Intelligent connected hybrid driving vehicle cooperative control method

Abstract

The application discloses a kind of intelligent network connection hybrid driving vehicle cooperative control method, comprising the following steps: step S10, establishing hybrid driving vehicle system model;Step S20, the local state and adjacent vehicle state of artificial driving vehicle and automatic driving vehicle in vehicle train are respectively acquired;Step S30, the vehicle speed of automatic driving vehicle in vehicle train is controlled according to local state and adjacent vehicle state, and artificial driving vehicle maintains the driving speed and driving distance of expectation according to adjacent vehicle state and driving experience.This application has the beneficial effects that: 1, by the method of terminal sliding mode control, the violent fluctuation of individual vehicle is stabilized, and the comfort is improved;2, the disturbance observer based on terminal sliding mode control is used to estimate and compensate nonlinear external disturbance, to improve the control accuracy of intelligent network connection vehicle hybrid driving system;3, the stability of vehicle train system is realized by Lyapunov stability theorem, to avoid collision between vehicles.

Description

Technical Field

[0001] This invention relates to the field of cooperative control of intelligent connected vehicles, and more specifically to a cooperative control method for intelligent connected hybrid driving vehicles. Background Technology

[0002] Before the advent of autonomous driving, hybrid driving will become the dominant technology in the transportation sector. Cooperative control, as a key technology in intelligent transportation systems, can effectively alleviate traffic congestion, reduce fuel consumption, decrease vehicle emissions, and improve driving comfort and safety. For example, Weinan Gao, Zhong-Ping Jiang, and Kaan Ozbay published "Data-Driven Adaptive Optimal Control of Connected Vehicles" in *IEEE Transactions on Intelligent Transportation Systems*, which discloses a data-driven adaptive optimization control scheme for connected vehicles.

[0003] Existing technologies suffer from at least the following technical problems: Manually driven vehicles rely heavily on the driver's habits, while autonomous vehicles are more constrained by controllers, resulting in significant differences in driving behavior between the two. Especially when the vehicle in front is autonomous, the driver may increase their following distance due to psychological factors, leading to traffic instability and even accidents. Furthermore, the nonlinear mechanisms of the vehicle system itself and the complex nonlinear relationship between the vehicle and its environment make it difficult to accurately establish a hybrid driving cooperative control system. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a cooperative control method for intelligent connected hybrid driving vehicles. For hybrid driving fleet systems with nonlinear external disturbances, the method adopts a disturbance observer estimation and compensation based on terminal sliding mode control and Lyapunov stability theorem to achieve the stability and safety of the fleet system.

[0005] This invention is achieved through the following technical solution: a cooperative control method for intelligent connected hybrid driving vehicles, comprising the following steps: Step S10: Establish a hybrid driving vehicle system model Among them, the system state coefficient matrix State vector Input coefficient matrix , control input vector Coupling matrix , The coupling matrix represents the difference between the driving speed of the first vehicle and its desired driving speed. , For nonlinear external disturbances, let , , , Let be the driving distance between the i-th vehicle and the vehicle in front. For the desired driving distance, Let i be the driving speed of the i-th vehicle. The desired driving speed of the vehicle, As the lead car and In the formula, i = 1, 2, ..., N, where N is the number of vehicles in a convoy traveling in the same lane, including both manually driven and autonomous vehicles. When i = 2 and the second vehicle is manually driven... and When i=2 and the second vehicle is an autonomous vehicle and When i ≥ 3 and the i-th vehicle is a manually driven vehicle , , , and Let be the uncertain parameters caused by different drivers of the i-th vehicle. , The desired speed for manually driven vehicles, V max The maximum speed is h. s and h g These are the preset spacing ranges. The minimum and maximum values ​​of the distance between the i-th and i-th vehicles. When the vehicle remains stationary at zero speed, the distance between the two vehicles... exist Vehicle speed within range Depending on the distance between workshops The growth continues until the workshop distance reaches its maximum value. speed Reaching top speed ; When i ≥ 3 and the i-th vehicle is an autonomous vehicle , , , The control gain matrix is ​​a positive definite matrix. For the terminal sliding surface The adjoint matrix, β, p, and q are all positive constants. and , , , , To interfere with the observer parameters and , for The estimated interference error function is as follows: , For any positive definite matrix Q, the following conditions are satisfied: The Lyapunov function is , And H is a positive definite real symmetric matrix; Step S20: The manually driven vehicles and autonomous vehicles in the fleet acquire their own local state and the state of neighboring vehicles; Step S30: Based on the hybrid driving vehicle system model obtained in step S10, the autonomous vehicles in the fleet control their speed through local state and neighbor vehicle state, while the manually driven vehicles maintain the desired driving speed and driving distance according to the neighbor vehicle state and driving experience.

[0006] Furthermore, The value range is (0,1).

