Interconnected automatic driving vehicle queue control method based on DSFTZND-TZ discrete model

Abstract

The invention discloses an interconnected automatic driving vehicle queue control method based on a DSFTZND-TZ discrete model, and relates to the technical field of interconnected automatic driving vehicle queue control, and the method comprises the following steps: constructing an interconnected automatic driving vehicle queue control system equation; a DSFTZND-TZ discrete model is constructed; solving an interconnection automatic driving vehicle queue control system equation through a DSFTZND-TZ discrete model to obtain the position, the speed and the acceleration of each interconnection automatic driving vehicle so as to complete interconnection automatic driving vehicle queue control. The method has the advantages of over-fitting prevention, fast convergence, high precision and low time delay in solving the problem of an interconnection automatic driving vehicle queue control system equation.

Description

Technical Field

[0001] The present invention relates to the technical field of interconnected autonomous vehicle platoon control, and particularly to an interconnected autonomous vehicle platoon control method based on a DSFTZND-TZ discrete model. Background Art

[0002] In the control of interconnected autonomous vehicle platoons, different controllers or a combination with observers are mainly used to control the position, speed, and acceleration of each interconnected autonomous vehicle. If a small number of vehicles are in a straight line, there are many optimization methods. However, when the number of vehicles in the platoon is large, vehicle controllers often encounter a large number of external interference problems when solving the system equations. This is not desirable in dealing with vehicle platoon control systems. Some literature has presented non-linear manifolds and distributed observers to suppress external interference and solve the exact solution of the system. However, this method often uses fixed parameters, which is a very inefficient process in experiments and will result in problems such as too long convergence time of the system equations, leading to various time-delay problems of the system equations.

[0003] To solve this problem, prior art literature has proposed a method with time-varying parameters. In the case of introducing time-varying parameters, the system equations can converge within a finite time. However, since this method introduces a time-varying increasing parameter, as time increases, the time-varying parameter tends to be infinitely large and is difficult to implement, which may cause equipment damage. At the same time, the increase in computational cost makes it difficult to solve in real time. Summary of the Invention

[0004] Aiming at the above deficiencies in the prior art, the interconnected autonomous vehicle platoon control method based on the DSFTZND-TZ discrete model provided by the present invention solves the problem that it is difficult to effectively control the interconnected autonomous vehicle platoon stably in the prior art.

[0005] To achieve the above object of the invention, the technical solution adopted by the present invention is as follows:

[0006] Provide an interconnected autonomous vehicle platoon control method based on a DSFTZND-TZ discrete model, which includes the following steps:

[0007] S1. Set the longitudinal dynamic system equation of the interconnected autonomous vehicle;

[0008] S2. Linearize the longitudinal dynamic system equation of the interconnected autonomous vehicle and add external interference to obtain the rewritten longitudinal dynamic system equation;

[0009] S3. Based on the rewritten longitudinal dynamic system equation, construct the longitudinal dynamic system equation of the interconnected autonomous vehicle with virtual signal connection;

[0010] S4. Discretize the rewritten longitudinal dynamics system equation and the longitudinal dynamics system equation of the virtual signal interconnected autonomous driving vehicle respectively, and correspondingly obtain the discretized first longitudinal dynamics system equation and the discretized second longitudinal dynamics system equation;

[0011] S5. Construct the interconnected autonomous driving vehicle queue control system equation according to the discretized first longitudinal dynamics system equation and the discretized second longitudinal dynamics system equation;

[0012] S6. Construct the DSFTZND-TZ discrete model;

[0013] S7. Solve the interconnected autonomous driving vehicle queue control system equation through the DSFTZND-TZ discrete model to obtain the position, speed and acceleration of each interconnected autonomous driving vehicle, and then complete the interconnected autonomous driving vehicle queue control.

