Intelligent network connection fleet robust control method considering communication delay and external interference

Abstract

The invention provides an intelligent network connection fleet robust control method considering communication delay and external interference. The method comprises the following steps: constructing a hybrid traffic system dynamics model; the mixed traffic flow is divided into a plurality of sub-motorcades, the control problem is simplified, and a time delay system and a closed-loop system of a pair of sub-motorcades are constructed; solving constraint conditions meeting the hybrid traffic flow robust stability and the fleet stability; iteration solution is carried out on an intelligent network connection motorcade robust controller, a control gain matrix is obtained, mixed traffic flow longitudinal track optimization control is achieved according to a time delay system in combination with control input, and the stability and robustness of a motorcade system are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of hybrid traffic flow trajectory control, and in particular, to a robust control method for intelligent connected vehicle platoons considering communication delay and external interference. Background Art

[0002] In a mixed traffic environment involving intelligent connected vehicles and human-driven vehicles, the interaction between vehicles and the uncertainty of human drivers may lead to traffic congestion or unstable traffic flow fluctuations. By designing a robust controller for intelligent connected vehicle platoons, the unstable phenomena of traffic flow caused by external interferences (such as sudden deceleration of vehicles, communication delay, etc.) can be effectively suppressed. Although existing studies have proposed different methods to improve the stability of hybrid traffic flow on urban roads, most methods lack the randomness of the distribution of intelligent connected vehicles and human-driven vehicles in the actual driving environment and the time delay of information transmission between vehicles. Summary of the Invention

[0003] The present invention provides a robust control method for intelligent connected vehicle platoons considering communication delay and external interference for random heterogeneous traffic flow, aiming to effectively suppress the influence of interference on hybrid traffic flow and improve the robustness and stability of hybrid traffic flow in the scenario of communication delay and random distribution of intelligent connected vehicles.

[0004] To reduce the complexity of the control problem, the random heterogeneous traffic flow is divided into multiple sub-platoons based on the sequence of intelligent connected vehicles in the platoon, assuming that each vehicle in the sub-platoon has the same speed and acceleration. The optimal speed model and the cooperative adaptive cruise control system are respectively applied to the design of the robust controller for intelligent connected vehicle platoons to describe the car-following behavior of human-driven vehicles and intelligent connected vehicles, and the acceleration change of human-driven vehicles is regarded as an external disturbance. Secondly, a robust controller for intelligent connected vehicle platoons is designed for a pair of following sub-platoons in the hybrid platoon system. On this basis, the time-delay system and the closed-loop system are modeled, and an H ∞ state feedback controller is designed. Finally, the robust stability and platoon stability of the hybrid platoon system are analyzed by using the H ∞ norm of the closed-loop transfer function and the delay bounded real lemma respectively. The robust controller can effectively reduce traffic oscillations in hybrid traffic flow and can adapt to different market penetration rates of intelligent connected vehicles under different settings of communication delay and external interference.

[0005] The technical means adopted by the present invention are as follows:

[0006] The present invention provides a robust control method for intelligent connected vehicle platoons considering communication delay and external interference, including:

[0007] Divide the mixed traffic flow into at least two sub - fleets, and construct the time - delay system and closed - loop system for any pair of sub - fleets based on the dynamic model of the mixed traffic system;

[0008] Solve the constraint conditions that satisfy the robust stability of the mixed traffic flow and the stability of the fleet based on the time - delay system and the closed - loop system to obtain a state - feedback controller;

[0009] Iteratively solve the state - feedback controller through the external disturbance signal and communication delay of the mixed traffic flow to obtain a control gain matrix;

[0010] Update the state equation in the time - delay system according to the control gain matrix, and optimize the longitudinal trajectory of the mixed traffic flow based on the updated state equation.

[0011] Optionally, before the step of dividing the mixed traffic flow into at least two sub - fleets and constructing the time - delay system and closed - loop system for any pair of sub - fleets based on the dynamic model of the mixed traffic system, it further includes:

[0012] Construct the car - following behavior equation of human - driven vehicles through the optimal speed model:

[0013]

[0014] where, a i (t) represents acceleration; represents the deviation from the equilibrium spacing; represents the deviation from the equilibrium speed; represents the difference between the (i - 1) - th human - driven vehicle and the equilibrium speed v * ; i represents the vehicle index, k represents the sub - fleet index; I k is the set of sub - fleets, η 1 , η 2 and η 3 are the weights regarding the deviation from the equilibrium speed, deviation from the equilibrium spacing, and speed difference of human - driven vehicles respectively.

