Cooperative network connection vehicle control method considering model uncertainty influence

Abstract

The invention provides a cooperative network connection vehicle control method considering the influence of model uncertainty. According to the invention, longitudinal vehicle tracking and transverse lane keeping control targets are realized by using a sliding mode control method. A longitudinal controller is designed in combination with an RBF neural network and a sliding mode control theory, and uncertain items in a vehicle model are processed by using approximation characteristics of the RBF neural network. By designing an auxiliary sliding mode surface, the chattering phenomenon existing in the transverse lane keeping controller is suppressed. Then, the internal stability and queue stability of the proposed controller are analyzed. Finally, the effectiveness of the algorithm is verified through a SIMULINK simulation experiment and an intelligent network connection vehicle platform experiment.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle control, and in particular to a collaborative networked vehicle control method taking into account the influence of model uncertainty. Background Art

[0002] The vehicle dynamics model is the basis for establishing the CACC system. However, due to the influence of environmental conditions and working conditions, the uncertainty of the vehicle model may cause the instability of the CACC system, affecting the safety and comfort of driving.

[0003] References:

[0004] Literature

[65] : A.Bayuwindra, L.Aakre, J.Ploeg and H.Nijmeijer, "Combined lateral and longitudinal CACC for a unicycle-type platoon," 2016IEEE Intelligent Vehicles Symposium(IV), Gothenburg, Sweden, 2016, pp.527-532, doi:10.1109 / IVS.2016.7535437. Summary of the invention

[0005] According to the technical problems mentioned in the above background technology, a collaborative connected vehicle control method considering the influence of model uncertainty is provided. The present invention considers using a sliding mode control method to achieve longitudinal vehicle tracking and lateral lane keeping control objectives. The longitudinal controller is designed by combining the RBF neural network and the sliding mode control theory, and the approximation characteristics of the RBF neural network are used to deal with the uncertainties in the vehicle model. By designing an auxiliary sliding surface, the jitter phenomenon existing in the lateral lane keeping controller is suppressed. Then, the internal stability and queue stability of the proposed controller are analyzed. Finally, the effectiveness of the proposed algorithm is verified through SIMULINK simulation experiments and intelligent connected vehicle platform experiments.

[0006] The technical means adopted by the present invention are as follows:

[0007] A cooperative connected vehicle control method considering the influence of model uncertainty comprises the following steps:

[0008] Step 1: Consider using the sliding mode control method to set the longitudinal vehicle tracking and lateral lane keeping control objectives;

[0009] Step 2: Design the longitudinal controller based on sliding mode control theory;

[0010] Step 3: Use the approximation characteristics of the RBF neural network to handle the uncertainties in the vehicle model;

[0011] Step 4: Introduce an auxiliary sliding surface to suppress the chattering phenomenon existing in the lateral lane keeping controller;

[0012] Step 5: Analyze the internal stability and queue stability of the controller and further optimize the controller;

[0013] Step 6: Verify the feasibility of the method through SIMULINK simulation experiments and intelligent connected vehicle platform experiments.

[0014] Further, in step 1, the control objectives include: internal stability and queue stability;

[0015] The internal stability is defined as: the vehicle tracking error e x,i and e y,i , satisfies the following formula, that is, the tracking error converges to zero;

[0016]

[0017] Wherein, t represents time;

[0018] The stability of the queue means that when the leading vehicle is disturbed, the tracking error of the vehicle will not be amplified and transmitted along the queue, that is, for the state quantity x i (t), disturbance ω i ,exist Function β, Function σ and constants k1, k2∈R + , so that the following holds:

[0019]

[0020] Furthermore, from the error expression, formula (4), we can know that:

[0021]

[0022] In order to achieve speed tracking and distance maintenance between vehicles, the longitudinal vehicle tracking controller is obtained by combining the error expression:

[0023]

[0024] Among them, λ i >0; then s 1,i Taking the derivative with respect to time, we get

[0025]

[0026] in,

[0027]

[0028] Because F r,i Contains unknown dynamic parameters and external disturbances, so f x,i Assume that f is an unknown nonlinear function. x,i smooth and bounded;

[0029] Then the longitudinal controller is:

[0030]

[0031] Among them, η i >0,k 1,i >0

[0032] Furthermore, in step 3, the RBF neural network is used to approximate f x,i , including the following steps:

[0033] Step 31: Set x i is the network input, and Function f x,i Expressed as Then the RBF network output is:

[0034]

[0035] in, Represents the ideal weight matrix W i * estimates;

[0036] Step 32: Substitute the longitudinal controller into:

[0037]

[0038]

[0039] in, Indicates f x,i (·) estimation error; Represents the weight error of the network;

[0040] Step 33: Define the Lyapunov function as:

[0041]

[0042] Among them, γ 1,i >0;

[0043] Step 34: Derivative with respect to time, substitute equation (12) to obtain:

[0044]

[0045] The adaptive law is set as:

[0046]

[0047] Step 35: Substitute equation (15) into equation (14) to obtain:

[0048]

[0049] Since the network approximation error ε i is a real number approaching zero, and η i >0,k 1,i >0, then It can be obtained that the sliding mode function s 1,i It gradually converges to zero, and the longitudinal tracking error e x, i also converges to zero.

[0050] Furthermore, in step 4, the sliding mode function is designed as:

[0051]

[0052] Among them, c i =[c 1,i c 21,i c 22,i ], are the controller parameters to be designed;

[0053] Derivative of formula (17) with respect to time yields:

[0054]

[0055]

[0056] in,

[0057]

[0058] Then the auxiliary sliding surface is:

[0059]

[0060] Also set:

[0061]

[0062] Define the Lyapunov function as:

[0063]

[0064] Taking the derivative of equation (22) with respect to time, we get:

[0065]

[0066] in,

[0067]

[0068] make:

[0069]

[0070] where ρ i >0, and put it into formula (23) and simplify it to get:

[0071]

[0072] That is, when the control law is formula (27),

[0073]

[0074] Sliding surface σ i is converged to zero, then the sliding surface s 2,i is converged to zero, the lateral tracking error e y,i It converges to zero.

[0075] Furthermore, in step 5, if there is a type Function β, a class Function σ, and constants k1, k2∈R + , so that for any initial state x i (0) and the external disturbance ω i (t), both satisfy equations (28) and (29):

[0076]

[0077] Let x i (t) = [e x,i ,e y,i ] T ,ω i =f x,i , from which we get:

[0078]

[0079] ||ω i (t)|| ∞ =max|ω i |;(31)

[0080] Initial state quantity|x i (0)| is bounded, and the function f x,i is a smooth bounded function; therefore, there exist k1, k2 that satisfy the condition of equation (28);

[0081] Tracking Error x,i ,e y,i converge to zero, then |x i (t)|converges to a very small neighborhood of the origin; Indicates that within a given time interval [0, t], ω i L ∞ norm, that is It is known that there is a class Function β, a class Function σ, so that the following holds:

[0082]

[0083] If the condition of formula (32) is satisfied, the stability requirement of the disturbance queue is met.

[0084] Compared with the prior art, the present invention has the following advantages:

[0085] 1. The adaptive RBF sliding mode control technology provided by the present invention realizes the estimation and compensation of unknown model parameters of networked vehicles by combining with the RBF neural network.

[0086] 2. Solved the lateral and longitudinal control problems faced by the collaborative control of connected vehicles under curved road conditions.

[0087] 3. The chattering phenomenon existing in the lateral lane keeping controller is suppressed.

[0088] In summary, the application of the technical solution of the present invention solves the lateral and longitudinal control problems of the cooperative control of connected vehicles under curved road conditions. Therefore, the technical solution of the present invention solves the need for an accurate and linear vehicle control model in the prior art, and solves the jitter phenomenon faced by the prior art in the lane keeping controller. The assumption of the prior art that the vehicle is traveling on a horizontal road is removed. Based on the above reasons, the present invention can be widely promoted in the fields of connected vehicle control and the like. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0090] Figure 1 This is the vehicle dynamics and trajectory tracking model of the present invention.

[0091] Figure 2 This is the cooperative networked vehicle control system of the present invention.

[0092] Figure 3 It is a schematic diagram of the vehicle layout of the present invention.

[0093] Figure 4 The horizontal controller u of the present invention 3,1 .

[0094] Figure 5 The horizontal controller u of the present invention 2,1 .

[0095] Figure 6 is the lateral controller u obtained by traditional sliding mode control 2,1 .

[0096] Figure 7 is the longitudinal tracking error of the vehicle.

[0097] Figure 8 is the lateral tracking error of the vehicle.

[0098] Fig. 9 is the speed error of the vehicle.

[0099] Fig.10 is the trajectory of the vehicle.