[0014] The beneficial effects of the present invention are as follows: In the DSFTZND-TZ discrete model of the present invention, a new distributed observer, controller, nonlinear manifold, bounded variable parameter and variable time step are designed and adopted. The distributed observer enables the present invention to track the signals of virtual signal vehicles in real time. The controller enables the present invention to converge within a finite time. The nonlinear manifold enables the model to improve the convergence characteristics of the present invention under noise interference. The bounded variable parameter can prevent the parameters of the present invention from overfitting. The variable time step enables the present invention to have a faster convergence speed and higher precision, and has the advantages of high precision and low delay. The present invention is superior to existing solutions in terms of convergence speed, precision and robustness. Description of the Drawings

[0015] Figure 1 It is a flow schematic diagram of this method;

[0016] Figure 2 It is a vehicle communication topology diagram provided in the embodiment of the present application based on this method;

[0017] Figure 3 It is a comparison diagram of the error convergence precision of the position, speed and acceleration of this method and the method of using other discrete models to solve the interconnected autonomous driving vehicle queue control system with the expected value under constant noise conditions;

[0018] Figure 4 It is a comparison diagram of the error convergence precision of the position, speed and acceleration of this method and the method of using other discrete models to solve the interconnected autonomous driving vehicle queue control system with the expected value under linear time-varying noise conditions;

[0019] Figure 5Comparison chart of the error convergence accuracy between the positions, speeds, and accelerations of this method and those solved by other discrete models for the interconnected autonomous vehicle platoon control system and the expected values under bounded random noise conditions;

[0020] Figure 6 Comparison chart of the error convergence accuracy between the positions, speeds, and accelerations of this method and those solved by other discrete models for the interconnected autonomous vehicle platoon control system and the expected values under bounded periodic noise conditions;

[0021] Figure 7 Comparison chart of the error convergence accuracy between the positions, speeds, and accelerations of this method and those solved by other discrete models for the interconnected autonomous vehicle platoon control system and the expected values under Gaussian white noise conditions;

[0022] Figure 8 Comparison chart of the actual positions, speeds, and accelerations of each vehicle solved by this method and the expected values for the interconnected autonomous vehicle platoon control system under constant noise conditions;

[0023] Figure 9 Error chart of the actual positions, speeds, and accelerations of each vehicle solved by this method and the expected values for the interconnected autonomous vehicle platoon control system under constant noise conditions; where (a) is the position difference between the interconnected autonomous vehicle and the virtual interconnected autonomous vehicle; (b) is the position difference between adjacent interconnected autonomous vehicles; (c) is the speed difference between the interconnected autonomous vehicle and the virtual interconnected autonomous vehicle; (d) is the acceleration difference between the interconnected autonomous vehicle and the virtual interconnected autonomous vehicle;

[0024] Figure 10 Comparison chart of the actual positions, speeds, and accelerations of each vehicle solved by this method and the expected values for the interconnected autonomous vehicle platoon control system under linear time-varying noise conditions;

[0025] Figure 11 Error chart of the actual positions, speeds, and accelerations of each vehicle solved by this method and the expected values for the interconnected autonomous vehicle platoon control system under linear time-varying noise conditions; where (a) is the position difference between the interconnected autonomous vehicle and the virtual interconnected autonomous vehicle; (b) is the position difference between adjacent interconnected autonomous vehicles; (c) is the speed difference between the interconnected autonomous vehicle and the virtual interconnected autonomous vehicle; (d) is the acceleration difference between the interconnected autonomous vehicle and the virtual interconnected autonomous vehicle;

[0026] Figure 12 Comparison chart of the actual positions, speeds, and accelerations of each vehicle solved by this method and the expected values for the interconnected autonomous vehicle platoon control system under bounded random noise conditions;

[0027] Figure 13For the case of bounded random noise, the error graphs of the actual position, speed, and acceleration of each vehicle in the interconnected autonomous vehicle platoon control system solved by this method compared with the expected values; where (a) is the position difference between the interconnected autonomous vehicle and the virtual interconnected autonomous vehicle; (b) is the position difference between adjacent interconnected autonomous vehicles; (c) is the speed difference between the interconnected autonomous vehicle and the virtual interconnected autonomous vehicle; (d) is the acceleration difference between the interconnected autonomous vehicle and the virtual interconnected autonomous vehicle.

[0028] Figure 14 For the case of bounded periodic noise, the comparison graphs of the actual position, speed, and acceleration of each vehicle in the interconnected autonomous vehicle platoon control system solved by this method compared with the expected values.