[0015] Construct the car - following behavior equation of connected and autonomous vehicles through the cooperative adaptive cruise control system:

[0016]

[0017] where, u i (t) represents the control input; represents the difference between the i - th connected and autonomous vehicle and the equilibrium spacing z * ; represents the difference between the i - th connected and autonomous vehicle and the equilibrium speed v * ; represents the difference between the (i - 1) - th connected and autonomous vehicle and the equilibrium speed v* The difference, δ 1 , δ 2 and δ 3 are the weights for the deviation from the balanced speed, the deviation from the balanced spacing, and the speed difference of the connected and automated vehicle, respectively;

[0018] The dynamic model of the mixed traffic system is constructed based on the car-following behavior equation of the human-driven vehicle and the car-following behavior equation of the connected and automated vehicle.

[0019] Optionally, the mathematical representation of the time-delay system is:

[0020]

[0021] where x(t) represents the system state vector of a pair of following sub-traffic flows; represents the derivative of the system state vector at time t; w(t) represents the acceleration change of the human-driven following traffic flow, i.e., the external disturbance; z(t) represents the controlled output of the time-delay system; y(t) represents the control output of the time-delay system; A is the coefficient matrix of the system state vector in the state equation of the time-delay system; A τ is the coefficient matrix of the system state vector with communication delay in the state equation; B is the coefficient matrix of the system control input in the state equation; B w is the coefficient matrix of the system external disturbance in the state equation; C z is the coefficient matrix of the system state vector in the controlled output equation of the time-delay system; D z is the coefficient matrix of the system control input in the controlled output equation; C is the coefficient matrix of the system state vector in the control output equation of the time-delay system.

[0022] Optionally, the mathematical representation of the coefficient matrix A of the system state vector is:

[0023]

[0024] The coefficient matrix A of the system state vector τ has the mathematical representation of:

[0025]

[0026] The mathematical representation of the coefficient matrix B is:

[0027]

[0028] where T c is the safe headway time;

[0029] The coefficient matrix B w has the mathematical representation of:

[0030]

[0031] The coefficient matrix C z and the coefficient matrix D z are mathematically represented as:

[0032]

[0033] where C z = [μ z , μ v , μ z , μ v , D z = μ u , and the coefficients μ z , μ v and μ u represent the spacing error, speed error, and weight of the control input, respectively.

[0034] The mathematical representation of the coefficient matrix C is:

[0035]

[0036] Optionally, the mathematical representation of the closed-loop system is:

[0037]

[0038] where is the coefficient matrix of the system state vector with communication delay in the state equation of the closed-loop system; is the coefficient matrix of the system state vector with communication delay in the controlled output equation of the closed-loop system.

[0039] Optionally, the mathematical representation of the constraint conditions for the robust stability of the mixed traffic flow is:

[0040]

[0041] where, ε and γ > 0 are both given constants; Q 1 , and represent symmetric positive definite matrices; Q 2 , Q 3 and Y represent ordinary matrices.

[0042] Optionally, the mathematical representation of the constraint conditions for the platoon stability of the mixed traffic flow is:

[0043]

[0044] where, η1 , η 2 and η 3 are the weights of the deviation equilibrium speed, deviation equilibrium spacing, and speed difference of the human-driven vehicle following the platoon, respectively. If the above constraints are satisfied, the described human-driven vehicle following platoon system is stable.

[0045]

[0046] wherein, δ 1 , δ 2 and δ 3 are the weights of the deviation equilibrium speed, deviation equilibrium spacing, and speed difference of the connected and automated vehicle following the platoon, respectively.

[0047] Optionally, the step of updating the state equation in the time-delay system according to the control gain matrix and performing optimal control on the longitudinal trajectory of the mixed traffic flow based on the updated state equation includes:

[0048] For the connected and automated leading platoon I in the entire platoon 1 , it is set that the connected and automated leading platoon I 1 always travels at the desired spacing z * and the desired speed v * , then the quadratic interpolation method is used to process the communication delay to obtain the communication delay signal;

[0049] Within a preset time, the acceleration change of the human-driven vehicle is used as an external disturbance signal;

[0050] According to the communication delay signal, external disturbance signal, and the control gain matrix, the state equation in the time-delay system is updated by iterative solution. Specifically, the state vector and control input in the state equation are updated to perform optimal control on the longitudinal trajectory of the mixed traffic flow.

