yaw angular velocity and an ideal value of the centroid sideslip angular velocity based on the linear two-degree-of-freedom model through the front wheel rotation angle and the longitudinal velocity. In one embodiment, the ideal value of the centroid slip angle is expressed as follows:
Figure BDA0001846076910000051
the expression of the ideal value of the yaw rate is as follows:
Figure BDA0001846076910000061
wherein, betadIs an ideal value of the centroid slip angle, gammadK is an ideal value of the yaw rate, K is a stability coefficient, and K is taken to be 2 multiplied by 10-3，βmaxIs the maximum value of the mass center slip angle, delta is the front wheel rotation angle, m is the automobile mass, vxThe longitudinal speed of the automobile and the mu is the road adhesion coefficient; g is the acceleration of gravity, CrIs the form factor of the lateral force curve of the left wheel and the right wheel.
Step 300: and according to the RBF neural network algorithm, the ideal value of the yaw angular velocity, the actual value of the yaw angular velocity, the ideal value of the centroid sideslip angle and the actual value of the centroid sideslip angle, making uncertain interference items of the automobile bounded.
In step 300, it can be understood that, in practical application, the entire vehicle control system is always affected by uncertain factors such as a modeling difference value and external environment interference, and these uncertain factors may seriously interfere with the stability performance of the control system, so that the controller may not achieve an ideal control effect. Specifically, the RBF neural network control algorithm is firstly used for carrying out self-adjustment approximation on the uncertain disturbance items, then a proper control law is designed for the control system, so that the terminal sliding mode quickly reaches the sliding mode surface within limited time and is kept to be zero, at the moment, the terminal sliding mode variable enters the terminal sliding mode motion state, the error state enters the sliding mode, and finally the system state convergence is realized. And the weight adjustment in the neural network is replaced by the automobile parameter estimation, model information is not needed, self-adjustment control based on single parameter estimation can be realized, so that a self-adjustment algorithm is simplified, the method is suitable for being applied to a complex control system, the buffeting problem caused by terminal sliding mode control is effectively solved while the system stability is ensured, and the driving stability of the automobile is improved.
In one specific example: the uncertain interference items include: parameter perturbation, modeling error, jitter matrix, and the like.
Step 400: and generating the total required yaw moment of the automobile according to the ideal value of the yaw angular velocity, the actual value of the yaw angular velocity, the ideal value of the centroid side deviation angle, the actual value of the centroid side deviation angle and the bounded uncertain disturbance term.
In step 400, a total required yaw moment under the premise of stable driving of the automobile is determined according to a difference value between an actual value of the yaw velocity and an ideal value and a difference value between an actual value of the centroid yaw angle and an ideal value, and specifically: inputting the difference value between the ideal value of the yaw angular velocity and the actual value of the yaw angular velocity and the difference value between the ideal value of the centroid side slip angle and the actual value of the centroid side slip angle into the following terminal sliding mode control formula:
Figure BDA0001846076910000071
and defines:
Figure BDA0001846076910000072
calculating the total required yaw moment of the automobile as follows:
Figure BDA0001846076910000073
in the formula (I); s is an offset factor, beta is a centroid slip angle, gamma is a yaw rate, and Fxfr、Fxfl、Fyfr、FyflFront right and front left tire force components in the longitudinal and lateral directions, respectively, d is the left and right wheel track, η is a design parameter, and η>0, a and b are formula parameters.
Step 500: the total required yaw moment is divided into individual wheels, and the division result is output to a moment adjuster.
In step 500, dividing the total required yaw moment by the torque between the wheels based on a quadratic programming method, specifically: the total required yaw moment can be expressed as:
vk＝Buk
in the formula:
Figure BDA0001846076910000074
uk＝(Fxfl Fxfr Fxrl Fxrr)T。
according to a quadratic programming optimization distribution control formula: minJ ═ Fxfl 2+Fxfr 2+Fxrl 2+Fxrr 2
Obtaining the dividing result:
Figure BDA0001846076910000075
in the formula: i represents i ═ fl, fr, rl, rr respectively for the front left, front right, rear left, rear right and tire directions, and FziMu is the road surface adhesion coefficient for each wheel vertical load.
In one specific example: after step 500, a step of controlling the moment regulator to distribute the yaw moment among the respective wheels may be further performed according to the division result.
