Intelligent vehicle motion prediction control system and method based on prediction model

Abstract

The application discloses a kind of intelligent car motion prediction control system and method based on prediction model, the prediction control system includes: data acquisition module, it includes: steering wheel rotation angle sensor, for collecting steering wheel rotation angle signal;IMU sensor, for collecting the longitudinal acceleration, lateral acceleration, yaw rate and heading angle of vehicle;GPS sensor is used to collect vehicle longitudinal speed, lateral speed, longitudinal displacement and lateral displacement;Wheel speed sensor, for collecting the wheel speed of four wheels;Central control module, vehicle state prediction is carried out according to the data collected by the data acquisition module, and control instruction is issued according to prediction result;Execution module, which executes the control instruction;Wherein, the execution module includes steering wheel, left front wheel motor, right front wheel motor, left rear wheel motor, right front wheel motor.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of intelligent vehicles, and particularly relates to an intelligent vehicle motion prediction control system and method based on a prediction model. BACKGROUND

[0002] With the continuous in-depth development of the electrification and intelligentization of automobiles, intelligent electric vehicles have brought convenience to people's travel while further improving driving quality, driving safety and driving pleasure. Vehicle motion control is a prerequisite for ensuring normal driving of vehicles under various working conditions. For example, the ABS (Antilock Brake System) system can make the vehicle have a larger road adhesion force under emergency braking conditions, ensure that the vehicle has a larger braking deceleration, and shorten the braking safety distance; the ESP (Electronic Stability Program) system can ensure that the vehicle can maintain stable driving under insufficient steering or excessive steering. The control systems currently equipped on vehicles all use threshold control, that is, the vehicle actively intervenes only when the current state of the vehicle reaches the control boundary. Although this can improve driving performance, the safety, smoothness and passenger comfort of the control still need to be improved. SUMMARY

[0003] The purpose of the application is to provide an intelligent vehicle motion prediction control system and method based on a prediction model, which can improve the overall vehicle control effect through prediction of the future state of the vehicle.

[0004] The technical scheme provided by the application is as follows:

[0005] An intelligent vehicle motion prediction control system based on a prediction model comprises:

[0006] A data acquisition module comprises:

[0007] A steering wheel angle sensor is configured to acquire a steering wheel angle signal.

[0008] An IMU sensor is configured to acquire the longitudinal acceleration, lateral acceleration, yaw rate and heading angle of the vehicle.

[0009] A GPS sensor is configured to acquire the longitudinal vehicle speed, lateral vehicle speed, longitudinal displacement and lateral displacement of the vehicle.

[0010] A wheel speed sensor is configured to acquire the wheel speed of four wheels.

[0011] A central control module is configured to predict the state of the vehicle according to the data acquired by the data acquisition module and issue a control instruction according to the prediction result.

[0012] An execution module is configured to execute the control instruction.

[0013] The execution module comprises a steering wheel, a left front wheel motor, a right front wheel motor, a left rear wheel motor and a right rear wheel motor.

[0014] A smart vehicle motion prediction control method based on a prediction model, comprising:

[0015] The longitudinal vehicle speed signal, the lateral vehicle speed signal, the yaw rate signal, the longitudinal displacement signal, the lateral displacement signal and the heading angle signal collected in multiple sampling periods before the current time are preprocessed to obtain longitudinal vehicle speed standard values, lateral vehicle speed standard values, yaw rate standard values, longitudinal displacement standard values, lateral displacement standard values and heading angle standard values.

[0016] The system state quantity is The control quantity is U con = δ f The vehicle state is predicted according to the longitudinal vehicle speed standard values, the lateral vehicle speed standard values, the yaw rate standard values, the longitudinal displacement standard values, the lateral displacement standard values and the heading angle standard values to obtain the yaw rate prediction value, the mass side slip angle prediction value, the front axle side slip angle prediction value, the rear axle side slip angle prediction value and the lateral acceleration prediction value at each time in the prediction time domain.

[0017] Wherein, v x is the longitudinal vehicle speed, v y is the lateral vehicle speed, ω is the yaw rate, X is the longitudinal displacement, Y is the lateral displacement, is the heading angle.

[0018] The yaw rate prediction value, the mass side slip angle prediction value, the front axle side slip angle prediction value, the rear axle side slip angle prediction value and the lateral acceleration prediction value at each time in the prediction time domain are compared with the yaw rate critical stability boundary, the mass side slip angle critical stability boundary, the front axle side slip angle critical stability boundary, the rear axle side slip angle critical stability boundary and the lateral acceleration critical stability boundary at each time.

[0019] If more than one of the yaw rate prediction value, the mass side slip angle prediction value, the front axle side slip angle prediction value, the rear axle side slip angle prediction value and the lateral acceleration prediction value is greater than the corresponding critical stability boundary, the stability level of the vehicle in the prediction time domain is determined, and the steering and / or four-wheel torque of the vehicle at the next time is adjusted according to the stability level.

[0020] Preferably, the vehicle state is predicted by a discrete system prediction state matrix.

[0021] Wherein, the discrete system prediction state matrix is:

[0022] X sta (k+1) = (A pre Tti +I)X sta (k)+(B pre T ti )U con (k);

[0023] wherein, T ti is the period of discrete system, I is unit matrix;

[0024]

[0025]

[0026] wherein, C αf , C αr are front and rear wheel cornering stiffness respectively, a, b are the distance from the mass center to the front axle and the distance from the mass center to the rear axle respectively, m is the mass of the whole vehicle, I z is the moment of inertia.

[0027] Preferably, when one of the yaw rate prediction value, the mass center cornering angle prediction value, the front axle cornering angle prediction value, the rear axle cornering angle prediction value, the lateral acceleration prediction value is greater than the corresponding critical stability boundary:

[0028] comparing the prediction value of this item at each time with the critical stability boundary, the value corresponding to the time when the prediction value is greater than the critical stability boundary is recorded as X f1 , and the critical stability boundary corresponding to the time of this item is recorded as X fbou1 , then:

[0029] X f1 =[X f1 (1) X f1 (2)…X f1 (N f1 )];

[0030] X fbou1 =[X fbou1 (1) X fbou1 (2)…X fbou1 (N f1 )];

[0031] the error of the prediction value of this item corresponding to the corresponding time and the critical stability boundary is recorded as ΔX f1 , then:

[0032] |ΔX f1 |=[X f1 (1)-X fbou1 (1)||X f1 (2)-X fbou1 (2)|…|X f1 (N f1 )-X fbou1(N f1 )|];

[0033] like The vehicle is judged to be in the first stability level within the prediction time domain, and the vehicle steering is adjusted:

[0034] Control the front wheel angle at the next moment to be:

[0035] Among them, κ fb11 is the boundary coefficient; is the gain coefficient; is Δδ ff11 The proportional gain, is Δδ ff11 Integral gain, Δδ ff11 is the additional front wheel turning angle;

[0036] like The vehicle is judged to be in the second stability level within the prediction time domain, and the vehicle steering and four-wheel torque are adjusted:

[0037] like The vehicle is judged to be in the third stability level within the predicted time domain, and the four-wheel torque of the vehicle is adjusted.