[0051] Compared with the prior art, the present invention has the following advantages:

[0052] The present invention divides the mixed traffic flow into at least two sub-platoons, constructs a time-delay system and a closed-loop system for any pair of sub-platoons based on the dynamic model of the mixed traffic system; solves the constraint conditions for the robust stability of the mixed traffic flow and the stability of the platoon based on the time-delay system and the closed-loop system to obtain a state feedback controller; iteratively solves the state feedback controller through the external disturbance signal and communication delay of the mixed traffic flow to obtain a control gain matrix; updates the state equation in the time-delay system according to the control gain matrix and performs optimal control on the longitudinal trajectory of the mixed traffic flow. It effectively reduces traffic oscillations in the mixed traffic flow and can adapt to different market penetration rates of connected and automated vehicles under different settings of communication delays and external disturbances. Description of the Drawings

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0054] Figure 1 It is a flowchart of a robust control method for an intelligent connected vehicle fleet considering communication delay and external interference provided by an embodiment of the present application;

[0055] Figure 2 It is a control vehicle trajectory change diagram of the robust control method for an intelligent connected vehicle fleet considering communication delay and external interference provided by an embodiment of the present application in a scenario;

[0056] Figure 3 It is a vehicle trajectory change diagram of the robust control method for an intelligent connected vehicle fleet considering communication delay and external interference provided by an embodiment of the present application in another scenario;

[0057] Figure 4 It is a vehicle trajectory change diagram of the robust control method for an intelligent connected vehicle fleet considering communication delay and external interference provided by an embodiment of the present application in yet another scenario;

[0058] Figure 5 It is a vehicle trajectory change diagram of the robust control method for an intelligent connected vehicle fleet considering communication delay and external interference provided by an embodiment of the present application in still another scenario. Detailed Embodiments

[0059] In order to enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0060] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances, 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 inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0061] Referring to Figure 1 , the intelligent connected vehicle fleet robust control method considering communication delay and external interference provided by the present invention includes:

[0062] S101. Divide the mixed traffic flow into at least two sub-fleets, and construct a time-delay system and a closed-loop system for any pair of sub-fleets based on the mixed traffic system dynamics model;

[0063] S102. Solve the constraint conditions for the robust stability of the mixed traffic flow and the stability of the fleet based on the time-delay system and the closed-loop system to obtain a state feedback controller;

[0064] S103. Iteratively solve the state feedback controller through the external interference signal and communication delay of the mixed traffic flow to obtain a control gain matrix;

[0065] S104. Update the state equation in the time-delay system according to the control gain matrix, and perform optimal control on the longitudinal trajectory of the mixed traffic flow based on the updated state equation.

[0066] It can be understood that the intelligent connected vehicle fleet robust control method considering communication delay and external interference provided in this embodiment further considers the communication delay between vehicles and external disturbances through the mixed traffic system dynamics model; divides the random mixed traffic flow into multiple sub-fleets based on the sequence of intelligent connected vehicles in the fleet, constructs a time-delay system and a closed-loop system for a pair of following sub-fleets, and simplifies the complex random mixed traffic flow control problem into multiple sub-fleet control problems; solves the constraint conditions for the robust stability of the mixed traffic flow and the stability of the fleet; obtains the control input through the control gain matrix, and combines the state space equation in the time-delay system to achieve the longitudinal trajectory stability control of the mixed traffic flow. It effectively reduces traffic oscillations in the mixed traffic flow, and can adapt to different market penetration rates of intelligent connected vehicles under different settings of communication delay and external interference.

[0067] In some alternative embodiments, a dynamic model of the hybrid traffic system is constructed and calculated as follows:

[0068] The car-following behavior of human-driven vehicles is simulated using the optimal velocity model:

[0069]

[0070] where a i (t) represents the acceleration of the i-th human-driven vehicle; represents the difference between the i-th human-driven vehicle and the equilibrium spacing z * , that is, the deviation from the equilibrium spacing; represents the difference between the i-th human-driven vehicle and the equilibrium speed v * , that is, the deviation from the equilibrium speed; represents the difference between the (i - 1)-th human-driven vehicle and the equilibrium speed v * . i and k respectively represent the indices of vehicles and sub-teams; I k is the set of sub-teams. η 1 , η 2 and η 3 are the weights regarding the deviation from the equilibrium speed, the deviation from the equilibrium spacing, and the speed difference of human-driven vehicles respectively.

[0071] The car-following behavior of connected and autonomous vehicles is simulated using the cooperative adaptive cruise control system:

[0072]

[0073] where u i (t) represents the acceleration of the i-th connected and autonomous vehicle, that is, the control input; represents the difference between the i-th connected and autonomous vehicle and the equilibrium spacing z * , that is, the deviation from the equilibrium spacing; represents the difference between the i-th connected and autonomous vehicle and the equilibrium speed v * , that is, the deviation from the equilibrium speed; represents the difference between the (i - 1)-th connected and autonomous vehicle and the equilibrium speed v * . δ 1 , δ 2 and δ 3 are the weights regarding the deviation from the equilibrium speed, the deviation from the equilibrium spacing, and the speed difference of connected and autonomous vehicles respectively.