From the above description, the stability control method for the new energy automobile provided by the invention can obtain the ideal value of the yaw angular velocity and the ideal value of the centroid side-slip angular velocity through the front wheel rotation angle and the longitudinal velocity based on the simplified linear two-degree-of-freedom vehicle model, and can effectively improve the anti-interference capability of the automobile system by introducing the RBF neural network self-adjustment control strategy and taking the difference value between the actual values and the ideal values of the yaw angular velocity and the centroid side-slip angle as the input of the RBF neural network to perform self-adjustment approximation and self-identification on unknown interference items in the automobile system, so that the interference capability of the automobile system is effectively improved, and the purposes of weakening parameter perturbation, modeling errors and the jittering phenomenon are achieved. And finally, carrying out torque distribution among wheels on the total required yaw moment by a quadratic programming method, so that the actual running path of the automobile is consistent with the expected path, and further improving the running stability of the automobile.
In an embodiment, the invention further provides an embodiment of step 300 in the method for controlling stability of the new energy vehicle, and referring to fig. 2, the step 300 specifically includes the following steps:
step 301: calculating a difference between the ideal value of the yaw rate and the actual value of the yaw rate, and a difference between the ideal value of the centroid slip angle and the actual value of the centroid slip angle.
Step 302: and inputting the difference value of the ideal value of the yaw angular velocity and the actual value of the yaw angular velocity and the difference value of the ideal value of the centroid side slip angle and the actual value of the centroid side slip angle into the RBF neural network algorithm to generate uncertain disturbance term parameters.
In a specific embodiment: based on the RBF neural network minimum parameter learning method, the RBF neural network algorithm is as follows:
Figure BDA0001846076910000081
in the formula, xm＝[e1,e2]TE.g. R2 x 1 is an input signal; h ═ h1,h2,h3,h4,h5]TIs the output of a gaussian function; c. Cj＝[c1j,c2j]T，c1j＝[c11,…,c15]E R1 x 5 is the 1j base function center, c2j＝[c21,…,c25]e.R 1 x 5 is the 2j th base function center; w ═ Wi1,…,wi5]T(i is 1,2), namely W is equal to R5 multiplied by 2 as the weight of the ideal neural network, epsilon is equal to R2 multiplied by 1 as the approximation error of the neural network, and epsilon is less than or equal to | epsilon |NEstimating unknown interference term K by using RBF neural networkfAnd outputting the following:
Figure BDA0001846076910000082
step 303: and fitting the uncertain disturbance term parameters by using a minimum parameter method.
Unknown interference item K based on neural network minimum parameter learning methodfPerforming identification to obtain the optimal weight value, marking as M, and marking as M | | | W | | counting2Wherein M is a positive real number,
Figure BDA0001846076910000083
for the estimation of M,
Figure BDA0001846076910000084
the control rate based on the design is improved, and the control strategy after the design optimization is as follows:
Figure BDA0001846076910000091
wherein the uncertainty term KfEta ≧ epsilon is an estimate of the RBF neural networkNAnd | mu > 0. sgn(s) is a switching function defined as follows:
Figure BDA0001846076910000092
step 304: and optimizing the control rate of the uncertain disturbance item parameters after fitting by using a self-adaptive terminal sliding mode control method.
Step 305: and utilizing a Lyapunov function to make the uncertain interference items bounded according to the optimized control rate.
The method specifically comprises the following steps: using the following formula
Figure BDA0001846076910000093
And defines the Lyapunov function:
Figure BDA0001846076910000094
the first derivative of V(s) with respect to time is:
Figure BDA0001846076910000095
substituting the above into the first derivative of V(s) with respect to time:
Figure BDA0001846076910000096
wherein s isTsMhTh+1＝sTs||W||2hTh+1＝sTs||W||2||h||2+1＝sTs||WTh||2+1≥2sTWTh, γ > 0, i.e.:
Figure BDA0001846076910000097
order to
Figure BDA0001846076910000098
Where ρ > 0, p and q are odd numbers, and 1<p/q<2, whereby Ci1> 0(i ═ 1,2), and the simplification can be:
Figure BDA0001846076910000099
namely:
Figure BDA00018460769100000910
for a single parameter
Figure BDA0001846076910000101
Estimating, designing a self-regulation law as follows:
Figure BDA0001846076910000102
substituting the designed single parameter estimate into the equation:
Figure BDA0001846076910000103
in the formula, eta is ≧ epsilonN|，μ＞0，
Figure BDA0001846076910000104
Ck＝[C11,C21]T，Ci1＞0(i＝1,2)。
Wherein epsiloni-ηsgn(si)≤0(i＝1,2)。
This gives:
Figure BDA0001846076910000105
Figure BDA0001846076910000106
therefore, first, the RBF neural network control algorithm is applied to the uncertain disturbance term KfSelf-adjustment approximation is carried out, a proper control law is designed for a control system, the terminal sliding mode quickly reaches the sliding mode surface within a limited time and is kept to be zero, namely V is 0, at the moment, a terminal sliding mode variable s enters a terminal sliding mode motion state, an error state e,
Figure BDA0001846076910000107
And entering a sliding mode, and finally realizing the convergence of the system state.
