A smart connected vehicle platoon control method based on decentralized active disturbance rejection control

Abstract

The application relates to a kind of intelligent networked vehicle formation control methods based on decentralized active disturbance rejection control, first, the longitudinal dynamics model of the leading vehicle of vehicle formation is established, and then the tracking error model of the following vehicle is obtained according to the control target;Then, the active disturbance rejection controller of the leading vehicle and the following vehicle is designed respectively, which includes three parts of tracking differentiator, extended state observer and nonlinear state error feedback law, estimates and compensates the lumped disturbance of each vehicle.Compared with the existing work, the application adopts a third-order dynamics model and a third-order active disturbance rejection controller, which considers the dynamic characteristics of the vehicle actuator hysteresis more than the second-order dynamics model, is closer to the actual situation, and adopts decentralized active disturbance rejection control, which can better guarantee the tracking safety performance of vehicle formation.

Description

Technical Field

[0001] This invention relates to a distributed active disturbance rejection control method for intelligent connected vehicle platooning, belonging to the field of intelligent transportation technology. Background Technology

[0002] In recent years, research in the field of intelligent transportation has received increasing attention. As part of vehicle-road cooperative applications, vehicle platooning control has significant research value in improving road safety, alleviating traffic pressure, and reducing vehicle fuel consumption.

[0003] With the development of communication and vehicle-to-everything (V2X) technologies, intelligent connected vehicles have become a new breakthrough in solving traffic congestion, traffic safety, and traffic pollution. Connected vehicle platooning is an important operating mode for connected vehicles, comprehensively considering real-time road conditions, vehicle speeds, spacing, and reference speeds to formulate optimal control strategies for each vehicle, thereby improving the safety and robustness to external interference. Currently, connected vehicle platooning primarily relies on a follow-the-leader approach, where each connected vehicle adjusts its control strategy based on the speed and position of the vehicle ahead, maintaining the same speed and constant spacing. However, in reality, sudden external interference can significantly impact the entire connected vehicle platoon's operation, making stable operation difficult. Therefore, this application proposes a distributed active disturbance rejection control (ADRC)-based intelligent connected vehicle platooning control method to improve the system's anti-disturbance performance. Summary of the Invention

[0004] To overcome the shortcomings of existing research, this invention proposes an intelligent connected vehicle platooning control method based on distributed active disturbance rejection control. In view of the parameter uncertainties and external disturbances in actual working conditions, active disturbance rejection control technology is used to estimate and compensate for the lumped disturbances of the system, so as to achieve the goal of making the vehicle tracking error approach zero.

[0005] The specific steps of a platooning control method for intelligent connected vehicles based on distributed active disturbance rejection control are as follows:

[0006] Step 1: Establish a connected vehicle platoon model, including the longitudinal dynamics model of the lead vehicle and the tracking error model of the following vehicles;

[0007] The vehicles in the formation are numbered from 0 to i from front to back, where the first vehicle is the lead vehicle and the remaining i vehicles are the following vehicles. The longitudinal dynamics model of the vehicles is established as follows:

[0008]

[0009] Where, p i vi a i These represent the vehicle's position, velocity, and acceleration, respectively; τ i It is the engine time lag constant; u i This is the control input used to set the desired acceleration. Furthermore, the uncertainties in the vehicle's own parameters and the external disturbances are considered as a time-varying and bounded lumped disturbance fi(u). i (t), a i (t)), and finally perform feedback compensation and take τ. i The intermediate value is denoted as τ. i0 , where τ i0 It is a constant. Therefore, f i (u i (t), a i (t) can be expressed as follows:

[0010]

[0011] Therefore, the vehicle's dynamic model was ultimately established as follows:

[0012]

[0013] Among them, b i0 For the adjustable parameters to be designed, u i0 This is the control input for the active disturbance rejection controller. At this time, the system's control input u... i It can be obtained from the following formula.