[0038] Preferably, when it is determined that the vehicle is at the second stability level within the prediction time domain, the front wheel turning angle at the next moment is controlled to be:

[0039] Among them, κ fb12 is the boundary coefficient, is the gain coefficient, is Δδ ff12 The proportional gain, is Δδ ff12 Integral gain, Δδ ff12 is the additional front wheel turning angle;

[0040] The wheel end torque of the four wheels at the next moment is controlled as follows:

[0041] Left front wheel: T fl12 =T fl +ζ 12 ΔT fl12 ;

[0042] Right front wheel: T fr12 =T fr +ζ 12 ΔT fr12 ;

[0043] Left rear wheel: T rl12 =T rl +ζ12 ΔT rl12 ;

[0044] Right rear wheel: T rr12 = T rr + ζ 12 ΔT rr12 ;

[0045] wherein ζ 12 is an additional torque adjustment coefficient; is a front axle additional yaw moment proportion coefficient, B f is a front axle track, B r is a rear axle track; is a proportional gain of ΔM ff12 , is an integral gain of ΔM ff12 , ΔM ff12 is an additional yaw moment.

[0046] Preferably, when it is judged that the vehicle is in the third stability level in the prediction time domain, the four-wheel wheel end torques of the next time are controlled as follows:

[0047] Left front wheel: T fl13 = T fl + ζ 13 ΔT fl13 ;

[0048] Right front wheel: T fr13 = T fr + ζ 13 ΔT fr13 ;

[0049] Left rear wheel: T rl13 = T rl + ζ 13 ΔT rl13 ;

[0050] Right rear wheel: T rr13 = T rr + ζ 13 ΔT rr13 ;

[0051] wherein ζ 13 is an additional torque adjustment coefficient; is a front axle additional yaw moment proportion coefficient, B f is a front axle track; is a proportional gain of ΔM ff13 , is an integral gain of ΔM ff13Integral gain of yaw moment; ΔM ff13 is added to the yaw moment.

[0052] Preferably, two of the yaw angular velocity prediction value, the center of mass side slip angle prediction value, the front axle side slip angle prediction value, the rear axle side slip angle prediction value, and the lateral acceleration prediction value are greater than the corresponding critical stability boundary:

[0053] The prediction values of the two items at each time are compared with the critical stability boundary, and the value corresponding to the time when the prediction value is greater than the critical stability boundary is recorded as X f21 , X f22 , the corresponding critical stability boundary is recorded as X fbou21 , X fbou22 , then:

[0054] X f21 = [X f21 (1) X f21 (2) … X f21 (N f21 )];

[0055] X f22 = [X f22 (1) X f22 (2) … X f22 (N f22 )];

[0056] X fbou21 = [X fbou21 (1) X fbou21 (2) … X fbou21 (N f21 )];

[0057] X fbou22 = [X fbou22 (1) X fbou22 (2) … X fbou22 (N f22 )];

[0058] Wherein, N f21 , N f22 is the number of each item exceeding the stability boundary;

[0059] The error of each prediction value corresponding to the corresponding time and the corresponding critical stability boundary is recorded as ΔX f21 , ΔX f22 ;

[0060] |ΔX f21 | = [X f21 (1) -X fbou21 (1) | | X f21 (2) -X fbou21 (2) | … | X f21 (Nf21 )-X fbou21 (N f21 )|];

[0061] |ΔX f22 |=[X f22 (1)-X fbou22 (1)||X f22 (2)-X fbou22 (2)|…|X f22 (N f22 )-X fbou22 (N f22 )|];

[0062] like The vehicle is judged to be in the first stability level within the prediction time domain, and the vehicle steering is adjusted:

[0063] Control the front wheel angle at the next moment to be:

[0064] Among them, η f2 =ε f2 η f21 +(1-ε f2 )η f22 , η f2 =ε f2 η f21 +(1-ε f2 )η f22 , is the stability coefficient threshold corresponding to the first stability level; is the gain coefficient; is Δδ ff21 The proportional gain, is Δδ ff21 The integral gain, Δδ ff21 is the additional front wheel turning angle;

[0065] like The vehicle is judged to be in the second stability level within the prediction time domain, and the vehicle steering and four-wheel torque are adjusted: is the stability coefficient threshold corresponding to the second stability level;

[0066] like The vehicle is judged to be in the third stability level within the predicted time domain, and the four-wheel torque of the vehicle is adjusted.

[0067] Preferably, when it is determined that the vehicle is at the second stability level within the prediction time domain, the front wheel turning angle at the next moment is controlled to be:

[0068] in, is the gain coefficient, is Δδ ff22 The proportional gain, is Δδ ff22 The integral gain, Δδ ff22 is the additional front wheel turning angle;

[0069] The wheel end torque of the four wheels at the next moment is controlled as follows:

[0070] Left front wheel: T fl22 =T fl +ζ 22 ΔT fl22 ;

[0071] Right front wheel: T fr22 =T fr +ζ 22 ΔT fr22 ;

[0072] Left rear wheel: T rl22 =T rl +ζ 22 ΔT rl22 ;

[0073] Right rear wheel: T rr22 =T rr +ζ 22 ΔT rr22 ;

[0074] Among them, 22 is the additional torque adjustment coefficient; B is the additional yaw moment coefficient of the front axle, f is the front axle track; ΔM ff22 Proportional gain, K IΔMf22 ΔM ff22 The integral gain, ΔM ff22 is the additional yaw moment; B r Rear axle track.

[0075] Preferably, when it is determined that the vehicle is at the third stability level within the prediction time domain, the wheel end torques of the four wheels at the next moment are controlled to be:

[0076] Left front wheel: T fl23 =T fl +ζ 23 ΔT fl23 ;

[0077] Right front wheel: T fr23 =T fr +ζ 23 ΔTfr23 ;

[0078] Left rear wheel: T rl23 =T rl +ζ 23 ΔT rl23 ;

[0079] Right rear wheel: T rr23 =T rr +ζ 23 ΔT rr23 ;

[0080] Among them, 23 is the additional torque adjustment coefficient; Add the yaw moment ratio coefficient to the front axle; ΔM ff23 The proportional gain, ΔM ff23 Integral gain; ΔM ff23 is the additional yaw moment.