In order to further explain the invention, the invention further provides a specific application example of the new energy automobile stability control method, and the specific application example of the new energy automobile stability control method specifically comprises the following contents:
referring to fig. 5, a specific embodiment of the method for controlling stability of the new energy vehicle includes:
s0: and acquiring the rotation angle and the longitudinal speed of the front wheel of the automobile.
In a more specific example: the front wheel rotation angle and the longitudinal speed of the automobile can be obtained by arranging the ABS driving four-wheel brake on the automobile.
S1: and inputting the corner and the longitudinal speed of the front wheel into a linear two-degree-of-freedom model of the automobile to generate ideal values of the yaw angular speed and the centroid slip angle.
The ideal value of the centroid slip angle is expressed as follows:
Figure BDA0001846076910000108
the expression of the ideal value of the yaw rate is as follows:
Figure BDA0001846076910000111
wherein, betadIs the ideal value of the centroid slip angle, gamma d is the ideal value of the yaw angular velocity, K is the stability coefficient, and K is 2 multiplied by 10-3，βmaxIs the maximum value of the mass center slip angle, delta is the front wheel rotation angle, m is the automobile mass, vxThe longitudinal speed of the automobile and the mu is the road adhesion coefficient; g is the acceleration of gravity, CrIs the form factor of the lateral force curve of the left wheel and the right wheel.
In a more specific example: an additional yaw moment T can be added to the linear two-degree-of-freedom modelzRepresented by the following formula:
Figure BDA0001846076910000112
from the tire model, the vertical load F of each tirezi(i ═ 1,2, 3, 4) and the slip angle α of the tireiThe vehicle equation of state may be expressed as:
Figure BDA0001846076910000113
further simplifying from this, the above equation is converted to the equation of state as follows:
Figure BDA0001846076910000114
in the above formula:
Figure BDA0001846076910000115
u＝[Tz]further, the 2 signals of the yaw rate and the centroid slip angle can be measured by adding an inertial measurement unit IMU. The output equation of the system thus obtained is as follows:
y＝Cx+Dδ
in the above formula:
Figure BDA0001846076910000121
wherein alpha isyFor lateral acceleration, CαfAnd CαrRespectively, tire longitudinal and lateral stiffness.
S2: and obtaining the actual value of the yaw angular velocity and the actual value of the centroid slip angle of the automobile through an Inertial Measurement Unit (IMU).
S3: calculating a difference between the ideal value of the yaw rate and the actual value of the yaw rate, and a difference between the ideal value of the centroid slip angle and the actual value of the centroid slip angle.
S4: and inputting the difference value of the ideal value of the yaw angular velocity and the actual value of the yaw angular velocity and the difference value of the ideal value of the centroid side slip angle and the actual value of the centroid side slip angle into the RBF neural network algorithm to generate uncertain disturbance term parameters.
In practical application, the whole vehicle control system is always influenced by uncertain factors such as a modeling difference value and external environment interference, and the influence factors can seriously interfere with the stability performance of the control system, so that the controller cannot achieve an ideal control effect. Therefore, aiming at the problems of parameter perturbation and modeling error of a linear two-degree-of-freedom vehicle dynamics system, the state equation is written into the following form:
Figure BDA0001846076910000122
let f equal to Δ Ax + Δ B1δ+ΔB2u + xi, and satisfies | | | f | | | less than or equal to L. This equation f represents the nonlinear uncertainty of the system, and the equation of state is rewritten as follows:
Figure BDA0001846076910000123
the invention adopts RBF neural network to correct uncertainty KfPerforming a self-adjusting approximation, as shown in FIG. 3, with 2 inputs e1And e 25 hidden nodes hj(j ═ 1,2 …,5), 2 outputs K1And K2The RBF neural network of (1).