[0074] It can be understood that in this embodiment, the optimal velocity model and the cooperative adaptive cruise control system are modeled as dynamic models related to the state vector . represents the deviation from the equilibrium spacing of the i-th vehicle, represents the deviation from the equilibrium speed of the i-th vehicle.

[0075] In some optional embodiments, the mixed traffic flow is divided into multiple sub-convoys, the control problem is simplified, and a pair of time-delay systems and closed-loop systems following the sub-convoys are constructed, and the calculation is performed in the following manner:

[0076]

[0077] Among them, a 2 π(t) represents the human driver following the convoy I 2 π expected acceleration; Indicates that human drivers follow the convoy I 2π With equilibrium spacing z * The difference between , i.e. the deviation from the equilibrium spacing; Indicates that human drivers follow the convoy I 2π With equilibrium speed v * The difference is the deviation from equilibrium velocity; Indicates intelligent network following the team I 2π-1 With equilibrium speed v * The difference between (when π is 1, it is the intelligent network pilot fleet I 1 );a 2 π +1 (t) represents the intelligent network following the team I 2 π +1 The desired acceleration, i.e., the control input u(t); Indicates intelligent network following the team I 2π+1 With equilibrium spacing z * The difference between , i.e. the deviation from the equilibrium spacing; Indicates intelligent network following the team I 2π+1 With equilibrium speed v * The difference between τ and τ is the deviation from the equilibrium speed. τ represents the communication delay between the sub-teams. is the maximum communication delay, which is a known positive integer. Define the intelligent connected following fleet I with communication delay 2π+1 and human driving following convoy car I 2π The state set vector is as follows:

[0078]

[0079] Among them, x 2π (t-τ) represents the system state vector of the human driver following the convoy with communication delay; x 2π+1 (t-τ) represents the system state vector of the intelligent connected following vehicle fleet with communication delay; x(t-τ) represents the system state vector of a pair of following sub-vehicle fleets with communication delay. The time-delay system is calculated as follows:

[0080]

[0081] Among them, \(x(t)\) represents a pair of system state vectors following the platoon; represents the derivative of the system state vector at time \(t\); \(w(t)\) represents the acceleration change of the human-driven vehicle following the platoon, i.e., the external disturbance; \(z(t)\) represents the regulated output of the time-delay system; \(y(t)\) represents the control output of the time-delay system. represents the state equation, which is used to describe the dynamic change law of the system state over time.

[0082] \(A\) is the coefficient matrix of the system state vector in the state equation; \(A\) τ is the coefficient matrix of the system state vector with communication delay in the state equation; \(B\) is the coefficient matrix of the system control input in the state equation; \(B\) w is the coefficient matrix of the system external disturbance in the state equation; \(C\) z is the coefficient matrix of the system state vector in the regulated output equation; \(D\) z is the coefficient matrix of the system control input in the regulated output equation; \(C\) is the coefficient matrix of the system state vector in the control output equation.

[0083] In some alternative embodiments, the coefficient matrix \(A\) of the system state vector with communication delay in the state equation is calculated as follows:

[0084]

[0085] The coefficient matrix \(A\) of the system state vector with communication delay in the state equation τ is calculated as follows:

[0086]

[0087] The coefficient matrix \(B\) of the system control input in the state equation is calculated as follows:

[0088]

[0089] where \(T\) c is the safe headway.

[0090] The coefficient matrix \(B\) of the system external disturbance in the state equation w is calculated as follows:

[0091]

[0092] The coefficient matrix \(C\) of the system state vector in the regulated output equation z and the coefficient matrix \(D\) of the system control input z are calculated as follows:

[0093]

[0094] where C z =[μ z , μ v , μ z , μ v , D z =μ u . The coefficients μ z , μ v and μ u represent the spacing error, speed error, and weight of the control input, respectively.

[0095] The coefficient matrix C of the system state vector in the control output equation is calculated as follows:

[0096]

[0097] In some alternative embodiments, a pair of closed-loop systems following a sub-fleet are constructed, with the control input as the state output feedback controller, and calculated as follows:

[0098] u(t) = Ky(t - τ) = KCx(t - τ)

[0099] where K represents the control gain, and u(t) = Ky(t - τ) is the designed state output feedback controller. Substituting the state output feedback controller into the time-delay system, the closed-loop system can be obtained:

[0100]

[0101] where is the coefficient matrix of the system state vector with communication delay in the state equation of the closed-loop system; is the coefficient matrix of the system state vector with communication delay in the controlled output equation of the closed-loop system.