[0081] Preferably, when three or more of the yaw rate prediction value, the center of mass slip angle prediction value, the front axle slip angle prediction value, the rear axle slip angle prediction value, and the lateral acceleration prediction value are greater than their corresponding critical stability boundaries:

[0082] Compare the predicted values ​​of each item at each moment that are greater than the critical stability boundary with the critical stability boundary, and record the value corresponding to the moment when the predicted value is greater than the critical stability boundary as X f3n , the corresponding critical stability boundary is denoted as X fbou3n ,but:

[0083] X f3n =[X f3n (1) X f3n (2)…X f3n (N f3n )];

[0084] X fbou3n =[X fbou3n (1) X fbou3n (2)…X fbou3n (N f3n )];

[0085] Among them, N f3n is the number of items that exceed the stability boundary, and the value range of n is 1 to n bou ;

[0086] The error between the predicted values ​​at the corresponding time and the corresponding critical stability boundary is recorded as ΔX f3n ;

[0087] |ΔX f3n |=[|X f3n (1)-X fbou3n (1)||X f3n (2)-X fbou3n (2)|…|X f3n (N f3n )-X fbou3n (N f3n )|];

[0088] If , it is judged that the vehicle is in the first stability level in the prediction time domain, and the vehicle is adjusted in steering:

[0089] The front wheel steering angle at the next time is controlled as:

[0090] Wherein, is the stability coefficient threshold corresponding to the first stability level; is the gain coefficient; is the proportional gain of Δδ ff31 , Δδ ff31 is the integral gain of Δδ ff31 , and Δδ fl32 is the additional front wheel steering angle; If

[0091] , it is judged that the vehicle is in the second stability level in the prediction time domain, and the four-wheel wheel end torque at the next time is controlled as: Left front wheel: T fl =T 32 +ζ fl32 ΔT fr32 ;

[0092] Right front wheel: T fr =T 32 +ζ fr32 ΔT rl32 ;

[0094] Left rear wheel: T rl =T 32 +ζ rl32 ΔT rr32 ;

[0095] Right rear wheel: T rr =T 32 +ζ rr32 ΔT 32 ;

[0096] Wherein, ζ ff32 is an additional torque adjustment coefficient; a yaw moment ratio coefficient is added to the front axle; a proportional gain of ΔM ff32 , an integral gain of ΔM ff32 , ΔM ff32 is an additional yaw moment.

[0097] The beneficial effects of the present application are:

[0098] The intelligent vehicle motion prediction control system and method based on a prediction model provided by the present application can detect the state of the vehicle in multiple dimensions, accurately predict the future motion state of the vehicle, and further improve the overall vehicle control effect. BRIEF DESCRIPTION OF DRAWINGS

[0099] Figure 1 The structure diagram of the intelligent vehicle motion prediction control system based on a prediction model described in the present application.

[0100] Figure 2 The flowchart of the intelligent vehicle motion prediction control method based on a prediction model described in the present application. DETAILED DESCRIPTION

[0101] The present application will be further described in detail below with reference to the accompanying drawings, so that those skilled in the art can implement it according to the description.

[0102] As shown in Figure 1 , the present application provides an intelligent vehicle motion prediction control system based on a prediction model, which is composed of a data acquisition module, a central control module and an execution control module.

[0103] The data acquisition module includes a steering wheel angle sensor, an IMU (Inertial Measurement Unit) sensor, a GPS (Global Positioning System) sensor and a wheel speed sensor. The steering wheel angle sensor is used to collect the steering wheel angle signal δ sw ; the IMU sensor is an inertial measurement unit, which is used to collect the longitudinal acceleration a x , lateral acceleration a y , yaw rate ω and heading angle of the vehicle. The GPS sensor is used to collect the longitudinal vehicle speed v x , lateral vehicle speed v y , longitudinal displacement X and lateral displacement Y of the vehicle; the wheel speed sensor is used to collect the wheel speed n ij of the four wheels, wherein the value range of i and j is 1-2, and n 11 represents the left front wheel, n12 represents a right front wheel, n 21 represents a left rear wheel, n 22 represents a right rear wheel.

[0104] The central control module is composed of a data processing module, a prediction model module and a decision control module. p The data processing module is used for processing the data collected by the data collection module; the prediction model module is used for predicting the vehicle state in a prediction time domain N ts The decision control module is used for making decisions according to the prediction result of the prediction model module and issuing control instructions to the execution control module.

[0105] The execution control module includes a steering wheel, a left front wheel motor, a right front wheel motor, a left rear wheel motor and a right rear wheel motor. The steering wheel is used for controlling the driving direction of the vehicle. The left front wheel motor, the right front wheel motor, the left rear wheel motor and the right rear wheel motor are respectively used for controlling and adjusting the torque of the four wheels.

[0106] As shown in Figure 2 The application also provides an intelligent vehicle motion prediction control method based on a prediction model, and the specific method is as follows:

[0107] I. Data collection and processing

[0108] When the system is just started, the data amount of the first N ts (N ts The value range is 8-12) periods is less and insufficient for data processing, at this time, the data collected by the data collection module is directly used as the output of the data processing module; after more than N ts periods, the data of the first N tc -1 periods (N tc The value range is 4-6) at the current time are recorded, the data of the first N tc -1 periods and the current data together constitute the data of N tc periods; then, the data of N tc periods are processed to obtain the standard value of the corresponding data as the output value of the data processing module.

[0109] (1) The data processing method of the steering wheel angle signal is as follows,

[0110] Steering wheel angle:

[0111] The steering wheel angle signal is fitted according to the following formula to obtain the standard value of the steering wheel angle:

[0112]

[0113] In the formula, wherein the value range is and

[0114] (2) The data processing method of the longitudinal acceleration signal is as follows,

[0115] Longitudinal acceleration: The longitudinal acceleration signal is fitted according to the following formula to obtain the longitudinal acceleration standard value:

[0116] In the formula, wherein the value range is and

[0117] (3) The data processing method of the lateral acceleration signal is as follows,

[0118] Lateral acceleration: The lateral acceleration signal is fitted according to the following formula to obtain the lateral acceleration standard value:

[0119] In the formula, wherein the value range is and

[0120] (4) The data processing method of the yaw rate signal is as follows,

[0121] Yaw rate: The yaw rate signal is fitted according to the following formula to obtain the yaw rate standard value:

[0122] In the formula, wherein the value range is and

[0123] (5) The data processing method of the heading angle signal is as follows,

[0124] Heading angle: The heading angle signal is fitted according to the following formula to obtain the heading angle standard value:

[0125]

[0126] In the formula, wherein the value range is and

[0127] (6) The data processing method of the longitudinal vehicle speed is as follows,

[0128] Longitudinal vehicle speed:

[0129] The longitudinal speed signal is fitted according to the following equation to obtain the longitudinal speed standard value:

[0130]

[0131] In the equation, wherein The value range is and

[0132] (7) The data processing method of lateral vehicle speed is as follows,

[0133] Lateral vehicle speed:

[0134] The lateral speed signal is fitted according to the following equation to obtain the lateral speed standard value:

[0135]

[0136] In the equation, wherein The value range is and

[0137] (8) The data processing method of longitudinal displacement is as follows,

[0138] Longitudinal displacement:

[0139] The longitudinal displacement signal is fitted according to the following equation to obtain the longitudinal displacement standard value:

[0140]

[0141] In the equation, wherein The value range is and

[0142] (9) The data processing method of lateral displacement is as follows,

[0143] Lateral displacement:

[0144] The lateral displacement signal is fitted according to the following equation to obtain the lateral displacement standard value:

[0145]

[0146] In the equation, wherein The value range is And

[0147] (10) The data processing method of the left front wheel speed is as follows,

[0148] Left front wheel speed: [n 11 (1) n 11 (2)…n 11 (N tc )];

[0149] The left front wheel speed signal is fitted according to the following formula to obtain the left front wheel speed standard value:

[0150]

[0151] In the formula, Where The value range is And

[0152] (11) The data processing method of the right front wheel speed is as follows,

[0153] Right front wheel speed: [n 12 (1) n 12 (2)…n 12 (N tc )];

[0154] The right front wheel speed signal is fitted according to the following formula to obtain the right front wheel speed standard value:

[0155]

[0156] In the formula, Where The value range is And

[0157] (12) The data processing method of the left rear wheel speed is as follows,

[0158] Left rear wheel speed: [n 21 (1) n 21 (2)…n 21 (N tc )];

[0159] The left rear wheel speed signal is fitted according to the following formula to obtain the left rear wheel speed standard value:

[0160] In the formula, Where The value range is And

[0161] (13) The data processing method of the right rear wheel speed is as follows,

[0162] Right rear wheel speed: [n 22 (1) n 22 (2)…n 22 (N tc )];

[0163] The right rear wheel speed signal is fitted according to the following formula to obtain the right rear wheel speed standard value:

[0164] In the formula, wherein The value range is And

[0165] Wherein, the front wheel steering angle is:

[0166]

[0167] In the formula, i swf is the transmission ratio of the steering wheel steering angle and the front wheel steering angle.

[0168] II. Model-based state prediction

[0169] Take the system state quantity as Control quantity U con = δ f , wherein The prediction state matrix of the discrete system is as follows,

[0170] X sta (k+1) = (A pre T ti +I)X sta (k) + (B pre T ti )U con (k);

[0171] Wherein, T ti is the period of the discrete system,

[0172]

[0173]

[0174] In the above matrix, C αf , C αr are the front and rear wheel cornering stiffness respectively, a and b are the distances from the mass center to the front axle and the mass center to the rear axle respectively, m is the vehicle mass, I z is the moment of inertia, these parameters are known parameters; other parameter values are substituted into the output values of the data processing module for state prediction.

[0175] The system predicted state matrix can be obtained by v y , v x , ω, Y, X, The predicted value at each time in the prediction horizon.

[0176] Take The system predicted state at k+1 time is rewritten as:

[0177]

[0178] The centroid side slip angle is:

[0179]

[0180] The front axle side slip angle is:

[0181]

[0182] The rear axle side slip angle is:

[0183]

[0184] The system predicted state at k+2 time is:

[0185]

[0186] The centroid side slip angle is:

[0187]

[0188] The front axle side slip angle is:

[0189]

[0190] The rear axle side slip angle is:

[0191]

[0192] The system predicted state at k+3 time is:

[0193]

[0194] The centroid side slip angle is:

[0195]

[0196] The front axle side slip angle is:

[0197]

[0198] The rear axle side slip angle is:

[0199]

[0200] Thus recursively, the system predicted state at the N p

[0201]

[0202]

[0203]

[0204]

[0205]

[0206]

[0207]

[0208] III. Predicted critical stability boundary

[0209] Critical stability boundary at time k:

[0210] Critical stability boundary for yaw rate:

[0211]

[0212] where λ ω is the critical stability boundary coefficient for yaw rate, μ is the road adhesion coefficient, and g is the gravitational acceleration;

[0213] Critical stability boundary for side slip angle:

[0214]

[0215] where λ β is the critical stability boundary coefficient for side slip angle;

[0216] Critical stability boundary for front axle side slip angle:

[0217]

[0218] where λ is the critical stability boundary coefficient for front axle side slip angle;

[0219] Critical stability boundary for rear axle side slip angle:

[0220]

[0221] where λ is the critical stability boundary coefficient for rear axle side slip angle;

[0222] Critical stability boundary for lateral acceleration: ​​​​

[0223]

[0224] wherein, is the lateral acceleration critical stability boundary coefficient, is the longitudinal acceleration component coefficient;

[0225] Critical stability boundary at time k+1:

[0226] Critical stability boundary of yaw rate:

[0227]

[0228] wherein λ ω is the yaw rate critical stability boundary coefficient, μ is the road adhesion coefficient, and g is the gravitational acceleration;

[0229] Critical stability boundary of the center of mass side slip angle:

[0230]

[0231] wherein λ β is the center of mass side slip angle critical stability boundary coefficient;

[0232] Critical stability boundary of the front axle side slip angle:

[0233]

[0234] wherein, is the front axle side slip angle critical stability boundary coefficient;

[0235] Critical stability boundary of the rear axle side slip angle:

[0236]

[0237] wherein, is the rear axle side slip angle critical stability boundary coefficient;

[0238] Critical stability boundary of lateral acceleration:

[0239]

[0240] wherein, is the lateral acceleration critical stability boundary coefficient, is the longitudinal acceleration component coefficient;

[0241] By analogy, all the predicted critical stability boundaries at times k~k+N p can be calculated.