Based on the RBF neural network minimum parameter learning method, the RBF neural network algorithm is as follows:
Figure BDA0001846076910000124
in the formula, xm＝[e1,e2]T∈R2×1Is an input signal; h ═ h1,h2,h3,h4,h5]TIs the output of a gaussian function; c. Cj＝[c1j,c2j]T，c1j＝[c11,…,c15]∈R1×5Is the 1 j-th base function center, c2j＝[c21,…,c25]∈R1×5Is the 2j base function center; w ═ Wi1,…,wi5]T(i 1,2), i.e. W ∈ R5×2Is the weight of the ideal neural network, epsilon belongs to R2 ×1For the approximation error of the neural network, | Epsilon | < Epsilon |)NEstimating unknown interference term K by using RBF neural networkfAnd outputting the following:
Figure BDA0001846076910000131
unknown interference item K based on neural network minimum parameter learning methodfPerforming identification to obtain the optimal weight value, marking as M, and marking as M | | | W | | counting2Wherein M is a positive real number,
Figure BDA0001846076910000132
for the estimation of M,
Figure BDA0001846076910000133
s5: and optimizing the control rate of the uncertain disturbance item parameters after fitting by using a self-adaptive terminal sliding mode control method.
For a linear two-degree-of-freedom vehicle dynamics equation, a following error is defined:
e＝xd-x＝[βd-β,γd-γ]T
in the formula, xd＝[βd,γd]T，x＝[β,γ]T，e∈R2×1，e＝[e11,e21]TWherein γ isd，βdRespectively in steady-state steering
Figure BDA0001846076910000134
The desired yaw rate and the desired centroid slip angle.
Designing a sliding mode surface function as follows:
Figure BDA0001846076910000135
in which e is E.R2×1,
Figure BDA0001846076910000136
ρ > 0, p, q are odd numbers, and 1<p/q<2。
Derivation of the above slip form surface can be obtained:
Figure BDA0001846076910000137
the simplification can be obtained:
Figure BDA0001846076910000138
further simplification results in:
Figure BDA0001846076910000139
the formula is simplified to obtain:
Figure BDA00018460769100001310
s6: and utilizing a Lyapunov function to make the uncertain interference items bounded according to the optimized control rate.
In a more specific example, let:
Figure BDA00018460769100001311
Figure BDA00018460769100001312
Figure BDA00018460769100001313
bringing L, G, K into the above arrangement gives:
Figure BDA0001846076910000141
in order to quickly and correctly return the motion point of the control system to the nonlinear sliding mode surface, the switching control law operational expression is defined as follows:
Qu＝ηsgn(s)+μs
in the formula, Qu＝[Qi1,Qi1]TWhere (i ═ 1,2), η and μ are switching gains, and their values should be sufficiently large and η > 0 and μ > 0. sgn(s) is a switching function defined as follows:
Figure BDA0001846076910000142
designing a control order:
Figure BDA0001846076910000143
defining the Lyapunov function:
Figure BDA0001846076910000144
Vuthe first derivative with respect to time of(s) is:
Figure BDA0001846076910000145
control rate L to be designeduSubstituting into the formula to simplify:
Figure BDA0001846076910000146
wherein the content of the first and second substances,
Figure BDA0001846076910000147
therefore, the following steps are carried out:
Figure BDA0001846076910000148
wherein p and q are odd numbers and 1 < p/q < 2, whereby
Figure BDA0001846076910000149
And is
Qu∈R2×1,Qu＝[Q11,Q21]TFrom
Figure BDA00018460769100001410
The Lyapunov stability judgment is satisfied, and therefore the control strategy is proved to be feasible.
This makes it possible to obtain a method for bounding uncertain disturbance terms of the vehicle.
In a more specific example: the uncertain interference items include: parameter perturbation, modeling error and jitter matrix.
S7: and generating the total required yaw moment of the automobile according to the ideal value of the yaw angular velocity, the actual value of the yaw angular velocity, the ideal value of the centroid side deviation angle, the actual value of the centroid side deviation angle and the bounded uncertain disturbance term.
Inputting the difference value between the ideal value of the yaw angular velocity and the actual value of the yaw angular velocity and the difference value between the ideal value of the centroid side slip angle and the actual value of the centroid side slip angle into the following terminal sliding mode control formula:
Figure BDA0001846076910000151
and defines:
Figure BDA0001846076910000152
calculating the total required yaw moment of the automobile as follows:
Figure BDA0001846076910000153
in the formula (I); s is an offset factor, beta is a centroid slip angle, gamma is a yaw rate, and Fxfr、Fxfl、Fyfr、FyflFront right and front left tire force components in the longitudinal and lateral directions, respectively, d is the left and right wheel track, η is a design parameter, and η>0, a and b are formula parameters.