[0014] u i =τ i0 b i0 u i0

[0015] Define the vehicle position tracking error, speed tracking error, and acceleration tracking error as follows:

[0016]

[0017] Where d i，des L represents the desired distance between adjacent vehicles. i Let be the length of vehicle i. Following the vehicle aims to minimize the tracking error; therefore, the tracking error model is established as follows:

[0018]

[0019] Step 2: Select the control target

[0020] Step 2.1, Internal Stability

[0021] For all i = 0, 1, ..., I, the error dynamics of vehicle formation is asymptotically stable, i.e., it satisfies:

[0022]

[0023] Step 2.2, Queue Stability

[0024] If the spacing error between adjacent vehicles does not amplify along the platoon, then the vehicle formation system is queue-stable and satisfies the following conditions:

[0025]

[0026] Step 3: Design of Active Disturbance Rejection Controller

[0027] Specifically, the design of the active disturbance rejection controller includes three parts: a tracking differentiator, an extended state observer, and a nonlinear state error feedback law.

[0028] Step 3.1: Design of the Active Disturbance Rejection Controller for the Lead Vehicle

[0029] Step 3.1.1 Use a tracking differentiator to assign a reference trajectory p to the leading vehicle. 0，ref Arrange a suitable transition process Simultaneously output Differential signal and the second differential signal The specific expression is as follows:

[0030]

[0031] In the formula, r0 is the velocity factor; fhan is the fastest combined function, and its expression is as follows:

[0032] fhan(x1,x2,x3,r0)=-r0·sign(x1+s·a·(sqrt(a / r0)+s·b)-r0·b 3 / 6)

[0033] in,

[0034]

[0035] Step 3.1.2 uses ESO to estimate the lumped disturbance f0(u0(t), a0(t)) of the leading vehicle. The specific expression for the extended state observer of the leading vehicle is given below:

[0036]

[0037] In the above formula, e0(t) is the estimated deviation of the position of the leading vehicle; This is the predicted value of the system state variable p0(t); This is the predicted value of the system state variable v0(t); This is the predicted value of the system state variable a0(t); The estimated value of the disturbance f0(u0(t), a0(t)). B0 is the compensation factor; β1, β2, β3, and β4 are observer parameters; δ is a nonlinear parameter; fal is a nonlinear function, and its specific expression is:

[0038]

[0039] Finally, step 3.1.3 gives the nonlinear state error feedback law u of the leading vehicle. 00 as follows:

[0040]

[0041] Where, β 10 ,β 20 ,β 30 U is the gain coefficient. 00 (t) represents the actual control quantity of the system.

[0042] Step 3.2 Design of Active Disturbance Rejection Controller for Following Vehicle

[0043] Step 3.2.1 Use the tracking differentiator to calculate the reference position error e of the leading vehicle. i，ref，p Arrange a suitable transition process Simultaneously output Differential signal and the second differential signal The specific expression is as follows:

[0044]

[0045] Step 3.2.2 Use ESO to process the lumped disturbance f of the following vehicle. i (u i (t), a i (t), u i-1 (t), a i-1 The estimation is performed using (t). The specific expression for the extended state observer following the vehicle is given below:

[0046]

[0047] In the above formula, e i (t) represents the estimated deviation of the position error; For system state variable e i，p The predicted value of (t); For system state variable e i，v The predicted value of (t); For system state variable e i，a The predicted value of (t); For the disturbance f i (u i (t), a i (t), u i-1 (t), a i-1 The estimated value of (t)).

[0048] Finally, step 3.2.3 gives the nonlinear state error feedback law u for the following vehicle. i0 as follows:

[0049]

[0050] Where, β 10 ,β 20 ,β 30 U is the gain coefficient. i0 (t) represents the actual control quantity of the system.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] This invention treats the vehicle's own parameter uncertainties and the external disturbances it experiences as a time-varying and bounded lumped disturbance, and estimates and compensates for it through an active disturbance rejection controller, which helps improve the system's disturbance rejection performance. At the same time, the use of a third-order dynamic model takes into account the dynamic characteristics of the vehicle's actuator lag, making it closer to reality.