[0242] Four, decision control

[0243] A: If only one of the yaw rate, the mass side slip angle, the front axle side slip angle, the rear axle side slip angle, and the lateral acceleration of the vehicle exceeds the critical stability boundary in the prediction time domain, the item exceeding the critical stability boundary is recorded as X f1 , and the corresponding critical stability boundary is recorded as X fbou1 ;

[0244] X f1 = [X f1 (1) X f1 (2) … X f1 (N f1 )];

[0245] X fbou1 = [X fbou1 (1) X fbou1 (2) … X fbou1 (N f1 )];

[0246] N f1 is the number of items exceeding the stability boundary;

[0247] The error between the part exceeding the critical stability boundary and the corresponding critical stability boundary is recorded as ΔX f1 ,

[0248] |ΔX f1 | = [|X f1 (1) -X fbou1 (1) |X f1 (2) -X fbou1 (2) | … |X f1 (N f1 ) -X fbou1 (N f1 ) |];

[0249] If , where κ fb11 is a boundary coefficient, and the value range is 0.8-1.2; it indicates that the unstable state of the vehicle in the prediction time domain is weak, which is micro-instability (the first stability level), and the active steering control is adjusted by adding a front wheel, and the additional front wheel steering angle is:

[0250]

[0251] wherein, is the proportional gain of Δδ ff11 , is the integral gain of Δδ ff11 , and Δδ ff11 is the additional front wheel steering angle; the value range is 0-10, the value range is 0-100, and the specific value needs to be determined by actual situation through experimental test;

[0252] The actual output front wheel steering angle is:

[0253]

[0254] wherein, is a gain coefficient, and the value range is 0.8-1.2;

[0255] If wherein, κ fb12 is a boundary coefficient, and the value range of κ fb12 is 1.1*κ fb11 -1.2*κ fb11 ; it indicates that the vehicle is in a medium unstable state (second stability level) in the prediction time domain, and the stability of the vehicle is improved by steering and four-wheel torque adjustment, and the additional front wheel steering angle and the additional yaw moment are respectively:

[0256]

[0257]

[0258] wherein, is the proportional gain of Δδ ff12 , and the value range is 0-10, is the integral gain of Δδ ff12 , and the value range is 0-100, and Δδ ff12 is the additional front wheel steering angle;

[0259] is the proportional gain of ΔM ff12 , and the value range is 0-10, is the integral gain of ΔM ff12 , and the value range is 0-100, and ΔM ff12 is the additional yaw moment;

[0260] The actual values of the above parameters need to be determined through real vehicle experiment tests;

[0261] The actual output front wheel steering angle is:

[0262]

[0263] wherein, is a gain coefficient, and the value range is 0.8-1.2;

[0264] The additional yaw moment is converted to each wheel end torque,

[0265] Left front wheel:

[0266] wherein, The additional yaw moment ratio of the front axle is in the range of 0 to 1. f is the front axle track;

[0267] Right front wheel:

[0268] Left rear wheel: Among them, B r is the rear axle track;

[0269] Right rear wheel:

[0270] The actual torque at the wheel end is,

[0271] Left front wheel: T fl12 =T fl +ζ 12 ΔT fl12 ;

[0272] Right front wheel: T fr12 =T fr +ζ 12 ΔT fr12 ;

[0273] Left rear wheel: T rl12 =T rl +ζ 12 ΔT rl12 ;

[0274] Right rear wheel: T rr12 =T rr +ζ 12 ΔT rr12 ;

[0275] Among them, 12 is the additional torque adjustment coefficient, ranging from 0 to 0.4, T fl 、T fr 、T rl 、T rr The basic torque at the wheel end is calculated by the vehicle control system based on the driver's accelerator and brake pedal signals, and the response data is sent to each controller via the CAN bus.

[0276] like This indicates that the vehicle is extremely unstable (the third stability level) in the prediction time domain. The stability of the vehicle can be improved by adjusting the four-wheel torque. The additional yaw moment is:

[0277]

[0278] in, ΔM ff13 The proportional gain ranges from 0 to 10. ΔM ff13Integral gain, value range 0~100; the actual value of the above coefficient needs to be determined through real vehicle test;

[0279] The additional yaw moment is converted to each wheel end torque,

[0280] Left front wheel:

[0281] Wherein, Front axle additional yaw moment proportion coefficient, value range 0~1;

[0282] Right front wheel:

[0283] Left rear wheel: Wherein, B r Rear axle wheelbase;

[0284] Right rear wheel:

[0285] The actual torque of the wheel end is,

[0286] Left front wheel: T fr13 = T fr + ζ 13 ΔT fr13 ;

[0287] Right front wheel: T rl13 = T rl + ζ 13 ΔT rl13 ;

[0288] Left rear wheel: T rr13 = T rr + ζ 13 ΔT rr13 ;

[0289] Right rear wheel: T 13 = T f21 + ζ f22 ΔT fbou21 ;

[0290] Wherein, ζ fbou22 Additional torque adjustment coefficient, value range 0.75~1.5.

[0291] B: If two of the vehicle's yaw rate, mass center side slip angle, front axle side slip angle, rear axle side slip angle, lateral acceleration exceed the critical stability boundary in the prediction time domain, the item exceeding the critical stability boundary is recorded as X f21 , X f21 , and the corresponding critical stability boundary is recorded as X f21 , X f21 ;

[0292] Xf21 = [X f21 (1) X f21 (2) … X f21 (N f21 )] ;

[0293] X f22 = [X f22 (1) X f22 (2) … X f22 (N f22 )] ;

[0294] X fbou21 = [X fbou21 (1) X fbou21 (2) … X fbou21 (N f21 )] ;

[0295] X fbou22 = [X fbou22 (1) X fbou22 (2) … X fbou22 (N f22 )] ;

[0296] N f21 , N f22 is the number of each item beyond the stable boundary;

[0297] The error of the part beyond the critical stable boundary and the corresponding critical stable boundary is ΔX f21 , ΔX f22 ,

[0298] | ΔX f21 | = [ | X f21 (1) - X fbou21 (1) | | X f21 (2) - X fbou21 (2) | … | X f21 (N f21 ) - X fbou21 (N f21 ) |] ;

[0299] | ΔX f22 | = [ | X f22 (1) - X fbou22 (1) | | X f22 (2) - X fbou22 (2) | … | X f22 (N f22 ) - X fbou22 (N f22 ) |] ;

[0300] Unstable coefficient calculation:

[0301] The first unstable coefficient is:

[0302]

[0303] The second instability coefficient is:

[0304]

[0305] The total instability coefficient is:

[0306] η f2 = ε f2 η f21 + (1- ε f2 ) η f22 ;

[0307] wherein, ε f2 is a weight coefficient, and the value range is 0-1;

[0308] If , it indicates that the vehicle is in a weak instability (first stability level) in the prediction time domain, and the additional front wheel steering angle is used for adjustment,

[0309]

[0310] wherein, is the proportional gain of Δδ ff21 , and the value range is 0-10, is the integral gain of Δδ ff21 , and the value range is 0-100; Δδ ff21 is the additional front wheel steering angle; the actual values of the above coefficients need to be determined through real vehicle experiment test;