In a more specific example: the total required yaw moment can be expressed as:
vk＝Buk
in the formula:
Figure BDA0001846076910000154
uk＝(Fxfl Fxfr Fxrl Fxrr)T。
according to a quadratic programming optimization distribution control formula: minJ ═ Fxfl 2+Fxfr 2+Fxrl 2+Fxrr 2
Obtaining the dividing result:
Figure BDA0001846076910000155
in the formula: i represents i ═ fl, fr, rl, rr respectively for the front left, front right, rear left, rear right and tire directions, and FziMu is the road surface adhesion coefficient for each wheel vertical load.
S8: and controlling the moment regulator to distribute the yaw moment among the wheels according to the division result.
From the above description, the stability control method for the new energy automobile provided by the invention obtains the ideal values of the yaw rate and the centroid side-slip rate through the front wheel corners and the longitudinal speed based on the simplified linear two-degree-of-freedom vehicle model, performs self-adjustment approximation on unknown interference items in the system by introducing the RBF neural network self-adjustment control strategy, effectively improves the anti-interference capability of the system, determines the total required yaw moment under the condition of stable driving of the automobile through the difference value between the actual value of the yaw rate and the ideal value and the difference value between the actual value of the centroid side-slip rate and the ideal value, and finally performs torque distribution among wheels on the total required yaw moment through the quadratic programming method, thereby improving the driving stability of the automobile.
Based on the same inventive concept, the embodiment of the present application further provides a system for controlling stability of a new energy vehicle, which can be used to implement the method described in the foregoing embodiment, as described in the following embodiment. Because the principle of solving the problems of the new energy automobile stability control system is similar to the new energy automobile stability control method, the implementation of the new energy automobile stability control system can be referred to the implementation of the new energy automobile stability control method, and repeated parts are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.
The embodiment of the invention provides a specific implementation manner of a stability control system of a new energy automobile, which can realize a stability control method of the new energy automobile, and referring to fig. 4, the stability control system of the new energy automobile specifically comprises the following contents:
the brake unit 10 is used for acquiring the rotation angle and the longitudinal speed of the front wheel of the automobile;
a two-degree-of-freedom unit 20 for inputting the front wheel turning angle and the longitudinal speed into a linear two-degree-of-freedom model of the automobile, and generating ideal values of yaw angular velocity and centroid slip angle;
the neural network unit 30 is used for making uncertain disturbance terms of the automobile bounded according to an RBF neural network algorithm, the ideal value of the yaw angular velocity and the actual value of the yaw angular velocity, and the ideal value of the centroid slip angle and the actual value of the centroid slip angle;
a yaw moment unit 40, configured to generate a total required yaw moment of the automobile according to the ideal value of the yaw rate and the actual value of the yaw rate, the ideal value of the centroid yaw angle and the actual value of the centroid yaw angle, and the bounded uncertain disturbance term;
a dividing unit 50 for dividing the total required yaw moment into individual wheels and outputting the division result to the moment adjuster.
From the above description, the stability control system for the new energy automobile provided by the invention obtains the ideal value of the yaw angular velocity and the ideal value of the centroid side-slip angular velocity through the front wheel rotation angle and the longitudinal velocity based on the simplified linear two-degree-of-freedom vehicle model, and performs self-adjustment approximation and self-identification on unknown interference items in the automobile system by introducing the RBF neural network self-adjustment control strategy and taking the difference between the actual values and the ideal values of the yaw angular velocity and the centroid side-slip angle as the input of the RBF neural network, so that the automobile system is bounded, the anti-interference capability of the automobile system is effectively improved, and the purposes of weakening parameter perturbation, modeling errors and the jittering phenomenon are achieved. And finally, carrying out torque distribution among wheels on the total required yaw moment by a quadratic programming method, so that the actual running path of the automobile is consistent with the expected path, and further improving the running stability of the automobile.
The embodiment of the new energy vehicle stability control system provided by the application can be specifically used for executing the processing flow of the embodiment of the new energy vehicle stability control method in the above embodiment, and the functions of the processing flow are not repeated herein, and reference can be made to the detailed description of the embodiment of the method.
