An Adaptive Path Following Control Method for a Vehicle Platoon System

Abstract

The present invention relates to an adaptive path following control method for a vehicle platoon system. By optimizing the update rule of the target path and estimating the acceleration of adjacent vehicles, the information transfer speed between vehicles in the platoon is increased, the trajectory following convergence speed of the vehicle platoon is improved, and the adaptability of the vehicle in different environments is enhanced. First, a distributed vehicle model is created and an improved parameterized target path is designed; second, the vehicle platoon control objectives are formulated, and the control objectives include geometric objectives and dynamic objectives. The geometric objective allows the vehicle to travel on the target trajectory, and the dynamic objective allows the vehicle to reach the required speed; third, a path tracking controller for distributed vehicle platoon control is designed using a sliding mode control and an adaptive method.

Description

Technical Field

[0001] The present invention relates to the field of vehicle platoon control, and more particularly to an adaptive path following control method for a vehicle platoon system. Background Art

[0002] A vehicle platoon is a control method for realizing vehicle cooperation. This control method has the advantages of modularity, easy extensibility and flexibility. While reducing the system cost and management difficulty, it can effectively improve the transportation efficiency and ensure traffic safety.

[0003] Although researchers have achieved many important results in the field of vehicle platoon system research, there are still some problems to be solved urgently, such as less distributed application of vehicle platoon systems, weakened extensibility, increased communication overhead, etc.; these problems lead to an increase in the tracking error of vehicle platoons and a decrease in the error convergence speed.

[0004] In order to improve the scalability of the vehicle platoon system, improve the adaptability of the vehicle platoon in different environments, and make the vehicle platoon travel in a straight line at a desired speed, this patent proposes an adaptive path following control method for a vehicle platoon system. This method can optimize the information transmission between vehicle platoons, improve the error convergence speed of vehicle platoons, improve the adaptability of vehicles in complex road conditions, and ensure the stability of the entire control system. First, a nonlinear model of a distributed vehicle fleet is established to obtain a parametric form of the target path. Secondly, the geometric and dynamic targets of the vehicle row are formulated, and the relationship between the targets is analyzed. Thirdly, in order to achieve the established control target, a path following control based on distributed vehicle platoon control is designed by using a sliding mode control method and an adaptive control method while considering the unknown disturbances during the vehicle driving process. Summary of the Invention

[0005] The purpose of the present invention is to provide an adaptive path following control method for a vehicle platoon system, which improves the information transmission speed between vehicle platoons, the trajectory following convergence speed of vehicle platoons, and the adaptability of vehicles in different environments by optimizing the update rule of the target path and estimating the acceleration of adjacent vehicles.

[0006] To achieve the above object, the technical solution of the present invention is: an adaptive path following control method for a vehicle platoon system, which accelerates the error convergence of the vehicle's trajectory based on parametric path following; designs an adaptive controller based on a finite-time sliding mode control method to converge the vehicle position error and speed error within a finite time; estimates the acceleration of adjacent vehicles based on an adaptive algorithm to reduce the vehicle-to-vehicle communication overhead and sensor cost; the method specifically includes the following steps:

[0007] Step 1: Obtain the target path of the vehicle queue, the inter-vehicle distance of the vehicle queue, and the desired speed;

[0008] Step 2: Model the vehicle. The model describes the quantitative relationship between the input variables and the output variables. The input variable is the acceleration of the vehicle, and the output variable is the position of the vehicle. Based on a second-order integrator, create a vehicle model with a disturbance term;

[0009] Step 3: According to the vehicle model in Step 2, design the vehicle guidance speed and define the formation error function. Based on error feedback, design the guidance speed of each vehicle;

[0010] Step 4: According to the desired speed in Step 1 and the vehicle guidance speed in Step 3, design the target path update law. To accelerate the convergence speed of the vehicle line trajectory tracking error, improve the control accuracy of the controller, and improve the adaptability of the vehicle in different environments, parameterize the path;

[0011] Step 5: According to the error between the vehicle guidance speed and the actual vehicle speed in Step 3 and the position error between the vehicle and the target path, design the sliding surface;

[0012] Step 6: Modify the sliding mode approximation law to make the state on the sliding surface converge to the expected value;

[0013] Step 7: According to the sliding surface in Step 5 and the sliding mode approaching law in Step 6, design the input of the vehicle;

[0014] Step 8: Estimate the acceleration of the adjacent vehicle at the vehicle entrance according to the adaptive algorithm.