[0102] It can be understood that constructing the time-delay system aims to find a state output feedback controller u(t) such that the time-delay system can minimize the influence of the disturbance w(t) on the controlled output z(t). Substituting the state output feedback controller into the time-delay system, the closed-loop system can be obtained.

[0103] In some alternative embodiments, the constraints for satisfying the robust stability of the mixed traffic flow are solved and calculated as follows:

[0104]

[0105] where, ε and γ > 0 are both given constants; Q 1 , and represent symmetric positive definite matrices; Q 2 , Q 3 and Y represent ordinary matrices. The state feedback controller with H ∞ performance γ can be obtained by satisfying the above constraints as Then the control gain matrix is

[0106] It can be understood that if there exists a given constant γ > 0 such that the closed-loop system is asymptotically stable and the H ∞ norm of the transfer function from the disturbance input w(t) to the regulated output z(t) does not exceed γ, it means that the closed-loop system has H ∞ performance γ. The smaller the H ∞ norm of the system, the stronger the ability of the system to suppress external disturbances and the better the performance of the system.

[0107] In some alternative embodiments, to solve the constraints for the stability of the platoon in mixed traffic flow, it is calculated in the following manner:

[0108]

[0109] where η 1 , η 2 and η 3 are the weights for the deviation from the equilibrium speed, the deviation from the equilibrium spacing, and the speed difference of the human-driven vehicle following the platoon, respectively. If the above constraints are satisfied, the human-driven vehicle following platoon system is stable.

[0110]

[0111] where δ 1 , δ 2 and δ 3 are the weights for the deviation from the equilibrium speed, the deviation from the equilibrium spacing, and the speed difference of the connected and automated vehicle following the platoon, respectively. If the above constraints are satisfied, the connected and automated vehicle following platoon system is stable.

[0112] In some alternative embodiments, the robust controller of the connected and automated vehicle platoon is iteratively solved to obtain the control gain matrix, and the longitudinal trajectory optimization control of the mixed traffic flow is realized by combining the control input according to the time-delay system, which is calculated in the following manner:

[0113] By solving the constraints for the platoon stability, the parameters η d (d = 1, 2, 3) and δ d (d = 1, 2, 3) can be obtained. Further solving the constraints for the robust stability can obtain the control gain matrix K. For the connected and automated leading vehicle platoon I in the whole platoon 1, set it to always travel at the desired spacing z * and the desired speed v * .

[0114] In the simulation time domain, the quadratic interpolation method is used to process the communication delay τ. Within the specified time, the acceleration change of the human-driven vehicle is added as the external disturbance w(t). By continuously iteratively solving, the state vectors x k (t) and x k (t - τ) as well as the control input u(t) are updated. Substitute the updated state vectors and the control input into the state space equation to achieve the longitudinal trajectory optimization control of the mixed traffic flow.

[0115] In practical applications, obtain the state data of the existing vehicle fleet, including key parameters such as the initial speed, spacing, acceleration of the vehicles, and the communication delay between the vehicles. Input the state data into the designed robust controller for the intelligent connected vehicle fleet (state output feedback controller, u(t) = YQ 1 -1 Cx(t)). The robust controller for the intelligent connected vehicle fleet analyzes the dynamic behavior of the vehicle fleet based on the input data and the established time-delay system model, combined with the real-time changes of the external disturbance and the communication delay, and calculates and generates the optimized control input through the state feedback control method. Subsequently, the controller outputs specific vehicle fleet control instructions, including the target speed, spacing adjustment, and acceleration or deceleration schemes for each vehicle. Finally, apply the control instructions to the mixed vehicle fleet to dynamically adjust the driving trajectory of the intelligent connected vehicle fleet, thereby achieving the longitudinal trajectory optimization control of the vehicle fleet.

[0116] In some alternative embodiments, referring to Figure 2 , Figure 3 , Figure 4 and Figure 5 , Figure 2 is the vehicle trajectory change diagram when the communication delay is 1 s, the acceleration change is –2 m / s 2 , and the market penetration rate of intelligent connected vehicles is 50%; Figure 3 is the vehicle trajectory change diagram when the communication delay is 2 s, the acceleration change is –2 m / s 2 , and the market penetration rate of intelligent connected vehicles is 50%; Figure 4 is the vehicle trajectory change diagram when the communication delay is 1 s, the acceleration changes are –2 m / s 2 and –3 m / s 2 , and the market penetration rate of intelligent connected vehicles is 50%; Figure 5 is the vehicle trajectory change diagram when the communication delay is 1 s, the acceleration change is –2 m / s 2 , and the market penetration rate of intelligent connected vehicles is 80%.

[0117] Taking 4 simulation scenarios as examples, refer to Figure 2 , Figure 3 , Figure 4 and Figure 5 to compare the control performance of the robust controller of the intelligent connected vehicle fleet under different scenarios.