It can be known from the above description that the new energy vehicle stability control system provided by the embodiment of the present invention can establish a method for controlling the stability of a new energy vehicle that is intuitive, highly accurate, and easy to operate, and can obtain an ideal value of yaw angular velocity and an ideal value of centroid yaw angular velocity based on a simplified linear two-degree-of-freedom vehicle model through the front wheel rotation angle and the longitudinal velocity, and perform self-adjustment approximation and self-identification on an unknown interference item in a vehicle system by introducing an RBF neural network self-adjustment control strategy and using a difference between the actual value and the ideal value of the yaw angular velocity and the centroid yaw angular velocity as an input of the RBF neural network, so that the unknown interference item is bounded, and the anti-interference capability of the vehicle system is effectively improved, thereby achieving the purpose of weakening parameter perturbation, modeling error, and array shaking phenomenon. And finally, carrying out torque distribution among wheels on the total required yaw moment by a quadratic programming method, so that the actual running path of the automobile is consistent with the expected path, and further improving the running stability of the automobile.
An embodiment of the present application provides a specific implementation manner of an electronic device capable of implementing all steps in the method for controlling stability of a new energy vehicle in the foregoing embodiment, and referring to fig. 6, the electronic device specifically includes the following contents:
a processor (processor)1201, a memory (memory)1202, a communication Interface 1203, and a bus 1204;
the processor 1201, the memory 1202 and the communication interface 1203 complete mutual communication through the bus 1204; the communication interface 1203 is used for realizing information transmission among related devices such as a new energy automobile stability control system, a related server and a database;
the processor 1201 is configured to call a computer program in the memory 1202, and the processor implements all the steps in the first embodiment when executing the computer program, for example, the processor implements the following steps when executing the computer program:
step 100: and acquiring the rotation angle and the longitudinal speed of the front wheel of the automobile.
Step 200: and inputting the corner and the longitudinal speed of the front wheel into a linear two-degree-of-freedom model of the automobile to generate an ideal value of the yaw angular speed and an ideal value of the centroid slip angle.
Step 300: and according to the RBF neural network algorithm, the ideal value of the yaw angular velocity, the actual value of the yaw angular velocity, the ideal value of the centroid sideslip angle and the actual value of the centroid sideslip angle, making uncertain interference items of the automobile bounded.
Step 400: and generating the total required yaw moment of the automobile according to the ideal value of the yaw angular velocity, the actual value of the yaw angular velocity, the ideal value of the centroid side deviation angle, the actual value of the centroid side deviation angle and the bounded uncertain disturbance term.
Step 500: the total required yaw moment is divided into individual wheels, and the division result is output to a moment adjuster.
As can be seen from the above description, the electronic device provided in the embodiments of the present invention can obtain an ideal value of yaw angular velocity and an ideal value of centroid yaw angular velocity based on a simplified linear two-degree-of-freedom vehicle model through front wheel rotation angles and longitudinal velocities, introduce an RBF neural network self-tuning control strategy, and use a difference between an actual value and an ideal value of yaw angular velocity and centroid yaw angular velocity as an input of the RBF neural network, perform self-tuning approximation and self-identification on an unknown interference term in an automobile system, so as to make the interference-free capability of the automobile system limited, thereby achieving the purpose of reducing parameter perturbation, modeling error, and a matrix phenomenon. And finally, carrying out torque distribution among wheels on the total required yaw moment by a quadratic programming method, so that the actual running path of the automobile is consistent with the expected path, and further improving the running stability of the automobile.
An embodiment of the present application provides a computer-readable storage medium capable of implementing all the steps in the new energy vehicle stability control method in the foregoing embodiment, where the computer-readable storage medium stores a computer program, and the computer program implements all the steps in the first embodiment when executed by a processor, for example, the processor implements the following steps when executing the computer program:
step 100: and acquiring the rotation angle and the longitudinal speed of the front wheel of the automobile.
Step 200: and inputting the corner and the longitudinal speed of the front wheel into a linear two-degree-of-freedom model of the automobile to generate an ideal value of the yaw angular speed and an ideal value of the centroid slip angle.
Step 300: and according to the RBF neural network algorithm, the ideal value of the yaw angular velocity, the actual value of the yaw angular velocity, the ideal value of the centroid sideslip angle and the actual value of the centroid sideslip angle, making uncertain interference items of the automobile bounded.
Step 400: and generating the total required yaw moment of the automobile according to the ideal value of the yaw angular velocity, the actual value of the yaw angular velocity, the ideal value of the centroid side deviation angle, the actual value of the centroid side deviation angle and the bounded uncertain disturbance term.
Step 500: the total required yaw moment is divided into individual wheels, and the division result is output to a moment adjuster.