[0015] In an embodiment of the present invention, the specific implementation method of Step 1 is as follows:

[0016] Obtain the target path of the vehicle queue, the inter-vehicle distance of the queue vehicles, and the desired speed; Given the vehicle target trajectory as

[0017]

[0018] Wherein, And Are the position coordinates of the parameterized path changing with the path variable In the Coordinate system, Is the angle between the tangent of the vehicle queue position on the path and , The partial derivative of, Is The partial derivative of, Given the inter-vehicle distance of the vehicle queue as D i =[k x ,k y ,k z , where kx , k y , k z is a positive constant.

[0019] In an embodiment of the present invention, the specific implementation method of step two is as follows:

[0020] Based on the double integral model, the vehicle is regarded as a rigid body, the width of the vehicle is not considered, the transfer of the vehicle's own front and rear loads is ignored, only the in-plane motion of the vehicle is considered, the vertical motion of the vehicle is ignored, and an unknown disturbance is applied to the input of the vehicle to obtain the vehicle model:

[0021]

[0022] where i represents the vehicle number, is the motion position and yaw angle of the vehicle queue in the global coordinate system, [x i , y i is the position coordinate of the i-th vehicle in its own coordinate system, ψ i is the angle between and the i-th vehicle coordinate system i v is the speed of the i-th vehicle at . Since the vehicle will be affected by the external environment during driving, define as the disturbance term received by the i-th vehicle, δ x and δ y are the disturbance components of the vehicle at respectively, δ ψ is the disturbance received by the vehicle during steering. The absolute value of the disturbance term has an upper bound R(ψ i ) is the rotation matrix of the i-th vehicle. The rotation matrix can transform the coordinates at to the coordinates in the global coordinate system . V i is the speed of the i-th vehicle at , and u i is the control input of the i-th vehicle.

[0023] In an embodiment of the present invention, the specific implementation method of step three is as follows:

[0024] The leading speed of the i-th vehicle is designed as

[0025]

[0026] where is a non-zero bounded variable, N is the number of vehicles, v ds is the desired speed of the vehicle, a ij = 1 means that the i-th vehicle can obtain the information of the j-th vehicle. Otherwise, a ij= 0; a i0 = 1 indicates that the i-th vehicle can obtain the information of the virtual leading vehicle; otherwise, a i0 = 0, the speed of the j-th vehicle is v j , the vehicle formation error is e p,i , the rotation matrix of the j-th vehicle is set as R j ;

[0027] The vehicle queue error feedback gain κ p,i is

[0028]

[0029] where U > 0 is a positive constant gain, the magnitude of U is related to the speed during the vehicle queue merging process, and σ can make κ p,i the denominator non-zero, and U and σ are related to the state of the vehicle during queue merging.

[0030] In an embodiment of the present invention, the specific implementation method of step four is as follows:

[0031] The target path update law is defined as

[0032]

[0033] The target path update law of the i-th vehicle is designed as

[0034]

[0035] where the rotation matrix of the i-th vehicle is set as R i , the speed of the i-th vehicle is v i , the leading speed of the i-th vehicle is v g,i .

[0036] In an embodiment of the present invention, the specific implementation method of step five is as follows:

[0037] l i and l g The position error between them is e l,i = l g - l i , design the sliding mode surface of e l,i as

[0038]

[0039]

[0040]

[0041] where c is a positive constant gain, l gA trajectory generated for a vehicle to travel at a vehicle guidance speed signal is related to the acceleration of an adjacent vehicle, aiming to reduce the communication burden between vehicles.

[0042] In an embodiment of the present invention, the specific implementation method of step six is as follows:

[0043] Design a sliding mode reaching law. A suitable sliding mode reaching law can make the vehicle formation error e p,i and the position error e l,i converge to the sliding mode surface; design the sliding mode reaching law as formula (10):

[0044]

[0045] where is a positive constant gain.

[0046] In an embodiment of the present invention, the specific implementation method of step seven is as follows:

[0047] Simultaneously solve formula (8) and formula (10), define as the estimated value of a j , and the input of the vehicle is designed as

[0048]

[0049] According to the finite-time sliding mode surface, μ i is designed as

[0050]

[0051] where α > 0 is a positive constant gain, is the upper bound of μ i , aiming to make the system stable.