[0311] The actual output front wheel steering angle is:

[0312]

[0313] wherein, is a gain coefficient, is the stability coefficient threshold corresponding to the first stability level, and the value range is 0.2-0.3;

[0314] If , it indicates that the vehicle is in a moderate instability state (second stability level) in the prediction time domain, and the dynamic adjustment is performed through the additional front wheel steering angle and four-wheel torque, and the calculation of the additional front wheel steering angle and the additional yaw moment is:

[0315]

[0316]

[0317] wherein, is the proportional gain of Δδ ff22proportional gain of Δδ, value range 0~10, is the integral gain of Δδ, value range 0~100; ff22 ff22 proportional gain of ΔM, value range 0~10, is the integral gain of ΔM, value range 0~100; ff22 ff22 is the additional front wheel steering angle, ΔM ff22 is the additional yaw moment; the actual value of the above coefficients needs to be determined through real vehicle test;

[0318] The actual output front wheel steering angle is:

[0319]

[0320] wherein, is the gain coefficient, is the stability coefficient threshold corresponding to the second stability level, value range 0.5~0.65;

[0321] The additional yaw moment is converted to each wheel end torque:

[0322] Left front wheel:

[0323] wherein, is the front axle additional yaw moment proportionality coefficient, value range 0~1;

[0324] Right front wheel:

[0325] Left rear wheel:

[0326] Right rear wheel:

[0327] The actual torque of the wheel end is:

[0328] Left front wheel: T fl22 = T fl + ζ 22 ΔT fl22 ;

[0329] Right front wheel: T fr22 = T fr + ζ 22 ΔT fr22 ;

[0330] Left rear wheel: T rl22 = T rl + ζ 22 ΔT rl22 ; ​​​

[0331] Right rear wheel: T rr22 = T rr + ζ 22 ΔT rr22 ;

[0332] wherein, ζ 22 is an additional torque adjustment coefficient, with a value range of 0.2-0.6;

[0333] If indicates that the vehicle is in a strong unstable state (third stability level) in the prediction time domain, dynamic adjustment is performed through four-wheel torque, and the additional yaw moment is calculated as:

[0334]

[0335] wherein, is the proportional gain of ΔM ff23 , with a value range of 0-10, is the integral gain of ΔM ff23 , with a value range of 0-100; ΔM ff23 is the additional yaw moment; the actual values of the above coefficients need to be determined through real vehicle experiment tests;

[0336] Convert the additional yaw moment to the torque at each wheel end:

[0337] Left front wheel:

[0338] wherein, is the additional yaw moment proportion coefficient of the front axle, with a value range of 0-1;

[0339] Right front wheel:

[0340] Left rear wheel:

[0341] Right rear wheel:

[0342] The actual torque at the wheel end is:

[0343] Left front wheel: T fl23 = T fl + ζ 23 ΔT fl23 ;

[0344] Right front wheel: T fr23 = T fr + ζ 23 ΔT fr23 ;

[0345] Left rear wheel: T rl23 = T rl + ζ 23ΔT rl23 ;

[0346] Right rear wheel: T rr23 = T rr + ζ 23 ΔT rr23 ;

[0347] wherein ζ 23 is an additional torque adjustment coefficient, with a value range of 0.75-1.5.

[0348] C: If three or more of the vehicle's yaw rate, mass center side slip angle, front axle side slip angle, rear axle side slip angle, lateral acceleration in the prediction time domain exceed the critical stability boundary, the items exceeding the critical stability boundary are recorded as X f3n , and n has a value range of 1-n bou , wherein n bou is the number of items exceeding the critical stability boundary, and the corresponding critical stability boundary is recorded as X fbou3n ;

[0349] X f3n = [X f3n (1) X f3n (2)…X f3n (N f3n )];

[0350] X fbou3n = [X fbou3n (1) X fbou3n (2)…X fbou3n (N f3n )];

[0351] N f3n is the number of items exceeding the stability boundary, and n has a value range of 1-n bou ;

[0352] The error of the part exceeding the critical stability boundary and the corresponding critical stability boundary is recorded as ΔX f3n ,

[0353] |ΔX f3n | = [X f3n (1) - X fbou3n (1) | |X f3n (2) - X fbou3n (2) |…|X f3n (N f3n ) - X fbou3n (N f3n ) |];

[0354] Each instability coefficient is:

[0355]

[0356] The total instability coefficient is:

[0357]

[0358] Wherein, it needs to meet:

[0359] If It indicates that the vehicle is in a weak unstable state (first stability level) in the prediction time domain, and can be adjusted by applying an additional front wheel steering angle, which is:

[0360]

[0361] Wherein, is the proportional gain of Δδ ff31 , the value range is 0-10, is the integral gain of Δδ ff31 , the value range is 0-100, Δδ ff31 is the additional front wheel steering angle; the actual value of the above coefficients needs to be determined through real vehicle experiment test;

[0362] The actual output front wheel steering angle is:

[0363]

[0364] Wherein, is the gain coefficient, the value range is 0.05-0.2;

[0365] If Because there are many terms exceeding the critical stability boundary and the instability coefficient is large (second stability level), the vehicle is in an extremely unstable state in the prediction time domain, and needs to be dynamically adjusted and controlled by four-wheel torque, the additional yaw moment is:

[0366]

[0367] Wherein, is the proportional gain of ΔM ff32 , the value range is 0-10, is the integral gain of ΔM ff32 , the value range is 0-100, ΔM ff32 is the additional yaw moment; the actual value of the above coefficients needs to be determined through real vehicle experiment test;

[0368] Convert the additional yaw moment to the force torque of each wheel,

[0369] Left front wheel:

[0370] Wherein, The additional yaw moment ratio coefficient of the front axle is 0-1;

[0371] Right front wheel:

[0372] Left rear wheel:

[0373] Right rear wheel:

[0374] The actual moment of the wheel end is,

[0375] Left front wheel: T fl32 = T fl + ζ 32 ΔT fl32 ;

[0376] Right front wheel: T fr32 = T fr + ζ 32 ΔT fr32 ;

[0377] Left rear wheel: T rl32 = T rl + ζ 32 ΔT rl32 ;

[0378] Right rear wheel: T rr32 = T rr + ζ 32 ΔT rr32 ;

[0379] Wherein, ζ 32 is an additional torque adjustment coefficient, 0.75-1.25.

[0380] Five, actuator control

[0381] After the decision of step four, if the front wheel angle needs to be controlled, the calculated front wheel angle is multiplied by the transmission ratio i sw and applied to the steering wheel.