[0053] This invention employs distributed active disturbance rejection control. For the tracking error model of following vehicles, a transition process for the reference position error is arranged, aiming to make the vehicle tracking error zero, thereby better ensuring the tracking safety performance of vehicle formations. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a structural diagram of the active disturbance rejection control technology used in this invention;

[0056] Figure 2 The vehicle parameters are set according to the embodiments of the present invention;

[0057] Figure 3 This is a simulation diagram of the driving section of the present invention;

[0058] Figure 4 The figures show the simulation results of position, spacing error, speed, and control input under the control method of this invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] The specific steps of a platooning control method for intelligent connected vehicles based on distributed active disturbance rejection control are as follows:

[0061] Step 1: Establish a connected vehicle platoon model, including the longitudinal dynamics model of the lead vehicle and the tracking error model of the following vehicles;

[0062] The vehicles in the formation are numbered from 0 to i from front to back, where the first vehicle is the lead vehicle and the remaining i vehicles are the following vehicles. The longitudinal dynamics model of the vehicles is established as follows:

[0063]

[0064] Where, p i v i a i These represent the vehicle's position, velocity, and acceleration, respectively; τ i It is the engine time lag constant, which varies with operating conditions in actual situations; u i This serves as the control input for setting the desired acceleration. Furthermore, the vehicle's own parameter uncertainties and the external disturbances it experiences are considered as a time-varying and bounded lumped disturbance f. i (u i (t), a i (t)), and finally perform feedback compensation and take τ. i The intermediate value is denoted as τ. i0 , where τ i0 It is a constant. Therefore, f i (u i (t), a i (t) can be expressed as follows:

[0065]

[0066] Therefore, the vehicle's dynamic model was ultimately established as follows:

[0067]

[0068] Among them, b i0 For the adjustable parameters to be designed, u i0This is the control input for the active disturbance rejection controller. At this time, u i It can be obtained from the following formula.

[0069] u i =τ i0 b i0 u i0

[0070] Define the vehicle position tracking error, speed tracking error, and acceleration tracking error as follows:

[0071]

[0072] Where d i，des L represents the desired distance between adjacent vehicles. i Let be the length of vehicle i. Following the vehicle aims to minimize the tracking error; therefore, the tracking error model is established as follows:

[0073]

[0074] Will Substituting into the above formula, we get:

[0075]

[0076]

[0077] The tracking error model for following vehicles is ultimately established as shown below:

[0078]

[0079] Step 2: Select the control target

[0080] Step 2.1, Internal Stability

[0081] When the acceleration of the lead vehicle is 0, all following vehicles remain at the desired position and eventually reach the same speed as the lead vehicle. That is, for all i = 0, 1, ..., i, the error dynamics of the vehicle formation are asymptotically stable, i.e., satisfying:

[0082]

[0083] Step 2.2, Queue Stability

[0084] If the spacing error between adjacent vehicles does not amplify along the platoon, then the vehicle formation system is queue-stable, meaning that for any ε > 0, there exists a δ > 0 satisfying the following equation:

[0085]

[0086] Step 3: Design of Active Disturbance Rejection Controller

[0087] Specifically, the design of the active disturbance rejection controller (ADRC) comprises three parts: a tracking differentiator, an extended state observer, and a nonlinear state error feedback law. First, the tracking differentiator arranges the transient response and provides a reasonable control signal. Then, the extended state observer designs an extended state variable to track the influence of unknown parts of the model and external unknown disturbances, providing control inputs to compensate for these disturbances. Finally, the nonlinear error feedback control law provides the control strategy for the controlled object. The specific implementation structure diagram is shown below. Figure 1 As shown.

[0088] Step 3.1: Design of the Active Disturbance Rejection Controller for the Lead Vehicle

[0089] Step 3.1.1 Use a tracking differentiator to assign a reference trajectory p to the leading vehicle. 0，ref Arrange a suitable transition process Simultaneously output Differential signal and the second differential signal The specific expression is as follows:

[0090]

[0091] In the formula, r0 is the velocity factor; fhan is the fastest combined function, and its expression is as follows:

[0092] fhan(x1,x2,x3,r0)=-r0·sign(x1+s·a·(sqrt(a / r0)+s·b)-r0·b 3 / 6)

[0093] in,

[0094]

[0095] Step 3.1.2 Use ESO to estimate the lumped disturbance f0(u0(t), a0(t)) of the leading vehicle. First, let f0(u0(t), a0(t)) be a state variable ω0(t) of the leading vehicle, and the extended state equation of the leading vehicle is given as follows:

[0096]

[0097] In the above formula, y0(t) is the output of the leading vehicle.