[0100] Fig.11 is the lateral tracking error of the vehicle when applying the controller in

[65] .

[0101] Fig.12 is the longitudinal tracking error of the vehicle when the controller in

[65] is applied.

[0102] Fig.13 is the vehicle speed error when applying the controller in

[65] .

[0103] Fig.14 is the trajectory of the vehicle when the controller in

[65] is applied.

[0104] Fig.15 It is a schematic diagram of the overall process of the present invention. DETAILED DESCRIPTION

[0105] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0106] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0107] Consider a CACC system consisting of n+1 vehicles driving on a curved road (including a leading vehicle numbered 0 and n following vehicles numbered 1...n). All vehicles are affected by environmental resistance such as road slope and gusts when driving. In this application, the vehicles in the system use PF communication topology and exchange information through wireless communication.

[0108] Assume that the front wheel steering angle is small enough, that is, sin(δ i )≈δ i , cos(δ i )≈1, then according to Figure 1 As shown, the vehicle dynamics model of the following vehicle i is established as:

[0109]

[0110] Among them, v x,i and v y,i denote the longitudinal and lateral velocities of vehicle i in the Cartesian rectangular coordinate system, v ω represents wind speed, ρ i is the road slope angle, ω i represents the vehicle angular velocity, F D,i is the driving / braking force of the vehicle, F F,i and F R,i are the lateral forces on the front and rear wheels of the vehicle, F r,i includes the unknown nonlinear part and external disturbance, δ i is the steering angle, M i and I i is the mass and inertia of the vehicle, C a,i is the air resistance coefficient, f i is the rolling resistance coefficient, l F,i and l R,i is the distance between the front and rear axles and the center of mass of the vehicle, and g is the acceleration due to gravity.

[0111] In order to facilitate the processing of interference, it is assumed that both wind speed and road slope angle satisfy the energy bounded condition, that is,

[0112] Assuming the sideslip angle of the vehicle is small enough, the following equation holds true:

[0113]

[0114] Where: k F,i and k R,i are the cornering stiffness of the front and rear tires, α F,i and α R,i are the side slip angles of the front and rear tires respectively. Substituting equation (4.2) into equation (4.1) yields the nonlinear dynamic model for cooperative connected vehicle control:

[0115]

[0116] Define v i is the velocity of vehicle i in the Cartesian coordinate system, satisfying State quantity x i Defined as x i =[v x,i ,v y,i ,ω i ] T , and the control input is expressed as:

[0117]

[0118] Where f 1,i , f 2,i The specific form is:

[0119]

[0120] In order to facilitate the design of the controller, the control input u is introduced 1,i and u 2,i Decompose the vehicle dynamics model and make u 1,i and u 2,i Acts on longitudinal control and lateral control respectively.

[0121] The controller is designed to be divided into two parts: (1) longitudinal vehicle cooperative control, which ensures that the desired vehicle spacing and consistent speed are maintained between vehicles; and (2) lateral lane keeping control, which ensures that the vehicles in the system travel on the center line of the road to avoid deviation.

[0122] The structure of the cooperative connected vehicle control system is as follows: Figure 2 As shown in the figure, all vehicles in the system can obtain their own status information and receive information from the vehicle in front. The vehicle in front transmits the curve distance l through wireless communication. i-1 With speed vi-1 Passed to vehicle i, used to design the longitudinal tracking controller u 1,i ; At the same time, vehicle i obtains the expected heading angle of the preset road in real time With its own speed information v x,i , v y,i and ω i , used to design the lateral lane keeping controller u 2,i , and then the required driving / braking force F is calculated by equation (36) D,i With the steering angle δ i , and finally achieve vehicle control.

[0123] The vehicles in the CACC system adopt the PF communication topology structure, and the following vehicle i-1 transmits speed information v to vehicle i. i-1 and the traveled curve distance information l i-1 . Definition x,i and e v,i are the distance error and speed error between the following vehicle i and the preceding vehicle, and the error expression is established:

[0124]

[0125] Among them, l i and l i-1 They represent the distances traveled by the following vehicles i and i-1 along the curved road; v i represents the speed of the following vehicle i; l d is the desired inter-vehicle distance, and l d is a fixed value; R i Indicates the curvature radius of the road; p x,i Represents the horizontal coordinate of vehicle i in the Cartesian coordinate system.