[0007] Furthermore, The value range is (0,1).

[0008] Furthermore, the value range of β is (1, 100).

[0009] Furthermore, the local state includes the vehicle's own position, and the neighboring vehicle state includes the positions of adjacent vehicles.

[0010] Furthermore, both manually driven and autonomous vehicles are equipped with cameras, lidar, communication modules, GPS positioning modules, and computing units.

[0011] The beneficial effects of this invention are as follows: 1. By using terminal sliding mode control, the severe fluctuations of individual vehicles are stabilized, and the comfort is improved; 2. An interference observer based on terminal sliding mode control is used to estimate and compensate for nonlinear external interference, thereby improving the control accuracy of the intelligent connected vehicle hybrid driving system; 3. The chain stability of the fleet system is achieved through Lyapunov stability theorem, avoiding collisions between vehicles. Attached Figure Description

[0012] Figure 1 This is a topology diagram of vehicle information flow. Detailed Implementation

[0014] Referring to the accompanying drawings, the present invention provides a cooperative control method for intelligent connected hybrid driving vehicles, comprising the following steps.

[0015] Step S10: Establish a hybrid driving vehicle system model .in, , .

[0016] A convoy of N vehicles traveling in the same lane includes both manually driven and autonomous vehicles. Both manually driven and autonomous vehicles are equipped with cameras, LiDAR, communication modules, GPS positioning modules, and computing units. The vehicles operate in both manually driven and autonomous driving modes. The communication modules use low-power wireless communication modules with a communication range set to 5 meters or other distances.

[0017] System state coefficient matrix State vector Input coefficient matrix , control input vector Coupling matrix , The coupling matrix represents the difference between the driving speed of the first vehicle and its desired driving speed. , For nonlinear external disturbances, let .

[0018] In the formula, i = 1, 2, ..., N, where N is the number of vehicles in a convoy traveling in the same lane, including both manually driven and autonomous vehicles.

[0019] , , Let be the driving distance between the i-th vehicle (i≥2) and the vehicle in front. For the desired driving distance, Let i be the driving speed of the i-th vehicle. The desired driving speed of the vehicle (preset value, such as 20km / h). As the lead car and The controller of the autonomous vehicle is based on and Parameters such as these control the vehicle's driving speed .

[0020] When i=2 and the second vehicle is a manually driven vehicle and When i=2 and the second vehicle is an autonomous vehicle and .

[0021] When i ≥ 3 and the i-th vehicle is a manually driven vehicle , , , and Let be the uncertain parameters caused by different drivers of the i-th vehicle. , The desired speed for manually driven vehicles, V max The maximum speed is h. s and h g These are the preset spacing ranges. The minimum and maximum values ​​of the distance between the i-th and i-th vehicles. When the vehicle remains stationary at zero speed, the distance between the two vehicles... exist Vehicle speed within range Depending on the distance between workshops The growth continues until the workshop distance reaches its maximum value. speed Reaching top speed .

[0022] When i ≥ 3 and the i-th vehicle is an autonomous vehicle , , .

[0023] It is the control gain matrix and is a positive definite matrix.

[0024] For the terminal sliding surface The adjoint matrix, β, p, and q are all positive constants. and The value of β ranges from 1 to 100.

[0025] , , , In To interfere with the observer parameters and , for The estimated interference error function is as follows: .

[0026] For any positive definite matrix Q, the following conditions are satisfied: The Lyapunov function is , And H is a positive definite real symmetric matrix.

[0027] In the above model, to suppress the chattering phenomenon easily caused by traditional sliding mode control, a non-singular terminal sliding mode control algorithm based on a nonlinear sliding surface is proposed, where the terminal sliding surface is... By introducing dummy variables Through the following second-order interference observer Estimate the uncertainties To improve the control accuracy of hybrid driving systems for intelligent connected vehicles; internal vehicle stability is achieved based on Lyapunov's stability theorem, where the Lyapunov function is... ,in And H is a positive definite real symmetric matrix, and for any positive definite matrix Q, the following conditions are met: This ensures the stability of the entire team.

[0028] Step S20: The manually driven vehicles and autonomous vehicles in the fleet acquire their own local state and the states of neighboring vehicles. The local state includes the vehicle's own position, and the state of neighboring vehicles includes the positions of adjacent vehicles.

[0029] To address communication issues such as communication interruptions that can easily occur when vehicles are far apart, a bidirectional information flow topology is adopted. Vehicles only receive and transmit information from adjacent vehicles. This eliminates the need for all vehicles to transmit information, reducing communication costs and improving the reliability of information transmission. Taking a single-lane hybrid vehicle cooperative control system as an example, the information flow topology is shown in the attached diagram. Dark-colored vehicles represent manually driven vehicles, while light-colored vehicles represent autonomous vehicles. In the bidirectional information flow topology, autonomous vehicles obtain local state information from adjacent vehicles through local network communication.