[0052] In an embodiment of the present invention, the specific implementation method of step eight is as follows:

[0053] Define Design the estimated value to obtain

[0054]

[0055] where k1 > 0 is a positive constant gain.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] 1. An anti-interference vehicle platoon control system is proposed, and a second-order vehicle model with a disturbance term is constructed, which improves the reliability of the vehicle platoon control system under unknown disturbances. The vehicle platoon control is combined with the trajectory following control, and the update law of the target path is liberalized by using the parameterized trajectory generation method, which relaxes the requirements for the generated trajectory of the virtual vehicle and improves the trajectory following convergence speed of the vehicle platoon.

[0058] 2. The adaptive sliding mode control method is applied to the trajectory tracking control of the vehicle row. By estimating the acceleration of adjacent vehicles, the adaptability of the vehicle row under complex road conditions is improved, and at the same time, the measurement load and communication load of the vehicle are reduced, so that the vehicle displacement is reduced and the controller is stable. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0060] Figure 1 is the control structure diagram of the embodiment of the present invention.

[0061] Figure 2 is the vehicle coordinate system and topological structure in the global coordinate system of the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0062] The technical solutions of the present invention will be specifically described below with reference to the drawings.

[0063] An adaptive path following control method for a vehicle platoon system of the present invention specifically includes the following steps:

[0064] Step 1. Obtain the target path of the vehicle platoon, the distance between queue vehicles, and the desired speed:

[0065] The given vehicle target trajectory is

[0066]

[0067] where and are the position coordinates of the parameterized path varying with the path variable at , as shown in Figure 2 , is the angle between the tangent of the vehicle platoon position on the path and , is partial derivative of, is partial derivative of;

[0068] The given distance between vehicles is

[0069] D i = [k x , k y , k z (15)

[0070] where k x , k y , k z is a positive constant;

[0071] Step 2. Model the vehicle: The vehicle platoon system consists of N vehicles, all numbered i. During the process of trajectory following in the vehicle platoon system, two independent coordinate systems are used, namely the global coordinate system and the vehicle coordinate system. Among them, the axes of the global coordinate system are X E and Y E , and the axes of the vehicle coordinate system are X B and Y B . The circular shape represents the virtual leader vehicle, and the square represents the platoon vehicle. Parameterize the target path information of the virtual leader vehicle to obtain the parameterized target trajectory of the vehicle The virtual leader vehicle updates its motion position in real time according to and the trajectory update law. The vehicle can track the virtual leader vehicle. As shown in Figure 1 , based on the double integrator model, the vehicle is regarded as a rigid body, the width of the vehicle is not considered, the transfer of the front and rear loads of the vehicle itself is ignored, only the motion of the vehicle in the plane is considered, the vertical motion of the vehicle is ignored, and the unknown disturbance is applied to the input of the vehicle to obtain the model of the vehicle,

[0072]

[0073] where n0 is the virtual leader vehicle, is the motion position and yaw angle of the vehicle platoon at , [x i , y i is the position coordinate of the ith vehicle at , ψ i is the angle between and the ith vehicle coordinate system , v i is the speed of the ith vehicle at . Since the vehicle is affected by the external environment during driving, define as the disturbance term received by the ith vehicle, and δ x and δ y are the disturbance components of the vehicle at respectively, and δ ψThe disturbance received by the vehicle during steering, and the absolute value of the disturbance term has an upper bound R(ψ i ) is the rotation matrix of the i-th vehicle, and the rotation matrix can convert the coordinates in the vehicle coordinate system to the coordinates in the global coordinate system , V i is the speed of the i-th vehicle in , u i is the control input of the i-th vehicle;

[0074] Step 3: According to the vehicle model in Step 2, design the guiding speed of the vehicle. Define the formation position error of the i-th vehicle in as

[0075]

[0076] where is the position error between vehicles, e d,i is the position error between the vehicle and the virtual leader vehicle, a ij = 1 means that the i-th vehicle can obtain the information of vehicle j, otherwise, a ij = 0, a i0 = 1 indicates that the i-th vehicle can obtain the information of the virtual leader vehicle, otherwise, a i0 = 0, is a positive constant gain,

[0077] The position error between the vehicle and the virtual leader vehicle is defined as

[0078]

[0079] where is the parameterized target path, l i and l j are the own trajectory of the vehicle and the trajectory of the adjacent vehicle respectively. The guiding speed of the i-th vehicle is designed as

[0080]

[0081] where is a non-zero bounded variable, N is the number of vehicles, v ds is the expected speed of the vehicle, and the vehicle formation position error feedback gain κ p,i is

[0082]

[0083] where U > 0 is a positive constant gain, the magnitude of U is related to the speed during the vehicle queue merging process, and σ can make κ p,iThe denominator is not zero, and U and σ are related to the state when the vehicle merges into the queue.