[0126] In order to prevent the vehicle from deviating from the lane, a lateral lane keeping model is established for the following vehicle i:

[0127] like Figure 1 As shown, define e 1,i and e 2,i Respectively represent the lateral distance error and heading error between vehicle i and the center line of the road

[0128]

[0129] in, is the desired vehicle heading angle, and the lateral tracking error e is defined y,i =[e 1,i e 2,i ] T Among them, v i With v x,i and v y,i Different meanings, vi represents the velocity of the center of mass of vehicle i, v x,i and v y,i Represent the longitudinal speed and lateral speed of vehicle i respectively, and satisfy l i The differential of is equivalent to v i , p x,i .

[0130] Preferably, a cooperative connected vehicle control method considering the influence of model uncertainty comprises the following steps:

[0131] Step 1: Consider using the sliding mode control method to set the longitudinal vehicle tracking and lateral lane keeping control objectives;

[0132] Step 2: Design the longitudinal controller based on sliding mode control theory;

[0133] Step 3: Use the approximation characteristics of the RBF neural network to handle the uncertainties in the vehicle model;

[0134] Step 4: Introduce an auxiliary sliding surface to suppress the chattering phenomenon existing in the lateral lane keeping controller;

[0135] Step 5: Analyze the internal stability and queue stability of the controller and further optimize the controller;

[0136] Step 6: Verify the feasibility of the method through SIMULINK simulation experiments and intelligent connected vehicle platform experiments.

[0137] As a preferred implementation, in the present application, in step 1, the control objectives include: internal stability and queue stability;

[0138] The internal stability is defined as: the vehicle tracking error e x,i and e y,i , satisfies the following formula, that is, the tracking error converges to zero;

[0139]

[0140] Wherein, t represents time;

[0141] The stability of the queue means that when the leading vehicle is disturbed, the tracking error of the vehicle will not be amplified and transmitted along the queue, that is, for the state quantity x i (t), disturbance ω i ,exist Function β, Function σ and constants k1, k2∈R + , so that the following holds:

[0142]

[0143] As a preferred implementation, it can be seen from the error expression, that is, formula (4):

[0144]

[0145] In order to achieve speed tracking and distance maintenance between vehicles, the longitudinal vehicle tracking controller is obtained by combining the error expression:

[0146]

[0147] Among them, λ i >0; then s 1,i Taking the derivative with respect to time, we get

[0148]

[0149] in,

[0150]

[0151] Because F r,i Contains unknown dynamic parameters and external disturbances, so f x,i Assume that f is an unknown nonlinear function. x,i smooth and bounded;

[0152] Then the longitudinal controller is:

[0153]

[0154] Among them, η i >0,k 1,i >0

[0155] As a preferred implementation, in step 3, RBF neural network is used to approximate f x,i , including the following steps:

[0156] Step 31: Set x i is the network input, and Function f x,i Expressed as Then the RBF network output is:

[0157]

[0158] in, Represents the ideal weight matrix W i * estimates;

[0159] Step 32: Substitute the longitudinal controller into:

[0160]

[0161]

[0162] in, Indicates f x,i (·) estimation error; Represents the weight error of the network;

[0163] Step 33: Define the Lyapunov function as:

[0164]

[0165] Among them, γ 1,i >0;

[0166] Step 34: Derivative with respect to time, substitute equation (12) to obtain:

[0167]

[0168] The adaptive law is set as:

[0169]

[0170] Step 35: Substitute equation (15) into equation (14) to obtain:

[0171]

[0172] Since the network approximation error ε i is a real number approaching zero, and η i >0,k 1,i >0, then It can be obtained that the sliding mode function s 1,i It gradually converges to zero, and the longitudinal tracking error e x,i It also converges to zero.

[0173] In this application, in step 4, the sliding mode function is designed as:

[0174]

[0175] Among them, c i =[c 1,i c 21,i c 22,i ], are the controller parameters to be designed;

[0176] Derivative of formula (17) with respect to time yields:

[0177]

[0178] in,

[0179]

[0180] Then the auxiliary sliding surface is:

[0181]

[0182] Also set:

[0183]

[0184] Define the Lyapunov function as:

[0185]

[0186] Taking the derivative of equation (22) with respect to time, we get:

[0187]

[0188] in,

[0189]

[0190] make:

[0191]

[0192] where ρ i >0, and put it into formula (23) and simplify it to get:

[0193]

[0194] That is, when the control law is formula (27),

[0195]

[0196] Sliding surface σ i is converged to zero, then the sliding surface s 2,i is converged to zero, the lateral tracking error e y,i It converges to zero.