[0382] If the four-wheel torque needs to be adjusted, the four-wheel motor tracks the motor end torque obtained by dividing the wheel end torque calculated in step four by the transmission ratio.

[0383] Although the embodiments of the present application have been disclosed as above, it is not limited to the application listed in the specification and the embodiments, and it can be applied to various fields suitable for the present application, and other modifications can be easily realized by those skilled in the art, and therefore the present application is not limited to specific details and the figures shown and described herein, without departing from the general concept defined by the claims and the equivalent scope.

Claims

1. A method for intelligent vehicle motion prediction control based on a prediction model, characterized in that, The method comprises the following steps: The longitudinal vehicle speed signal, the lateral vehicle speed signal, the yaw rate signal, the longitudinal displacement signal, the lateral displacement signal and the heading angle signal collected in a plurality of sampling periods before the current time are preprocessed to obtain the longitudinal vehicle speed standard value, the lateral vehicle speed standard value, the yaw rate standard value, the longitudinal displacement standard value, the lateral displacement standard value and the heading angle standard value; The system state quantity is The control quantity is U con = δ f According to the longitudinal vehicle speed standard value, the lateral vehicle speed standard value, the yaw angle speed standard value, the longitudinal displacement standard value, the lateral displacement standard value and the heading angle standard value, the vehicle state is predicted to obtain the yaw angular velocity prediction value, the mass side slip angle prediction value, the front axle side slip angle prediction value, the rear axle side slip angle prediction value and the lateral acceleration prediction value at each time in the prediction time domain. wherein, v x is the longitudinal vehicle speed, v y is the lateral vehicle speed, ω is the yaw rate, X is the longitudinal displacement, Y is the lateral displacement, is the heading angle, δ f is the front wheel steering angle; The yaw rate prediction value, the mass center side slip angle prediction value, the front axle side slip angle prediction value, the rear axle side slip angle prediction value and the lateral acceleration prediction value at each time in the prediction time domain are compared with the yaw rate critical stability boundary, the mass center side slip angle critical stability boundary, the front axle side slip angle critical stability boundary, the rear axle side slip angle critical stability boundary and the lateral acceleration critical stability boundary at each time; If one or more of the yaw rate prediction value, the mass center side slip angle prediction value, the front axle side slip angle prediction value, the rear axle side slip angle prediction value and the lateral acceleration prediction value is greater than the corresponding critical stability boundary, the stability level of the vehicle in the prediction time domain is determined, and the steering and / or four-wheel torque of the vehicle at the next time is adjusted according to the stability level; When one of the yaw rate prediction value, the mass center side slip angle prediction value, the front axle side slip angle prediction value, the rear axle side slip angle prediction value and the lateral acceleration prediction value is greater than the corresponding critical stability boundary: The predicted value at each time point is compared with the critical stability boundary, and the value corresponding to the time point at which the predicted value is greater than the critical stability boundary is recorded as X f1 The critical stability boundary at the corresponding time point is recorded as X fbou1 Then: X f1 = [X f1 (1) X f1 (2)... X f1 (N f1 )]; X fbou1 = [X fbou1 (1) X fbou1 (2)... X fbou1 (N f1 )]; The error of the prediction value of the corresponding moment and the critical stability boundary is denoted as ΔX f1 Then: | ΔX f1 | = [ | X f1 (1) - X fbou1 (1) | | X f1 (2) - X fbou1 (2) |... | X f1 (N f1 ) - X fbou1 (N f1 ) | ] ; If then the vehicle is judged to be in the first stability class in the prediction horizon and the vehicle is subjected to a steering intervention: The front wheel turning angle at the next time is controlled as: wherein, κ fb11 is a boundary coefficient; is a gain coefficient; is a proportional gain of Δδ ff11 , Δδ is an integral gain of Δδ ff11 , Δδ ff11 is an additional front wheel steering angle; If then determine that the vehicle is in the second stability level in the prediction horizon, and apply steering and four-wheel torque regulation to the vehicle: If then the vehicle is judged to be in the third stability level in the prediction time horizon, and four-wheel torque regulation is performed on the vehicle. 2.The intelligent vehicle motion prediction control method based on prediction model according to claim 1, wherein, The vehicle state is predicted by a discrete system prediction state matrix; The discrete system prediction state matrix is: X sta (k+1) = (A pre T ti + I) X sta (k) + (B pre T ti ) U con (k); where T ti is the period of the discrete system and I is the identity matrix. In the formula, C αf , C αr are the front and rear wheel cornering stiffness, a and b are the distances from the center of mass to the front and rear axles, m is the mass of the vehicle, and I z is the moment of inertia. 3.The intelligent vehicle motion prediction control method based on prediction model according to claim 2, characterized in that, determining that the vehicle is in the second stability level in the prediction time domain, controlling the front wheel steering angle at the next time point as: wherein κ fb12 is a boundary coefficient, is a gain coefficient, is a proportional gain of Δδ ff12 is an integral gain of Δδ ff12 ;​ The four-wheel wheel end torque at the next time is controlled as: Left front wheel: T fl12 = T fl + ζ 12 ΔT fl12 ; Right front wheel: T fr12 = T fr + ζ 12 ΔT fr12 ; Left rear wheel: T rl12 = T rl + ζ 12 ΔT rl12 ; Right rear wheel: T rr12 = T rr + ζ 12 ΔT rr12 ; wherein ζ 12 is an additional torque adjustment coefficient; is a front axle additional yaw moment proportion coefficient, B f is a front axle wheelbase, B r is a rear axle wheelbase; is a proportional gain of is an integral gain of .​ 4.The intelligent vehicle motion prediction control method based on prediction model according to claim 3, wherein, When the vehicle is in the third stability level in the prediction time domain, the four-wheel wheel end torque at the next time is controlled as: Left front wheel: T fl13 = T fl + ζ 13 ΔT fl13 ; Right front wheel: T fr13 = T fr + ζ 13 ΔT fr13 ; Left rear wheel: T rl13 = T rl + ζ 13 ΔT rl13 ; Right rear wheel: T rr13 = T rr + ζ 13 ΔT rr13 ; wherein ζ 13 is an additional torque adjustment coefficient; is a front axle additional yaw moment proportion coefficient, B f is a front axle track width; is a proportional gain of AM ff13 , and is an integral gain of AM ff13 . 