[0084] Step 4: According to the desired speed in Step 1 and the vehicle guidance speed in Step 3, design the target path update law, which is defined as

[0085]

[0086] The path update law of the i-th vehicle is designed as

[0087]

[0088] where the rotation matrix of the i-th vehicle is set as R i , the speed of the i-th vehicle is v i , and the vehicle queue control guidance speed is v g,i .

[0089] Step 5: According to the error between the guidance speed in Step 3 and the actual speed of the vehicle, and the position error between the vehicle and the target path, design the sliding surface.

[0090] l i and l g The position error e l,i between them is defined as

[0091] e l，i = l g - l i (23) To enable the vehicle to track the speed guidance signal, using the sliding mode control method, design the sliding surface of e p,i and e l,i as

[0092]

[0093]

[0094]

[0095] where c is a positive constant gain. l g is the trajectory generated when the vehicle travels at the guidance speed signal, which is related to the acceleration of adjacent vehicles, aiming to reduce the communication burden between vehicles.

[0096] Step 6: Design the sliding surface reaching law to make the state on the sliding surface converge to the desired value.

[0097] The sliding mode reaching law (27) can be designed. By combining formula (25) and formula (27), the following function can be solved. At the same time, the sliding mode reaching law can be designed. A suitable reaching law can make the vehicle queue position error e p,i and position error e l,i Converging to the sliding surface, the sliding mode reaching law is designed as,

[0098]

[0099] in, is a positive constant gain;

[0100] Step 7: Design the vehicle input according to the sliding surface in step 5 and the reaching law in step 6.

[0101] definition for a j The vehicle input is designed to be

[0102]

[0103] In order to make p,i and e l,i To reach the sliding surface in a finite time, design,

[0104]

[0105] Among them, α>0 is a positive constant gain, μ i The purpose is to make the system stable.

[0106] Step 8: Estimate the acceleration of the vehicle's input adjacent vehicles according to the adaptive algorithm.

[0107] exist Estimated value Design to obtain,

[0108]

[0109] Wherein, k1 is a positive constant.

[0110] The above are preferred embodiments of the present invention. It should be noted that those skilled in the art may make various improvements and modifications without departing from the spirit of the present invention, and such improvements and modifications should also be considered to be within the scope of the present invention.