[0030] In step S30, based on the hybrid driving vehicle system model obtained in step S10, the autonomous vehicles in the fleet control their speed and adjust their speed to maintain the desired driving distance by considering local and neighboring vehicle states. The manually driven vehicles maintain the desired driving speed and driving distance based on the neighboring vehicle states and driving experience.

[0031] The present invention will now be described in detail with reference to specific embodiments.

[0032] In this embodiment, the fleet consists of 5 vehicles, with the 1st, 3rd, and 4th vehicles being manually driven, and the 2nd and 5th vehicles being autonomous vehicles. s For 1 meter, h g The desired driving distance is 5 meters. Set to 3 meters. i The value is 0.5, β is 5, p is 3, and q is 2. and The uncertain parameters are due to different drivers of the manually driven vehicle, i=1,3,4. Vmax =20km / h, h s =1 meter and h g =5 meters. Taking the third manually driven vehicle as an example, ,but .

[0033] Taking the fifth vehicle as an example, while driving, it acquires its own local state and the states of neighboring vehicles. Under the control of the vehicle controller, it smoothly maintains the desired distance from the vehicle in front. Specifically, it controls the speed of its own vehicle (the autonomous vehicle) based on the driving state of the vehicle in front (the manually driven vehicle) to maintain the desired driving distance. This is achieved through the controller. Make and Approaching zero. When and As the speed approaches zero, the convoys maintain the desired speed and geometric formation.

[0034] Finally, it is necessary to note that the above content is only used to help understand the technical solution of the present invention and should not be construed as a limitation on the scope of protection of the present invention; any non-essential improvements and adjustments made by those skilled in the art based on the above content of the present invention are all within the scope of protection claimed by the present invention.

Claims

1. A cooperative control method for intelligent connected hybrid driving vehicles, characterized in that, Includes the following steps: Step S10: Establish a hybrid driving vehicle system model , in, System state coefficient matrix , State vector , Input coefficient matrix , Control input vector , Coupling matrix , This represents the difference between the current driving speed of the first vehicle and its desired driving speed. Coupling matrix , For nonlinear external disturbances, let , , , Let be the driving distance between the i-th vehicle and the vehicle in front. For the desired driving distance, Let i be the driving speed of the i-th vehicle. The desired driving speed of the vehicle, As the lead car and , In the formula, i = 1, 2, ..., N, where N is the number of vehicles in a platoon traveling in the same lane, including both manually driven and autonomous vehicles. When i=2 and the second vehicle is a manually driven vehicle and , When i=2 and the second vehicle is an autonomous vehicle and , When i ≥ 3 and the i-th vehicle is a manually driven vehicle , , , and Let be the uncertain parameters caused by different drivers of the i-th vehicle. , The desired speed for manually driven vehicles, V max The maximum speed is h. s and h g These are the preset spacing ranges. The minimum and maximum values ​​of the distance between the i-th and i-th vehicles. When the vehicle remains stationary at zero speed, the distance between the two vehicles... exist Vehicle speed within range Depending on the workshop distance The growth continues until the workshop distance reaches its maximum value. speed Reaching top speed ; When i ≥ 3 and the i-th vehicle is an autonomous vehicle , , , The control gain matrix is ​​a positive definite matrix. For the terminal sliding surface The adjoint matrix, β, p, and q are all positive constants. and , , , , To interfere with the observer parameters and , for The estimated interference error function is as follows: , For any positive definite matrix Q, the following conditions are satisfied: The Lyapunov function is , And H is a positive definite real symmetric matrix; Step S20: The manually driven vehicles and autonomous vehicles in the fleet acquire their own local state and the state of neighboring vehicles; Step S30: Based on the hybrid driving vehicle system model obtained in step S10, the autonomous vehicles in the fleet control their speed through local state and neighbor vehicle state, while the manually driven vehicles maintain the desired driving speed and driving distance according to the neighbor vehicle state and driving experience.

2. The intelligent connected hybrid driving vehicle cooperative control method according to claim 1, characterized in that, The value range is (0,1).

3. The intelligent connected hybrid driving vehicle cooperative control method according to claim 1, characterized in that, The value range is (0,1).

4. The intelligent connected hybrid driving vehicle cooperative control method according to claim 1, characterized in that, The value range of β is (1, 100).

5. The intelligent connected hybrid driving vehicle cooperative control method according to claim 1, characterized in that, The local state includes the vehicle's own position, and the neighboring vehicle state includes the positions of adjacent vehicles.

6. The cooperative control method for intelligent connected hybrid driving vehicles according to claim 1, characterized in that, Both manually driven and autonomous vehicles are equipped with cameras, lidar, communication modules, GPS positioning modules, and computing units.

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