[0029] Figure 15 For the case of bounded periodic noise, the error graphs of the actual position, speed, and acceleration of each vehicle in the interconnected autonomous vehicle platoon control system solved by this method compared with the expected values; where (a) is the position difference between the interconnected autonomous vehicle and the virtual interconnected autonomous vehicle; (b) is the position difference between adjacent interconnected autonomous vehicles; (c) is the speed difference between the interconnected autonomous vehicle and the virtual interconnected autonomous vehicle; (d) is the acceleration difference between the interconnected autonomous vehicle and the virtual interconnected autonomous vehicle.

[0030] Figure 16 For the case of Gaussian white noise, the comparison graphs of the actual position, speed, and acceleration of each vehicle in the interconnected autonomous vehicle platoon control system solved by this method compared with the expected values.

[0031] Figure 17 For the case of Gaussian white noise, the error graphs of the actual position, speed, and acceleration of each vehicle in the interconnected autonomous vehicle platoon control system solved by this method compared with the expected values; where (a) is the position difference between the interconnected autonomous vehicle and the virtual interconnected autonomous vehicle; (b) is the position difference between adjacent interconnected autonomous vehicles; (c) is the speed difference between the interconnected autonomous vehicle and the virtual interconnected autonomous vehicle; (d) is the acceleration difference between the interconnected autonomous vehicle and the virtual interconnected autonomous vehicle. Detailed implementation manners

[0032] The following describes the detailed implementation manners of the present invention to facilitate those skilled in the art of this technology to understand the present invention. However, it should be clear that the present invention is not limited to the scope of the detailed implementation manners. For those of ordinary skill in the art of this technology, as long as various changes are within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions created using the concept of the present invention are within the scope of protection.

[0033] Such as Figure 1As shown in the figure, the interconnected autonomous vehicle platoon control method based on the DSFTZND-TZ discrete model includes the following steps:

[0034] S1. Set the longitudinal dynamics system equation of the interconnected autonomous vehicle;

[0035] S2. Linearize the longitudinal dynamics system equation of the interconnected autonomous vehicle and add external disturbances to obtain the rewritten longitudinal dynamics system equation;

[0036] S3. Based on the rewritten longitudinal dynamics system equation, construct the longitudinal dynamics system equation of the virtual signal interconnected autonomous vehicle;

[0037] S4. Discretize the rewritten longitudinal dynamics system equation and the longitudinal dynamics system equation of the virtual signal interconnected autonomous vehicle respectively to obtain the discretized first longitudinal dynamics system equation and the discretized second longitudinal dynamics system equation;

[0038] S5. Construct the interconnected autonomous vehicle platoon control system equation according to the discretized first longitudinal dynamics system equation and the discretized second longitudinal dynamics system equation;

[0039] S6. Construct the DSFTZND-TZ discrete model;

[0040] S7. Solve the interconnected autonomous vehicle platoon control system equation through the DSFTZND-TZ discrete model to obtain the position, speed and acceleration of each interconnected autonomous vehicle, and then complete the interconnected autonomous vehicle platoon control.

[0041] In step S1, the expression of the longitudinal dynamics system equation of the vehicle is:

[0042]

[0043] where l j (t), v j (t), T j (t) and respectively represent the position, speed, actual driving / braking torque and desired driving / braking torque of the jth interconnected autonomous vehicle; and are the first-order derivatives of l j (t), v j (t) and T j (t); m j , η j , R j , g, f and respectively represent the mass of the j-th connected and autonomous vehicle, the mechanical efficiency of the powertrain, the tire radius, the combined aerodynamic drag coefficient, the gravitational acceleration, the rolling resistance coefficient, and the inertial delay of longitudinal dynamics; t represents time.