Claims

1. An adaptive path following control method for a vehicle platoon system, characterized in that, Accelerate the error convergence of a vehicle based on the trajectory of a parameterized path-following vehicle; Design an adaptive controller based on a finite-time sliding mode control method to converge the vehicle position error and speed error within a finite time; estimate the acceleration of adjacent vehicles based on an adaptive algorithm to reduce vehicle-to-vehicle communication overhead and sensor costs; the method specifically includes the following steps: Step 1: Obtain the target path of the vehicle queue, the distance between vehicles in the vehicle queue, and the desired speed; Step 2: Model the vehicle. The model is a description of the quantitative relationship between input variables and output variables; Among them, the input variable is the acceleration of the vehicle, and the output variable is the position of the vehicle; based on a second-order integrator, create a vehicle model with a disturbance term; Step 3: According to the vehicle model in Step 2, design the vehicle guidance speed and define a formation error function; Design the guidance speed of each vehicle based on error feedback; Step 4: According to the desired speed in Step 1 and the vehicle guidance speed in Step 3, design a target path update law; to accelerate the convergence speed of the vehicle line trajectory tracking error, improve the control accuracy of the controller, and improve the adaptability of the vehicle in different environments, parameterize the path; Step 5: According to the error between the vehicle guidance speed in Step 3 and the actual speed of the vehicle, and the position error between the vehicle and the target path, design a sliding mode surface; Step 6: Modify the sliding mode approximation law to make the state on the sliding mode surface converge to the expected value; Step 7: According to the sliding mode surface in Step 5 and the sliding mode reaching law in Step 6, design the input of the vehicle; Step 8: Estimate the acceleration of adjacent vehicles at the vehicle entrance according to the adaptive algorithm; The specific implementation method of Step 1 is: Obtain the target path of the vehicle queue, the distance between queue vehicles, and the desired speed; given the vehicle target trajectory as Among them, and are the position coordinates of the parameterized path varying with the path variable in the coordinate system, and is the included angle between the tangent of the vehicle queue position on the path and , is 's partial derivative, is 's partial derivative. Given that the distance between vehicles in the vehicle queue is D i =[k x ,k y ,k z , where k x ,k y ,k z are positive constants; The specific implementation method of Step 2 is: Based on a double-integral model, the vehicle is regarded as a rigid body, the width of the vehicle is not considered, the vehicle ignores the transfer of its own front and rear loads, only considers the in-plane motion of the vehicle, ignores the vertical motion of the vehicle, and applies an unknown disturbance to the input of the vehicle to obtain the vehicle model: where \(i\) represents the vehicle number, which are the motion position and yaw angle of the vehicle queue in the global coordinate system, \([x i , y i \) is the position coordinate of the \(i\)-th vehicle in its own coordinate system, \(\psi i is the angle between and the \(i\)-th vehicle coordinate system i , \(v is the speed of the \(i\)-th vehicle under . Since the vehicle is subject to the interference of the external environment during driving, define as the interference term of the \(i\)-th vehicle, \(\delta x and \(\delta y are the disturbance components of the vehicle under respectively, \(\delta ψ is the disturbance during vehicle steering. The absolute value of the interference term has an upper bound \(R(\psi i )\) is the rotation matrix of the \(i\)-th vehicle. The rotation matrix can transform the coordinates under into the coordinates in the global coordinate system . \(V i is the speed of the \(i\)-th vehicle under , and \(u i is the control input of the \(i\)-th vehicle; The specific implementation method of Step 3 is: The guidance speed of the i-th vehicle is designed as Among them, is a non-zero bounded variable, N is the number of vehicles, v ds is the expected speed of the vehicle, a ij = 1 means that the i-th vehicle can obtain the information of the j-th vehicle, otherwise, a ij = 0; a i0 = 1 indicates that the i-th vehicle can obtain the information of the virtual leading vehicle, otherwise, a i0 = 0, the speed of the j-th vehicle is v j , the vehicle formation error is e p,i , the rotation matrix of the j-th vehicle is set as R j ; Vehicle platoon error feedback gain κ p,i is where U>0 is a positive constant gain, the magnitude of U is related to the speed during the vehicle platoon merging process, and σ can ensure that κ p,i the denominator is non-zero, and U and σ are related to the state of the vehicle during platoon merging; The specific implementation method of Step 4 is: The target path update law is defined as The target path update law of the i-th vehicle is designed as Among them, the rotation matrix of the i-th vehicle is set as R i , the speed of the i-th vehicle is v i , the leading speed of the i-th vehicle is v g,i ; The specific implementation method of Step 5 is: l i and l g The position error between them is e l,i = l g - l i Design the sliding mode surface of e l,i as where c is a positive constant gain, l g is a trajectory generated when the vehicle travels at the vehicle guidance speed signal, is related to the acceleration of the adjacent vehicle, aiming to reduce the communication burden between vehicles.

2. The adaptive path following control method for a vehicle platoon system according to claim 1, characterized in that, The specific implementation method of Step 6 is: Design a sliding mode reaching law. An appropriate sliding mode reaching law can make the vehicle formation error e p,i and the position error e l,i converge to the sliding mode surface. The designed sliding mode reaching law is formula (10): Among them, is a positive constant gain.

3. The adaptive path following control method for a vehicle platoon system according to claim 2, characterized in that, The specific implementation method of Step 7 is: Combining formula (8) and formula (10), define as the estimated value of a j The input of the vehicle is designed to be According to the finite-time sliding surface, μ i is designed as where α > 0 is a positive constant gain, is the upper bound of μ i to stabilize the system.

4. The adaptive path following control method for a vehicle platoon system according to claim 3, characterized in that, The specific implementation method of Step 8 is: Definition For the estimated value Design to obtain Where, k1>0 is a positive constant gain.

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