[0098] The specific expression for the extended state observer of the leading vehicle is given below:

[0099]

[0100] In the above formula, e0(t) is the estimated deviation of the position of the leading vehicle; This is the predicted value of the system state variable p0(t); This is the predicted value of the system state variable v0(t); This is the predicted value of the system state variable a0(t); The estimated value of the disturbance f0(u0(t), a0(t)). B0 is the compensation factor; β1, β2, β3, and β4 are observer parameters; δ is a nonlinear parameter; fal is a nonlinear function, and its specific expression is:

[0101]

[0102] Finally, step 3.1.3 gives the nonlinear state error feedback law u of the leading vehicle. 00 as follows:

[0103]

[0104] Where, β 10 ,β 20 ,β 30 U is the gain coefficient, δ is the nonlinear parameter, and u 00 (t) represents the actual control quantity of the system.

[0105] Step 3.2 Design of Active Disturbance Rejection Controller for Following Vehicle

[0106] Step 3.2.1 Use the tracking differentiator to calculate the reference position error e of the leading vehicle. i，ref，p =0 Arrange a suitable transition process Simultaneously output Differential signal and the second differential signal The specific expression is as follows:

[0107]

[0108] Step 3.2.2 Use ES0 to apply the lumped disturbance f of the following vehicle i (u i (t), a i (t), u i-1 (t), a i-1 Estimate (t)). First, let f i (u i (t), a i (t), u i-1 (t), a i-1 (t) is a state variable ω of the following vehicle. i The extended state equation for the following vehicle is given as follows: (t).

[0109]

[0110] The specific expression for the extended state observer following the vehicle is given below:

[0111]

[0112] In the above formula, e i (t) represents the estimated deviation of the position error; For system state variable e i，p The predicted value of (t); For system state variable e i，v The predicted value of (t); For system state variable e i，a The predicted value of (t); For the disturbance f i (u i (t), a i (t), u i-1 (t), a i-1 The estimated value of (t)); B i β1, β2, β3, and β4 are the compensation factors; δ is the observer parameter; and fal is the nonlinear function.

[0113] Finally, step 3.2.3 gives the nonlinear state error feedback law u for the following vehicle. i0 as follows:

[0114]

[0115] Where, β 10 ,β 20 ,β 30 U is the gain coefficient. i0 (t) represents the actual control quantity of the system.

[0116] This embodiment considers a vehicle convoy with one lead vehicle and five following vehicles. The vehicle's own parameter settings are as follows: Figure 2 As shown. The simulation time interval Δt = 0.001s, and the control input u of each vehicle is set. i The upper and lower limits are u min =-2, u max =2. The initial speed and initial acceleration of all vehicles are set to 0. The initial position of the vehicle at the rear of the convoy is set to 0, the expected spacing between adjacent vehicles is set to 15, and the initial positions of the remaining vehicles are calculated by a constant time spacing strategy, which are 25, 50, 75, 100, and 125 respectively. Thus, the initial spacing error of all vehicles is 0.

[0117] For the active disturbance rejection controller, the tracking differentiator parameter r0 = 1; the extended state observer parameters β1, β2, β3, and β4 are set to 80, 2400, 32000, and 160000 respectively; the nonlinear parameter δ = 0.01; and the compensation factor B. i =1; Nonlinear state error feedback parameter β 10 ,β 20 ,β 30 The values ​​are set to 1, 10, and 20 respectively. The driving route used in the simulation process is as follows: Figure 3 As shown.