[0044] The specific method of linearizing the longitudinal dynamics system equation of the connected and autonomous vehicle and adding external disturbances in step S2 includes the following sub-steps:

[0045] S2-1. Construct a linearized expression:

[0046]

[0047] where represents the input after linearization of the j-th connected and autonomous vehicle;

[0048] S2-2. Apply the linearized expression to the longitudinal dynamics system equation of the connected and autonomous vehicle to obtain the longitudinal dynamics system equation of the linearized vehicle, and its expression is:

[0049]

[0050] where a j (t) is the acceleration of the j-th connected and autonomous vehicle; is the first derivative of a j (t);

[0051] S2-3. Add external disturbances to the longitudinal dynamics system equation of the linearized vehicle to obtain the longitudinal dynamics system equation after adding disturbances, and its expression is:

[0052]

[0053] where is the external disturbance of the j-th connected and autonomous vehicle;

[0054] S2-4. Let to obtain the rewritten longitudinal dynamics system equation, and its expression is:

[0055]

[0056] where ρ j (t) is the external disturbance after the new input of the j-th connected and autonomous vehicle, |ρ j (t)| ≤ Λ, Λ is a bounded positive real number, |·| represents the absolute value operator; c j (t) is the controller to be designed for the j-th connected and autonomous vehicle.

[0057] In step S3, the expression of the longitudinal dynamics system equation of the virtual signal interconnected autonomous vehicle is as follows:

[0058]

[0059] Where l 0 (t), v 0 (t), a 0 (t) and c 0 (t) represent the position, speed, acceleration and input of the virtual signal interconnected autonomous vehicle respectively; And Are the first-order derivatives of l 0 (t), v 0 (t) and a 0 (t); c 0 (t) ≤ c max , c max Is a positive real number.

[0060] In step S4, the expression of the discretized first longitudinal dynamics system equation is as follows:

[0061]

[0062] Where l j,k , v j,k , a j,k , c j,k And ρ j,k Respectively represent the sampling values of the j-th interconnected autonomous vehicle at time t k For l j (t), v j (t), a j (t), c j (t) and ρ j (t);

[0063] In step S4, the discretized second longitudinal dynamics system equation, its expression is as follows:

[0064]

[0065] Where l 0,k , v 0,k , a 0,k And c 0,k Respectively represent the sampling values of the virtual signal interconnected autonomous vehicle at time t k For l 0 (t), v 0 (t), a 0 (t) and c 0 (t).

[0066] In step S5, the expression of the interconnected autonomous vehicle platoon control system equation is as follows:

[0067]

[0068] where l k = [l 1,k , l 2,k ,..., l m,k T ; v k = [v 1,k , v 2,k ,..., v m,k T ; a k = [a 1,k , a 2,k ,..., a m,k T ; c k = [c 1,k , c 2,k ,..., c m,k T ; ρ k = [ρ 1,k , ρ 2,k ,..., ρ m,k T ; and are the first-order derivatives of l k , v k and c k respectively; [.] T represents the transpose of the matrix; m is the total number of interconnected autonomous vehicles;

[0069] The expression of the error function of the interconnected autonomous vehicle platoon control system equation is:

[0070]

[0071] where and are the observed values of l 0,k , v 0,k and a 0,k of each interconnected autonomous vehicle with respect to the virtual signal interconnected autonomous vehicle; d = j·d 0 , d represents the designed distance between each interconnected autonomous vehicle and the virtual signal interconnected autonomous vehicle, and d 0 is the expected spacing designed between adjacent vehicles; e l,k , e v,k and e a,k ​​​​​They are the error values of each connected and autonomous vehicle in terms of position, speed, and acceleration relative to the virtual signal connected and autonomous vehicle during the k-th sampling, respectively.

[0072] In step S6, the DSFTZND-TZ discrete model includes a distributed observer, a controller, a nonlinear manifold, bounded variable parameters, and a variable time step; the expression for the derivative of the error function in the DSFTZND-TZ discrete model is:

[0073]

[0074] The expression for the distributed observer is:

[0075]

[0076] Where and are respectively and 's first-order derivatives; Q = L + P, which is the Laplacian matrix of the communication topology graph of the connected and autonomous vehicles, and P = [p 1 , p 2 ,..., p j ,..., p m T is the communication link matrix between the connected and autonomous vehicles and the virtual signal connected and autonomous vehicle. When the j-th signal connected and autonomous vehicle can communicate with the virtual signal connected and autonomous vehicle, p j = 1, otherwise p j = 0; Sgn(X) is the sign function. When X < 0, Sgn(X) = -1; when X = 0, Sgn(X) = 0; when X > 0, Sgn(X) = 1; θ 1,k , θ 2,k and θ 3,k are all bounded variable parameters;