As can be seen from the above description, the computer-readable storage medium according to the embodiments of the present invention can obtain an ideal value of yaw angular velocity and an ideal value of centroid yaw angular velocity based on a simplified linear two-degree-of-freedom vehicle model through the front wheel rotation angle and the longitudinal velocity, perform self-tuning approximation and self-identification on an unknown interfering item in an automobile system by introducing an RBF neural network self-tuning control strategy and using a difference between the actual value and the ideal value of yaw angular velocity and centroid yaw angular velocity as an input of the RBF neural network, so as to make the RBF neural network self-tuning approach and self-identify the unknown interfering item, thereby effectively improving the anti-interference capability of the automobile system, and achieving the purpose of weakening parameter perturbation, modeling error, and the matrix phenomenon. And finally, carrying out torque distribution among wheels on the total required yaw moment by a quadratic programming method, so that the actual running path of the automobile is consistent with the expected path, and further improving the running stability of the automobile. The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Although the present application provides method steps as described in an embodiment or flowchart, additional or fewer steps may be included based on conventional or non-inventive efforts. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. In actual implementation, the system or client product may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.
Although embodiments of the present description provide method steps as described in embodiments or flowcharts, more or fewer steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When a system or an end product in practice executes, it can execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing, or even in the context of distributed data processing) according to the embodiments or methods shown in the drawings. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded.
For convenience of description, the above system is described with the functions divided into various modules, which are described separately. Of course, in implementing the embodiments of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, and the like. The above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, systems or units, and may be in an electrical, mechanical or other form.
Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the system included therein for implementing various functions may also be considered as a structure within the hardware component. Or even a system for performing various functions can be considered to be a software module implementing the method or a structure within a hardware component.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The embodiments of this specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The described embodiments may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of an embodiment of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
The above description is only an example of the embodiments of the present disclosure, and is not intended to limit the embodiments of the present disclosure. Various modifications and variations to the embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present specification should be included in the scope of the claims of the embodiments of the present specification.

Claims (9)

1. A stability control method for a new energy automobile is characterized by comprising the following steps:
acquiring the rotation angle and the longitudinal speed of a front wheel of an automobile;
inputting the corner and the longitudinal speed of the front wheel into a linear two-degree-of-freedom model of the automobile to generate an ideal value of the yaw angular speed and an ideal value of the centroid slip angle;
according to an RBF neural network algorithm, the ideal value of the yaw angular velocity, the actual value of the yaw angular velocity, the ideal value of the centroid sideslip angle and the actual value of the centroid sideslip angle, making uncertain interference items of the automobile bounded;
generating a total required yaw moment of the automobile according to the ideal value of the yaw velocity, the actual value of the yaw velocity, the ideal value of the centroid side slip angle, the actual value of the centroid side slip angle and the bounded uncertain disturbance terms, and the method comprises the following steps:
through yaw angular velocity actual value and ideal value difference, barycenter sideslip angle actual value and ideal value difference, total demand yaw moment under the stable travelling prerequisite of car is decided out, specifically is: inputting the difference value between the ideal value of the yaw angular velocity and the actual value of the yaw angular velocity and the difference value between the ideal value of the centroid side slip angle and the actual value of the centroid side slip angle into the following terminal sliding mode control formula:
Figure FDA0002967405320000011
and defines:
Figure FDA0002967405320000012
calculating the total required yaw moment of the automobile as follows:
Figure FDA0002967405320000013
in the formula (I); s is an offset factor, beta is a centroid slip angle, gamma is a yaw rate, and Fxfr、Fxfl、Fyfr、FyflFront right and front left tire force components in the longitudinal and lateral directions, respectively, d is the left and right wheel track, η is a design parameter, and η>0, a and b are formula parameters;
the total required yaw moment is divided into individual wheels, and the division result is output to a moment adjuster.
2. The stability control method of the new energy automobile according to claim 1, wherein the ideal value of the centroid slip angle is expressed as follows:
Figure FDA0002967405320000021
the expression of the ideal value of the yaw rate is as follows:
Figure FDA0002967405320000022
wherein, betadIs an ideal value of the centroid slip angle, gammadK is an ideal value of the yaw rate, K is a stability coefficient, and K is taken to be 2 multiplied by 10-3，βmaxIs the maximum value of the mass center slip angle, delta is the front wheel rotation angle, m is the automobile mass, vxThe longitudinal speed of the automobile and the mu is the road adhesion coefficient; g is the acceleration of gravity, CrIs the form factor of the lateral force curve of the left wheel and the right wheel.