[0197] Preferably, in step 5, if there is a type Function β, a class Function σ, and constants k1, k2∈R + , so that for any initial state x i (0) and the external disturbance ω i (t), both satisfy equations (28) and (29):

[0198]

[0199] make From this we get:

[0200]

[0201] ||ω i (t)|| ∞ =max|ω i |;(31)

[0202] Initial state quantity|x i (0)| is bounded, and the function f x , i is a smooth bounded function; therefore, there exist k1, k2 that satisfy the condition of equation (28);

[0203] Tracking Error x,i ,e y,i converge to zero, then |x i (t)|converges to a very small neighborhood of the origin; Indicates that within a given time interval [0, t], ω i L ∞ norm, that is It is known that there is a class Function β, a class Function σ, so that the following holds:

[0204]

[0205] If the condition of formula (32) is satisfied, the stability requirement of the disturbance queue is met.

[0206] In order to verify the effectiveness of the controller designed in this application, a simulation experiment was conducted on a CACC system consisting of one leading car and three following cars. ω = sin(t), road slope angle ρ i =0.1±10%, RBF network structure is 2-5-1, c j =[-1 -0.5 0 0.5 1], b j =5. The controller designed in this application is used to conduct simulation experiments. In the experiment, by appropriately enlarging k 1,i And reduce η i To reduce the longitudinal controller u 1,i The vehicle dynamics parameters and the values ​​of the parameters involved in the experiment are shown in Tables 1 and 2.

[0207] Table 4.1 Vehicle dynamics parameters

[0208]

[0209] Table 4.2 Parameter values ​​involved in the algorithm

[0210]

[0211] Figure 4 and 5 The u obtained by using the controller designed in this application is given 2,1 and u 3,1 situation, Figure 6 This is the controller curve obtained by using the traditional sliding mode control method. As can be seen from the figure, by hiding the discontinuous function in the sliding mode controller in the controller, through the integration, a controller without high-frequency switching can be obtained. However, since the traditional sliding mode control does not process the discontinuous function, the chattering phenomenon is more obvious.

[0212] simulation Figure 7 , Figure 8 as well as Fig. 9 The longitudinal tracking error, lateral tracking error and speed tracking error of the vehicle obtained by using the control algorithm proposed in this application are respectively given. It can be seen from the simulation diagram that when there are unknown nonlinear terms and external disturbances in the vehicle model, the platoon control system can reach a stable state in a short time through the controller designed in this application, and the vehicles can maintain the desired spacing while the following vehicle can track the speed of the leading vehicle well.

[0213] When the vehicle model is uncertain, the driving trajectories of the leader vehicle and the following vehicle are obtained under the action of the controller. As can be seen from the figure, after the leader vehicle sets off, under the action of the controller, the following vehicles can quickly and accurately track the trajectory of the leader vehicle. In summary, through simulation experiments, the designed controller can achieve the control objectives and meet the stability requirements.

[0214] The simulation experiment was carried out using the controller designed in reference

[65] . Fig.11 and Fig.12 The simulation experiment of the controller designed in the application document

[65] is given. When there are unknown nonlinear terms and external disturbances in the vehicle model, the lateral and longitudinal tracking errors between vehicles in the CACC system are obtained. It can be seen that due to the influence of unknown nonlinear terms and unknown external disturbances, the lateral and longitudinal tracking errors have a certain amplitude of oscillation, and the error convergence is poor. Fig.13 The speed error of the vehicle obtained using the controller in reference

[65] is given. As can be seen from the figure, when the follower car tracks the speed of the leader car, there is a certain amplitude of oscillation and the tracking effect is poor.

[0215] Fig.14The driving trajectories of the leader vehicle and three follower vehicles are obtained by using the controller designed in reference

[65] for experiments. From the overall trajectory diagram, it can be seen that due to the influence of model uncertainty, the follower vehicles have oscillations when tracking the driving trajectory of the leader vehicle, and the control system is unstable, which may lead to safety accidents. Therefore, when there are unknown nonlinear terms and external disturbances in the vehicle model, the controller designed in reference

[65] cannot achieve the control target, while the control algorithm proposed in this chapter can effectively solve the above problems.