[0118] Scenario design, including:

[0119] The simulation duration is 200 s, and the time interval Δt = 0.1 s. Set the initial speed and desired speed of the vehicle to be v(0) = v * = 12 m / s. Set the initial spacing and desired spacing to be z(0) = z * = 50 m. The maximum speed is v max = 15 m / s, and the minimum speed is v min = 7 m / s. The maximum acceleration and minimum acceleration are a max = 2 m / s 2 and a min = –5 m / s 2 . The safe headway is T c = 2 s. Set the response degree of the vehicle to speed to be α = 1, β = 1. The weights of the control inputs are taken as μ z = 0.5, μ v = 0.5, μ u = 1. The vehicle length is taken as 4 m. The spacing when the vehicle is stationary is taken as 2 m. The total number of vehicles is 10. The market penetration rates of intelligent connected vehicles are 50% and 80% respectively. The communication delays are set to τ = 1 s and τ = 2 s respectively. The disturbance range of the acceleration of human-driven vehicles is –2 m / s 2 and –3 m / s 2 . Similar designs can be achieved without increasing the difficulty. To reflect the randomness of the arrival of intelligent connected vehicles, a mixed traffic flow with random arrivals is designed. The intelligent connected leading vehicle fleet I 1 and the human-driven following vehicle fleet I 2 are each composed of 1 vehicle. For other following vehicles, a Poisson distribution is used to generate a random vehicle sequence, namely Pois(5) at a 50% market penetration rate and Pois(8) at an 80% market penetration rate.

[0120] Result analysis, including:

[0121] Refer to Figures 2 to 5, human-driven vehicles and connected and automated vehicles are distinguished by solid and dashed lines. The red solid line represents the disturbed human-driven vehicle, i.e., the first human-driven vehicle in the whole vehicle platoon, and the orange solid line represents the second human-driven vehicle in the whole vehicle platoon. The gray solid line represents other human-driven vehicles in the vehicle platoon. Except that the second connected and automated vehicle in the whole vehicle platoon is represented by a green dashed line, the remaining blue dashed lines represent other connected and automated vehicles in the vehicle platoon. The legend shows the vehicle sequence of the mixed vehicle platoon. The numbers outside the brackets represent the numbers of all vehicles in the mixed vehicle platoon, and the numbers inside the brackets separately represent the numbers of human-driven vehicles and connected and automated vehicles in the whole mixed vehicle platoon. The trajectories of the first connected and automated vehicle and the first human-driven vehicle are predefined. The first connected and automated vehicle in the whole vehicle platoon always travels at the desired speed, and the subsequent human-driven vehicles follow the vehicle in front using the optimal speed model, and the disturbance settings are predefined. Figures 2 to 5 The vertical coordinates in subfigures (a) to (b) represent speed and acceleration respectively, and the horizontal coordinate is time. The maximum and minimum values of the speed and acceleration of the second human-driven vehicle and the second connected and automated vehicle in the whole mixed vehicle platoon are marked with orange and green numerical values. The time when the speed and acceleration are stable is marked as the bold numbers on the horizontal axis of subfigures (a) and (b).

[0122] Scenario 1 aims to demonstrate the controller performance with a communication delay τ = 1 s and an acceleration disturbance of –2 m / s 2 Its speed and acceleration changes are as Figure 2 shown. Since it is set that the first human-driven vehicle has an acceleration fluctuation of –2 m / s 2 from the 11th s to the 15th s, the speeds and accelerations of the subsequent human-driven vehicles and connected and automated vehicles have fluctuated to varying degrees. It can be found from Figure 2 that the deviation of the equilibrium speed and acceleration of the second connected and automated vehicle is less than that of the second human-driven vehicle, indicating that the connected and automated vehicle can stabilize external disturbances. The whole vehicle platoon reaches speed stability and acceleration stability at 81.80 s and 54.90 s respectively, indicating that the proposed controller can stabilize the mixed traffic flow to the equilibrium speed within a certain time.

[0123] Scenario 2 aims to demonstrate the controller performance under different communication delays, and its speed and acceleration changes are in Figure 3Shown in the figure. Comparing Scenario 2 with Scenario 1, the second human-driven vehicle in the vehicle platoon follows a connected and automated vehicle (CAV) instead of a human-driven vehicle. Therefore, the speed and acceleration fluctuations of the following vehicles in Scenario 2 are much smaller than those in Scenario 1. Compared with Scenario 1, the time for the vehicle platoon speed and acceleration to tend to be stable is faster in Scenario 2 because the vehicle following the disturbed human-driven vehicle in Scenario 2 is a CAV, which further demonstrates the ability of CAVs to rapidly attenuate disturbances and stabilize traffic flow. This observation verifies that CAVs can stabilize traffic flow and reduce the adverse effects of disturbances on human-driven vehicles in the subsequent vehicle platoon.