[0077] The expression for the controller c k of the DSFTZND-TZ discrete model is:

[0078]

[0079] Where β 1 , β 2 , β 3 , ξ 1 , ξ 2 and ξ 3 are all positive real numbers; Π k is a bounded variable parameter; is the nonlinear manifold, and its expression is: ​

[0080]

[0081] where \(e\) l,i 、\(e\) v,i and \(e\) a,i are the error values of each connected and autonomous vehicle (CAV) relative to the virtual signal CAV in terms of position, speed, and acceleration during the \(k\)th sampling, respectively; \(\tau\) i is the variable time step, and its expression is:

[0082]

[0083] where \(\delta\), \(\Gamma\), and \(\alpha\) are all positive real numbers greater than 0 and less than or equal to 1; \(\varPhi\) i represents the attenuation function; \(\exp(.)\) represents the exponential function with the natural constant \(e\) as the base; \(\varXi\) i represents the strong initial state function; \(\log\) represents the logarithm;

[0084] When \(t\) i \(\leq v\) i , there exists

[0085] When \(t\) i \(> v\) i , there exists

[0086] where \(\Upsilon\) is a positive real number greater than 1; \(\varepsilon\) is a positive real number; \(\lambda\) max \((Q)\) and \(\lambda\) min \((Q)\) represent the maximum eigenvalue and the minimum eigenvalue of matrix \(Q\), respectively; \(v\) i is the critical time, \(v\) i \(= t\) 1,i \(+ t\) 2,i \(+ t\) 3,i \(+ t\) 4,i \(+ t\) del , \(t\) del represents the hardware response time and is a positive real number; \(t\) 1,i , \(t\) 2,i , \(t\) 3,i and \(t\) 4,i have the following expressions:

[0087]

[0088] where \(\ln(.)\) represents the natural logarithm function with the natural constant \(e\) as the base; is transpose of;

[0089] Correspondingly, when \(i\) takes the value of 0, it corresponds to the initial state. When \(i\) takes the value of \(k\), \(\tau\)k The corresponding expression is:

[0090]

[0091] where Φ k represents the attenuation function; Ξ k represents the strong initial state function; log represents the logarithm;

[0092] When t k ≤ν k there exists

[0093] When t k >ν k there exists

[0094] v k is the critical time, v k =t 1,k +t 2,k +t 3,k +t 4,k +t del t del represents the hardware response time, which is a positive real number; t 1,k t 2,k t 3,k and t 4,k The expressions of are as follows:

[0095]

[0096] where ln(.) represents the natural logarithm function with base e; is the transpose of,

[0097] The expressions of the position, velocity, and the first derivative of the acceleration of the interconnected autonomous vehicle queue control system equation solved by the DSFTZND-TZ discrete model for the interconnected autonomous vehicle are:

[0098]

[0099] The specific method for solving the interconnected autonomous vehicle queue control system equation by the DSFTZND-TZ discrete model is:

[0100] When k takes values of 0 and 1, let

[0101] When k is greater than or equal to 2, let

[0102] where sk+1 The iterative updated value of s k ; is the first derivative of s k ; s k-1 represents the iterative value of s k at the previous moment; s k-2 represents the iterative value of s k-1 at the previous moment; a 0,k is the acceleration of the virtual connected autonomous vehicle; v 0,k is the speed of the virtual connected autonomous vehicle;

[0103] By substituting into the DSFTZND-TZ discrete model, the positions, speeds, and accelerations of each connected autonomous vehicle are obtained.

[0104] In the specific implementation process, the acceleration of the virtual connected autonomous vehicle is expressed as:

[0105]

[0106] In an embodiment of the present invention, the communication topology diagram of the vehicle is as Figure 2 shown. To verify the effect of the present invention, the present method is compared with control methods using other discrete models (such as TDZND-TZ and NRTDZND-TZ discrete models), and the results are as Figure 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7 shown. It can be intuitively seen from Figures 3 to 7 that the convergence speed of the present method is faster than that of the methods using other models; in terms of accuracy, the accuracy of the present method in solving the equation of the connected autonomous vehicle queue control system is better than that of the methods using TDZND-TD model and NRTDZND-TZ model; in terms of noise suppression, the method using TDZND-TD model fails to converge in the case of periodic noise, while the present method still converges to the 10 -4 th order and is better than the method using NRTDZND-TZ model. Therefore, the performance of the present method is more competitive than other advanced solutions.