[0216] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0217] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0218] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0219] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0220] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0221] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0222] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A cooperative connected vehicle control method considering the influence of model uncertainty, characterized in that: The following steps are involved: Step 1: Consider using the sliding mode control method to set the longitudinal vehicle tracking and lateral lane keeping control objectives; Step 2: Design the longitudinal controller based on sliding mode control theory; Step 3: Use the approximation characteristics of the RBF neural network to handle the uncertainties in the vehicle model; Step 4: Introduce an auxiliary sliding surface to suppress the chattering phenomenon existing in the lateral lane keeping controller; Step 5: Analyze the internal stability and queue stability of the controller and further optimize the controller; Step 6: Verify the feasibility of the method through SIMULINK simulation experiments and intelligent connected vehicle platform experiments.

2. A cooperative connected vehicle control method considering the influence of model uncertainty according to claim 1, characterized in that: In the step 1, the control objectives include: internal stability and queue stability; The internal stability is defined as: the vehicle tracking error e x,i and e y,i , satisfies the following formula, that is, the tracking error converges to zero; Wherein, t represents time; The stability of the queue means that when the leading vehicle is disturbed, the tracking error of the vehicle will not be amplified and transmitted along the queue, that is, for the state quantity x i (t), disturbance ω i ,exist Function β, Function σ and constants k1, k2∈R + , so that the following holds:

3. The cooperative connected vehicle control method considering the influence of model uncertainty according to claim 1, characterized in that: From the error expression, formula (4), we can know that: In order to achieve speed tracking and distance maintenance between vehicles, the longitudinal vehicle tracking controller is obtained by combining the error expression: Among them, λ i >0; then s 1,i Taking the derivative with respect to time, we get in Because F r,i Contains unknown dynamic parameters and external disturbances, so f x,i Assume that f is an unknown nonlinear function. x,i smooth and bounded; Then the longitudinal controller is: Among them, η i >0,k 1,i >0.

4. The method for controlling a cooperative connected vehicle under the influence of model uncertainty according to claim 1, characterized in that: In step 3, RBF neural network is used to approximate f x,i , including the following steps: Step 31: Set x i is the network input, and Function f x,i Represented as f x,i =W i *T H i +ε i ; Then the RBF network output is: in, Represents the ideal weight matrix W i * estimates; Step 32: Substitute the longitudinal controller into: in, Indicates f x,i (·) estimation error; Represents the weight error of the network; Step 33: Define the Lyapunov function as: Among them, γ 1,i >0; Step 34: Derivative with respect to time, substitute equation (12) to obtain: The adaptive law is set as: Step 35: Substitute equation (15) into equation (14) to obtain: Since the network approximation error ε i is a real number approaching zero, and η i >0,k 1,i >0, then It can be obtained that the sliding mode function s 1,i It gradually converges to zero, and the longitudinal tracking error e x,i It also converges to zero.

5. The method for controlling a cooperative connected vehicle considering the influence of model uncertainty according to claim 1, characterized in that: In step 4, the sliding mode function is designed as: Among them, c i =[c 1,i c 21,i c 22,i ], are the controller parameters to be designed; Derivative of formula (17) with respect to time yields: in, Then the auxiliary sliding surface is: Also set: Define the Lyapunov function as: Taking the derivative of equation (22) with respect to time, we get: in, make: where ρ i >0, and put it into formula (23) and simplify it to get: That is, when the control law is formula (27), Sliding surface σ i is converged to zero, then the sliding surface s 2,i is converged to zero, the lateral tracking error e y,i It converges to zero.

6. The method for controlling a cooperative connected vehicle under the influence of model uncertainty according to claim 1, characterized in that: In step 5, if there is a class Function β, a class Function σ, and constants k1, k2∈R + , so that for any initial state x i (0) and the external disturbance ω i (t), both satisfy equations (28) and (29): Let x i (t) = [e x,i ,e y,i ] T ,ω i =f x,i , from which we get: ||ω i (t)|| ∞ =max|ω i |; (31) Initial state quantity|x i (0)| is bounded, and the function f x,i is a smooth bounded function; therefore, there exist k1, k2 that satisfy the condition of equation (28); Tracking Error x,i ,e y,i converge to zero, then |x i (t)|converges to a very small neighborhood of the origin; Indicates that within a given time interval [0, t], ω i L ∞ norm, that is It is known that there is a class Function β, a class Function σ, so that the following holds: If the condition of formula (32) is satisfied, the stability requirement of the disturbance queue is met.

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