[0124] Scenario 3 aims to show the controller performance under different disturbance ranges, and its speed and acceleration changes are as Figure 4 shown. Due to the increase in the frequency and amplitude of the disturbances, compared with Scenario 1, the time for Scenario 3 to tend to be stable is relatively longer. However, in Scenario 3, the mixed traffic flow can eventually reach equilibrium, indicating that even with the increase in disturbance frequency and amplitude, the controller can effectively stabilize the heterogeneous traffic flow to the equilibrium speed.

[0125] Scenario 4 aims to show the controller performance under a relatively high market penetration rate of CAVs, and its speed and acceleration changes are as Figure 5 shown. It can be observed from the figure that when the market penetration rate of CAVs increases, the speed and acceleration fluctuations of human-driven vehicles decrease, and the time for the speed and acceleration of the mixed traffic flow to reach stability is also faster. Compared with the 50% market penetration rate of CAVs in Scenario 1, the proposed controller in Scenario 4 can rapidly stabilize the heterogeneous traffic flow under an 80% market penetration rate of CAVs.

[0126] In one embodiment, a computer device is provided. The computer device can be a server, and it includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client through a network connection. When the computer program is executed by the processor, it realizes the functions or steps of the server side of a robust control method for a connected and automated vehicle platoon considering communication delay and external interference.

[0127] In one embodiment, a computer device is provided. The computer device can be a client. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server through a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the client side of an intelligent connected vehicle platoon robust control method considering communication delay and external interference.

[0128] In one embodiment, a computer device is proposed, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented: dividing the mixed traffic flow into at least two sub-platoons, constructing a time-delay system and a closed-loop system for any pair of sub-platoons based on the mixed traffic system dynamics model; solving the constraint conditions that satisfy the robust stability of the mixed traffic flow and the platoon stability based on the time-delay system and the closed-loop system to obtain a state feedback controller; iteratively solving the state feedback controller through the external interference signal and communication delay of the mixed traffic flow to obtain a control gain matrix; updating the state equation in the time-delay system according to the control gain matrix, and optimizing the longitudinal trajectory of the mixed traffic flow based on the updated state equation.

[0129] In one embodiment, a computer-readable storage medium is proposed. The computer-readable storage medium stores a computer program. When the computer program is executed by the processor, the following steps are implemented: dividing the mixed traffic flow into at least two sub-platoons, constructing a time-delay system and a closed-loop system for any pair of sub-platoons based on the mixed traffic system dynamics model; solving the constraint conditions that satisfy the robust stability of the mixed traffic flow and the platoon stability based on the time-delay system and the closed-loop system to obtain a state feedback controller; iteratively solving the state feedback controller through the external interference signal and communication delay of the mixed traffic flow to obtain a control gain matrix; updating the state equation in the time-delay system according to the control gain matrix, and optimizing the longitudinal trajectory of the mixed traffic flow based on the updated state equation.

[0130] It should be noted that for the functions or steps that the above computer-readable storage medium or computer device can achieve, reference can be made to the relevant descriptions on the server side and the client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0131] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0132] Those skilled in the art can clearly understand that for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0133] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

[0134] 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.

[0135] In the above embodiments of the present invention, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0136] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and 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 robust control method for an intelligent connected vehicle fleet considering communication delay and external interference, characterized in that: include: Divide the mixed traffic flow into at least two sub-convoys, and construct the time-delay system and closed-loop system of any pair of sub-convoys based on the mixed traffic system dynamics model; Based on the time-delay system and the closed-loop system, constraints satisfying the robust stability of mixed traffic flow and the stability of the convoy are solved to obtain a state feedback controller; Iteratively solving the state feedback controller through the external interference signal and communication delay of the mixed traffic flow to obtain a control gain matrix; The state equation in the time-delay system is updated according to the control gain matrix, and the longitudinal trajectory of the mixed traffic flow is optimally controlled based on the updated state equation.