[0107] Figure 8 , Figure 9 , Figure 10 , Figure 11 , Figure 12 , Figure 13 , Figure 14 , Figure 15 , Figure 16 and Figure 17It shows the comparison graph and error graph of the actual position, speed, and acceleration of each vehicle with the expected values when solving the equations of the interconnected autonomous vehicle platoon control system under different noise conditions. It can be seen from Figures 8 to 17 that when the parameters are moderate, the errors of the actual position, speed, and acceleration of each vehicle with the expected values can all reach the 10 -4 th order and converge within a finite time. Thus, it can be seen that this method has the advantages of high precision and low latency in solving the equations of the interconnected autonomous vehicle platoon control system.

Claims

1. A method for controlling a platoon of interconnected autonomous driving vehicles based on a DSFTZND-TZ discrete model, characterized in that: The following steps are involved: S1. Set the longitudinal dynamic system equations of the connected autonomous vehicle; S2, linearizing the longitudinal dynamics system equation of the connected autonomous driving vehicle and adding external disturbances to obtain a rewritten longitudinal dynamics system equation; S3. Constructing the longitudinal dynamics system equation of the virtual signal interconnected autonomous driving vehicle based on the rewritten longitudinal dynamics system equation; S4. discretizing the rewritten longitudinal dynamics system equation and the longitudinal dynamics system equation of the virtual signal interconnected autonomous driving vehicle, respectively, to obtain a discretized first longitudinal dynamics system equation and a discretized second longitudinal dynamics system equation; S5. constructing a control system equation for a platoon of interconnected autonomous driving vehicles according to the discretized first longitudinal dynamics system equation and the discretized second longitudinal dynamics system equation; S6, construct DSFTZND-TZ discrete model; S7. Solve the control system equations of the interconnected autonomous driving vehicle platoon through the DSFTZND-TZ discrete model to obtain the position, velocity, and acceleration of each interconnected autonomous driving vehicle, thereby completing the control of the interconnected autonomous driving vehicle platoon.

2. The interconnected autonomous driving vehicle platoon control method based on the DSFTZND-TZ discrete model according to claim 1, characterized in that: The expression of the longitudinal dynamic system equation of the vehicle in step S1 is: ； in , , and denote the position, speed, actual driving / braking torque, and expected driving / braking torque of the jth connected autonomous vehicle, respectively; , and They are , and The first derivative of ; , , , , , and denote the mass of the jth connected autonomous vehicle, the mechanical efficiency of the powertrain, the tire radius, the combined aerodynamic drag coefficient, the gravitational acceleration, the rolling resistance coefficient, and the inertial delay of the longitudinal dynamics; t denotes time.

3. The interconnected automatic driving vehicle platoon control method based on the DSFTZND-TZ discrete model according to claim 2 is characterized in that: The specific method of linearizing the longitudinal dynamics system equation of the connected autonomous driving vehicle and adding external disturbance in step S2 includes the following sub-steps: S2-1. Construct linearized expression: ； in represents the linearized input of the jth connected autonomous vehicle; S2-2. Apply the linearized expression to the longitudinal dynamics system equation of the connected autonomous driving vehicle to obtain the longitudinal dynamics system equation of the linearized vehicle, which is expressed as: ； in is the acceleration of the jth connected autonomous vehicle; for The first derivative of ; S2-3. Add external disturbance to the longitudinal dynamic system equation of the linearized vehicle to obtain the longitudinal dynamic system equation after the disturbance is added, and its expression is: ； in is the external interference of the jth connected autonomous driving vehicle; S2-4, Order , we get the rewritten longitudinal dynamic system equation, which is expressed as: ； in is the external disturbance newly input by the jth connected autonomous driving vehicle; The controller to be designed for the jth connected autonomous vehicle.