3. The new energy vehicle stability control method according to claim 1, before the step of limiting the uncertain disturbance term of the vehicle according to the RBF neural network algorithm, the ideal value of the yaw rate and the actual value of the yaw rate, the ideal value of the centroid slip angle and the actual value of the centroid slip angle, further comprising: and obtaining the actual value of the yaw angular velocity and the actual value of the centroid slip angle of the automobile through an Inertial Measurement Unit (IMU).
4. The new energy vehicle stability control method according to claim 1, wherein the bounding uncertain disturbance terms of the vehicle according to the RBF neural network algorithm, the ideal value of the yaw rate, the actual value of the yaw rate, the ideal value of the centroid slip angle and the actual value of the centroid slip angle comprises:
calculating a difference between the ideal value of the yaw rate and the actual value of the yaw rate, and a difference between the ideal value of the centroid slip angle and the actual value of the centroid slip angle;
inputting the difference value of the ideal value of the yaw angular velocity and the actual value of the yaw angular velocity and the difference value of the ideal value of the centroid side slip angle and the actual value of the centroid side slip angle into the RBF neural network algorithm to generate uncertain disturbance term parameters;
fitting the uncertain disturbance item parameters by using a minimum parameter method;
optimizing the control rate of the uncertain disturbance item parameters after fitting by using a self-adaptive terminal sliding mode control method;
and utilizing a Lyapunov function to make the uncertain interference items bounded according to the optimized control rate.
5. The stability control method of the new energy automobile according to claim 4, wherein the uncertain disturbance term comprises: parameter perturbation, modeling error and jitter matrix.
6. The new energy vehicle stability control method according to claim 1, wherein the generating of the total demanded yaw moment of the vehicle according to the ideal value of the yaw rate, the actual value of the yaw rate, the ideal value of the centroid slip angle, the actual value of the centroid slip angle and the bounded uncertain disturbance term comprises:
and inputting the difference value between the ideal value of the yaw angular velocity and the actual value of the yaw angular velocity and the difference value between the ideal value of the centroid side slip angle and the actual value of the centroid side slip angle into a terminal sliding mode control formula to generate the total required yaw moment of the automobile.
7. The utility model provides a new energy automobile stability control system which characterized in that includes:
the brake unit is used for acquiring the rotation angle and the longitudinal speed of the front wheel of the automobile;
the two-degree-of-freedom unit is used for inputting the corner and the longitudinal speed of the front wheel into a linear two-degree-of-freedom model of the automobile and generating an ideal value of the yaw angular speed and an ideal value of the centroid slip angle;
the neural network unit is used for enabling uncertain interference items of the automobile to be bounded according to an RBF neural network algorithm, the ideal value of the yaw velocity, the actual value of the yaw velocity, the ideal value of the centroid sideslip angle and the actual value of the centroid sideslip angle;
the yaw moment unit is used for generating the total required yaw moment of the automobile according to the ideal value of the yaw angular velocity, the actual value of the yaw angular velocity, the ideal value of the centroid side deviation angle, the actual value of the centroid side deviation angle and the bounded uncertain disturbance term;
the yaw moment unit is specifically used for deciding the total required yaw moment under the premise of driving the automobile stably through the difference value between the actual value of the yaw velocity and the ideal value and the difference value between the actual value of the centroid sideslip angle and the ideal value, and specifically comprises the following steps: inputting the difference value between the ideal value of the yaw angular velocity and the actual value of the yaw angular velocity and the difference value between the ideal value of the centroid side slip angle and the actual value of the centroid side slip angle into the following terminal sliding mode control formula:
Figure FDA0002967405320000031
and defines:
Figure FDA0002967405320000032
calculating the total required yaw moment of the automobile as follows:
Figure FDA0002967405320000033
in the formula (I); s is an offset factor, beta is a centroid slip angle, gamma is a yaw rate, and Fxfr、Fxfl、Fyfr、FyflFront right and front left tire force components in the longitudinal and lateral directions, respectively, d is the left and right wheel track, η is a design parameter, and η>0，a，b is a formula parameter;
and a dividing unit for dividing the total required yaw moment into individual wheels and outputting the division result to the moment adjuster.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and operable on the processor, wherein the processor implements the steps of the new energy vehicle stability control method according to any one of claims 1 to 6 when executing the computer program.
9. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the new energy vehicle stability control method according to any one of claims 1 to 6.
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