2. The method for robust control of an intelligent connected vehicle fleet considering communication delay and external interference according to claim 1 is characterized in that: Before the step of dividing the mixed traffic flow into at least two sub-convoys and constructing a time-delay system and a closed-loop system for any pair of sub-convoys based on the mixed traffic system dynamics model, the method further includes: The optimal speed model is used to construct the following behavior equation of human-driven vehicles: Among them, a i (t) represents acceleration; Indicates deviation from the equilibrium spacing; represents the deviation from equilibrium speed; represents the i-1th human-driven vehicle and the equilibrium speed v * The difference between the two; i represents the index of the vehicle, k represents the index of the sub-team; I k is the set of sub-vehicles, η1, η2 and η3 are the weights of the deviation from equilibrium speed, deviation from equilibrium distance and speed difference of human-driven vehicles, respectively. The following behavior equation of intelligent connected vehicles is constructed through a cooperative adaptive cruise control system: Among them, u i (t) represents the control input; represents the distance between the i-th intelligent connected vehicle and the equilibrium distance z * The difference between represents the i-th intelligent connected vehicle and the equilibrium speed v * The difference between represents the i-1th intelligent connected vehicle and the equilibrium speed v * , δ1, δ2 and δ3 are the weights of the deviation from equilibrium speed, deviation from equilibrium distance and speed difference of the intelligent connected vehicle respectively; The hybrid traffic system dynamics model is constructed according to the car-following behavior equation of the human-driven vehicle and the car-following behavior equation of the intelligent networked vehicle.

3. The method for robust control of an intelligent connected vehicle fleet considering communication delay and external interference according to claim 1 is characterized in that: The mathematical representation of the time-delay system is: Where x(t) represents the system state vector of a pair of following sub-convoys; represents the derivative of the state vector of the system at time t; w(t) represents the acceleration change of the human driver following the convoy, that is, the external disturbance; z(t) represents the regulated output of the time-delay system; y(t) represents the control output of the time-delay system; A is the coefficient matrix of the system state vector in the state equation of the time-delay system; A τ is the coefficient matrix of the system state vector containing communication delay in the state equation; B is the coefficient matrix of the system control input in the state equation; B w is the coefficient matrix of the external disturbance of the system in the state equation; C z is the coefficient matrix of the system state vector in the regulated output equation of the time-delay system; D z is the coefficient matrix of the system control input in the regulated output equation; C is the coefficient matrix of the system state vector in the control output equation of the time-delay system.

4. The method for robust control of an intelligent connected vehicle fleet considering communication delay and external interference according to claim 3 is characterized in that: The coefficient matrix A of the system state vector is mathematically represented as: The coefficient matrix A of the system state vector τ The mathematical representation of is: The mathematical representation of the coefficient matrix B is: Where T c It is the safe headway time; The coefficient matrix B w The mathematical representation of is: The coefficient matrix C z And the coefficient matrix D z The mathematical representation of is: Among them C z =[μ z ,μ v ,μ z ,μ v ], D z =μ u , coefficient μ z , μ v and μ u Represent the distance error, speed error and the weight of control input respectively. The mathematical representation of the coefficient matrix C is:

5. The method for robust control of an intelligent connected vehicle fleet considering communication delay and external interference according to claim 2 is characterized in that: The mathematical representation of the closed-loop system is: in is the coefficient matrix of the system state vector containing the communication delay in the state equation of the closed-loop system; is the coefficient matrix of the system state vector containing communication delay in the regulated output equation of the closed-loop system.

6. The method for robust control of an intelligent connected vehicle fleet considering communication delay and external interference according to claim 1, characterized in that: The mathematical representation of the constraints of the mixed traffic flow robust stability is: in, ε and γ>0 are given constants; Q1, and represents a symmetric positive definite matrix; Q2, Q3, and Y represent ordinary matrices.

7. The method for robust control of an intelligent connected vehicle fleet considering communication delay and external interference according to claim 1, characterized in that: The mathematical representation of the constraints on the stability of the mixed traffic flow fleet is: Wherein, η1, η2 and η3 are weights of the deviation from equilibrium speed, deviation from equilibrium distance and speed difference of the human-driven following convoy, respectively. If the above constraints are met, the human-driven following convoy system is stable. Among them, δ1, δ2 and δ3 are the weights of the deviation from equilibrium speed, deviation from equilibrium distance and speed difference of the intelligent connected following vehicle fleet, respectively.

8. The method for robust control of an intelligent connected vehicle fleet considering communication delay and external interference according to claim 1, characterized in that: The step of updating the state equation in the time-delay system according to the control gain matrix and optimizing the longitudinal trajectory of the mixed traffic flow based on the updated state equation comprises: For the intelligent network pilot fleet I1 in the entire fleet, it is assumed that the intelligent network pilot fleet I1 always has the expected distance z * and the expected speed v * When driving, the communication delay is processed by the quadratic interpolation method to obtain the communication delay signal; Within a preset time, the acceleration change of the human-driven vehicle is used as an external disturbance signal; The state equation in the time-delay system is updated by iterative solution according to the communication delay signal, the external disturbance signal and the control gain matrix. Specifically, the state vector and the control input in the state equation are updated to optimize the longitudinal trajectory of the mixed traffic flow.

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