4. The interconnected automatic driving vehicle platoon control method based on the DSFTZND-TZ discrete model according to claim 3 is characterized in that: The expression of the longitudinal dynamic system equation of the virtual signal interconnected autonomous driving vehicle in step S3 is: ； in , , and They represent the position, velocity, acceleration and input of the virtual signal interconnected autonomous driving vehicle respectively; , and They are , and The first derivative of .

5. The interconnected automatic driving vehicle platoon control method based on the DSFTZND-TZ discrete model according to claim 4 is characterized in that: The expression of the first longitudinal dynamic system equation discretized in step S4 is: ； in , , , and They represent the jth connected autonomous driving vehicle in Always , , , and The sampling value of ; The second longitudinal dynamic system equation discretized in step S4 is expressed as: ； in , , and They represent the virtual signal interconnected autonomous driving vehicles in Always , , and The sampling value of .

6. The interconnected autonomous driving vehicle platoon control method based on the DSFTZND-TZ discrete model according to claim 5, characterized in that: The expression of the control system equation of the interconnected autonomous driving vehicle platoon in step S5 is: ； in ; ; ; ; ; , and They are , and The first derivative of ; represents the transpose of the matrix; m is the total number of connected autonomous vehicles; The error function of the control system equation for the interconnected autonomous vehicle platoon is expressed as: ； in , and The number of connected autonomous vehicles to virtual signal connected autonomous vehicles is , and Observed value of , represents the design distance between each connected autonomous vehicle and the virtual signal connected autonomous vehicle, Designed for the desired spacing between adjacent vehicles; , and are the error values ​​of position, velocity and acceleration of each connected autonomous vehicle relative to the virtual signal connected autonomous vehicle, respectively.

7. The interconnected automatic driving vehicle platoon control method based on the DSFTZND-TZ discrete model according to claim 6 is characterized in that: In step S6, the DSFTZND-TZ discrete model includes a distributed observer, a controller, a nonlinear manifold, a bounded variable parameter, and a variable time step; wherein: The expression of the distributed observer is: ； in , and They are , and The first derivative of ; , is the Laplace matrix of the interconnected autonomous driving vehicle communication topology graph, is the communication link matrix between the interconnected autonomous driving vehicles and the virtual signal-interconnected autonomous driving vehicles. When the jth signal-interconnected autonomous driving vehicle can communicate with the virtual signal-interconnected autonomous driving vehicle, ,otherwise ; , , ; is a symbolic function, when When it is less than 0, ,when When it is equal to 0, ,when When greater than 0, ; , and All are bounded variable parameters; Controller for DSFTZND-TZ Discrete Model The expression is: ； in , , , , and are all positive real numbers; is a bounded variable parameter; is a nonlinear manifold, and its expression is: ； is a variable time step, and its expression is: ； in , and They are all positive real numbers greater than 0 and less than or equal to 1; represents the decay function; It represents the exponential function with the natural constant e as the base; represents a strong initial state function; log represents a logarithm; when When ; when When ; in is a positive real number greater than 1; is a positive real number; and Respectively represent matrices The maximum and minimum eigenvalues ​​of ; is the critical time, , Represents the hardware response time, which is a positive real number; is a bounded positive real number; , , and The expression is as follows: ； in It represents the logarithmic function with the natural constant e as the base; , for The transpose of , , .

8. The interconnected automatic driving vehicle platoon control method based on the DSFTZND-TZ discrete model according to claim 7, characterized in that: In step S7, the DSFTZND-TZ discrete model solves the first-order derivatives of the position, velocity and acceleration of the connected autonomous vehicle in the control system equation of the connected autonomous vehicle platoon as follows: 。 9. The interconnected automatic driving vehicle platoon control method based on the DSFTZND-TZ discrete model according to claim 8, characterized in that: The specific method for solving the control system equations of the interconnected autonomous driving vehicle platoon through the DSFTZND-TZ discrete model is: when When the value is 0 or 1, ; when When the value is greater than or equal to 2, let ; in ; express Iterative update value of ; for The first derivative of ; express The iteration value of the previous moment; express The iteration value of the previous moment; Acceleration of virtual connected autonomous vehicles; for the speed of virtually connected autonomous vehicles; By Substitute the DSFTZND-TZ discrete model to obtain the position, velocity, and acceleration of each connected autonomous driving vehicle.

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