[0118] Changes in the position, spacing error, speed, and control input of vehicles within the queue, such as Figure 4 As shown. See Figure 4 In Figure (d), different control inputs are generated at 3s and 50s. At these times, the speed and spacing errors of all vehicles change. However, as the control input decreases, the spacing error gradually decreases and approaches zero within a finite time, without amplifying along the vehicle queue. Furthermore, the speed of following vehicles reaches the same speed as the lead vehicle. Figure 4 Figures (b) and (c) show that the vehicles were all in their desired positions without any collisions. These results demonstrate that the proposed intelligent connected vehicle platooning control method based on distributed active disturbance rejection control can guarantee both the internal stability and the overall platoon stability.

[0119] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and these variations still fall within the protection scope of the present invention.

Claims

1. A method for intelligent connected vehicle platooning control based on distributed active disturbance rejection control, characterized in that: Includes the following steps: Step 1: Establish a connected vehicle platoon model, including the longitudinal dynamics model of the lead vehicle and the tracking error model of the following vehicles; Step 2: Select the control target; Step 3: Using active disturbance rejection control technology, design the tracking differentiator, the extended state observer, and the nonlinear state error feedback law respectively; Step three specifically includes: 3.

1. Design of Active Disturbance Rejection Controller for Lead Vehicle 3.1.1 Using a tracking differentiator to provide a reference trajectory for the leading vehicle Arrange a suitable transition process Simultaneously output Differential signal and second differential signal The specific expression is as follows: ； In the formula, For velocity factor; The fastest synthesis function is expressed as follows: ； in, ； 3.1.2 Using ESO to address lumped disturbances of the leading vehicle The estimation is performed, and the specific expression for the extended state observer of the leading vehicle is given as follows: ； In the above formula This represents the estimated deviation in the position of the lead vehicle; System state variables The predicted value; System state variables The predicted value; System state variables The predicted value; For disturbance The estimated value ; It is a compensating factor; These are the observer parameters. It is a nonlinear parameter; It is a nonlinear function, and its specific expression is: ； 3.1.3 Finally, the nonlinear state error feedback law for the leading vehicle is given. as follows: ； in, This is the gain coefficient. The actual control quantity of the system. 3.

2. Design of Active Disturbance Rejection Controller for Following Vehicles 3.2.1 Using a tracking differentiator to determine the reference position error of the leading vehicle Arrange a suitable transition process Simultaneously output Differential signal and second differential signal The specific expression is as follows: ； 3.2.2 Using ESO to address lumped disturbances of following vehicles An estimation is performed; the specific expression for the extended state observer of the following vehicle is given as follows: ； In the above formula This represents the estimation bias of the position error; System state variables The predicted value; System state variables The predicted value; System state variables The predicted value; For disturbance The estimated value ; 3.2.3 gives the nonlinear state error feedback law for the following vehicle. as follows: ； in, This is the gain coefficient. This refers to the actual control quantity of the system.

2. The intelligent connected vehicle platooning control method based on distributed active disturbance rejection control according to claim 1, characterized in that: Step one specifically includes: The vehicles in the formation will be arranged from front to back. The vehicles are numbered, with vehicle 0 being the lead vehicle in the convoy, and the rest... The vehicle is a following vehicle, and its longitudinal dynamics model is established as follows: ； in, These are the vehicle's position, speed, and acceleration, respectively. It is the engine time lag constant, taken as The intermediate value is denoted as a constant. ; The lumped disturbance of the vehicle is denoted as the control input used to set the desired acceleration. It can be expressed as follows: ； The final longitudinal dynamics model of the vehicle is as follows: ； in, For the adjustable parameters to be designed, This is the control input for the active disturbance rejection controller; System control input It can be obtained from the following formula, Define the vehicle position tracking error, speed tracking error, and acceleration tracking error as follows: ； in This represents the desired distance between adjacent vehicles. It is a vehicle Given the length of the vehicle, the goal of following the vehicle is to minimize the tracking error in the vehicle formation. The tracking error model is established as follows: 。 3. The intelligent connected vehicle platooning control method based on distributed active disturbance rejection control according to claim 1 or 2, characterized in that: Step two specifically includes: 2.

1. Internal stability For all The error dynamics of vehicle formation are asymptotically stable, i.e., they satisfy: ； 2.

2. The string stability condition is: 。

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