5.The intelligent vehicle motion prediction control method based on prediction model according to claim 1 or 2, characterized in that, When two of the yaw rate prediction value, the mass center side slip angle prediction value, the front axle side slip angle prediction value, the rear axle side slip angle prediction value and the lateral acceleration prediction value are greater than the corresponding critical stability boundary: The two items of predicted values at each time are compared with the critical stability boundary, and the value corresponding to the time when the predicted value is greater than the critical stability boundary is recorded as X f21 , X f22 , the corresponding critical stability boundary is recorded as X fbou21 , X fbou22 , then: X f21 = [X f21 (1) X f21 (2)…X f21 (N f21 )]; X f22 = [X f22 (1) X f22 (2)…X f22 (N f22 )] ; X fbou21 = [X fbou21 (1) X fbou21 (2)…X fbou21 (N f21 )]; X fbou22 = [X fbou22 (1) X fbou22 (2)…X fbou22 (N f22 )] ; where N f21 , N f22 are the number of each item that exceeds the stability boundary; The error of each predicted value corresponding to the corresponding moment and the corresponding critical stability boundary is denoted as ΔX f21 , ΔX f22 ; | ΔX f21 | = [ | X f21 (1) - X fbou21 (1) | | X f21 (2) - X fbou21 (2) |... | X f21 (N f21 ) - X fbou21 (N f21 ) | ]; | ΔX f22 | = [ | X f22 (1) - X fbou22 (1) | | X f22 (2) - X fbou22 (2) |... | X f22 (N f22 ) - X fbou22 (N f22 ) | ] ; If then it is determined that the vehicle is in a first stability class in the prediction horizon, and the vehicle is subjected to a steering regulation: The front wheel turning angle at the next time is controlled as: wherein η f2 = ε f2 η f21 + (1- ε f2 )η f22 , is a stability coefficient threshold corresponding to the first stability level; is a gain coefficient; is a proportional gain of Δδ ff21 , is an integral gain of Δδ ff21 ; If then determine that the vehicle is in a second stability level in the prediction horizon, and apply steering and four-wheel torque regulation to the vehicle: is a stability coefficient threshold value corresponding to the second stability level. If then the vehicle is judged to be in the third stability level in the prediction time horizon, and four-wheel torque regulation is performed on the vehicle. 6.The intelligent vehicle motion prediction control method based on prediction model according to claim 5, wherein, determining that the vehicle is in the second stability level in the prediction time domain, controlling the front wheel steering angle at the next time point as: in, is the gain coefficient, is Δδ ff22 The proportional gain, is Δδ ff22 The integral gain of The four-wheel wheel end torque at the next time is controlled as: Left front wheel: T fl22 = T fl + ζ 22 ΔT fl22 ; Right front wheel: T fr22 = T fr + ζ 22 ΔT fr22 ; Left rear wheel: T rl22 = T rl + ζ 22 ΔT rl22 ; Right rear wheel: T rr22 = T rr + ζ 22 ΔT rr22 ; wherein ζ 22 is an additional torque adjustment coefficient; is a front axle additional yaw moment proportion coefficient, B f is a front axle wheel track; is a proportional gain of ΔM ff22 is an integral gain of ΔM ff22 B r is a rear axle wheel track.​ 7.The intelligent vehicle motion prediction control method based on prediction model according to claim 6, characterized in that, When the vehicle is in the third stability level in the prediction time domain, the four-wheel wheel end torque at the next time is controlled as: Left front wheel: T fl23 = T fl + ζ 23 ΔT fl23 ; Right front wheel: T fr23 = T fr + ζ 23 ΔT fr23 ; Left rear wheel: T rl23 = T rl + ζ 23 ΔT rl23 ; Right rear wheel: T rr23 = T rr + ζ 23 ΔT rr23 ; wherein ζ 23 is an additional torque adjustment coefficient; is a front axle additional yaw moment proportion coefficient; is a proportional gain of ΔM ff23 is an integral gain of ΔM ff23 .​ 8.The intelligent vehicle motion prediction control method based on prediction model according to claim 1 or 2, characterized in that, When three or more of the yaw rate prediction value, the mass center side slip angle prediction value, the front axle side slip angle prediction value, the rear axle side slip angle prediction value and the lateral acceleration prediction value are greater than the corresponding critical stability boundary: The predicted value of each item at each time greater than the critical stability boundary is compared with the critical stability boundary, and the value corresponding to the time when the predicted value is greater than the critical stability boundary is recorded as X f3n The corresponding critical stability boundary is recorded as X fbou3n Then: X f3n = [X f3n (1) X f3n (2) … X f3n (N f3n )]; X fbou3n = [X fbou3n (1) X fbou3n (2)…X fbou3n (N f3n )]; wherein N f3n is the number of each item exceeding the stability boundary, and n has a value in the range of 1 to n bou ; The error of each predicted value corresponding to the corresponding moment and the corresponding critical stability boundary is denoted as ΔX f3n ; | ΔX f3n | = [ | X f3n (1) - X fbou3n (1) | | X f3n (2) - X fbou3n (2) |... | X f3n (N f3n ) - X fbou3n (N f3n ) | ]; If then it is determined that the vehicle is in a first stability class in the prediction horizon, and the vehicle is subjected to a steering regulation: The front wheel turning angle at the next time is controlled as: in, is the stability coefficient threshold corresponding to the first stability level; is the gain coefficient; is Δδ ff31 The proportional gain, is Δδ ff31 The integral gain of If then the vehicle is judged to be in the second stability level in the prediction time domain, and the four-wheel wheel end torque of the next time is controlled as Left front wheel: T fl32 = T fl + ζ 32 ΔT fl32 ; Right front wheel: T fr32 = T fr + ζ 32 ΔT fr32 ; Left rear wheel: T rl32 = T rl + ζ 32 ΔT rl32 ; Right rear wheel: T rr32 = T rr + ζ 32 ΔT rr32 ; Among them, 32 is the additional torque adjustment coefficient; Add the yaw moment ratio coefficient to the front axle; ΔM ff32 The proportional gain, ΔM ff32 The integral gain of .

9. A smart vehicle motion predictive control system based on a predictive model, which is controlled by the smart vehicle motion predictive control method based on a predictive model according to any one of claims 1-8, characterized in that, The method comprises the following steps: The data acquisition module comprises: A steering wheel angle sensor for collecting a steering wheel angle signal; An IMU sensor for collecting the longitudinal acceleration, lateral acceleration, yaw rate and heading angle of the vehicle; A GPS sensor for collecting the longitudinal vehicle speed, lateral vehicle speed, longitudinal displacement and lateral displacement of the vehicle; A wheel speed sensor for collecting the wheel speed of the four wheels; A central control module for predicting the vehicle state according to the data collected by the data acquisition module and issuing control instructions according to the prediction result; An execution module for executing the control instructions; The execution module comprises a steering wheel, a left front wheel motor, a right front wheel motor, a left rear wheel motor and a right rear wheel motor.

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