Control system and control method

Abstract

Improve the vehicle's ability to follow the target route. [Solution] A control system having an acquisition unit 121 that acquires the distance deviation between the vehicle and a target route for the vehicle, the azimuth angle deviation between a first direction angle of the target route and a second azimuth angle of the vehicle, the vehicle's yaw rate, vehicle speed, and sideslip angle; a generation unit 122 that generates a prediction model of changes in each of the distance deviation, azimuth angle deviation, sideslip angle, and second azimuth angle based on the distance deviation, azimuth angle deviation, vehicle speed, sideslip angle, yaw rate, and first direction angle, and a transfer function that represents the relationship between yaw rate and sideslip angle; and a yaw rate determination unit 123 that determines a target yaw rate that minimizes the value of an evaluation function whose parameters include the estimated distance deviation, estimated azimuth angle deviation, and target yaw rate predicted from the prediction model.

Description

[Technical field]

[0001] The present invention relates to a control system and a control method for controlling the behavior of a vehicle. [Background technology]

[0002] There is known a control system for controlling the behavior of a vehicle. Patent Document 1 discloses a technology for determining a steering angle that minimizes a value of an evaluation function including a lateral deviation, an azimuth angle deviation, a steering angle, and a change amount of the steering angle. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2023-140494 A Summary of the Invention [Problem to be solved by the invention]

[0004] Incidentally, when a vehicle turns, a sideslip angle occurs between the vehicle's traveling direction and the direction of the wheels. Therefore, when a vehicle is driven along a target route with a large curvature, it is necessary to generate a sideslip angle that will make the vehicle follow the target route. However, a sideslip angle generated by a steering angle or yaw rate of the vehicle determined without considering the sideslip angle may differ from the sideslip angle that turns the vehicle along the target route. Therefore, a steering angle or yaw rate that does not consider the sideslip angle causes the vehicle to turn so as to deviate from the target route, and the vehicle is unable to follow the target route.

[0005] The present invention has been made in consideration of these points, and has an object to improve the ability of a vehicle to follow a target route. [Means for solving the problem]

[0006] In a first aspect of the present invention, there is provided a control system having: an acquisition unit that acquires a distance deviation between a route point of a target route of the vehicle and the vehicle, an azimuth angle deviation between a first direction angle of the route point and a second azimuth angle of the vehicle, a yaw rate of the vehicle, a vehicle speed of the vehicle, and a sideslip angle of the vehicle; a generation unit that generates a prediction model that predicts changes in each of the distance deviation, the azimuth angle deviation, the sideslip angle, and the second azimuth angle based on the distance deviation, the azimuth angle deviation, the vehicle speed, the sideslip angle, the yaw rate, and the first direction angle, and a transfer function that represents the relationship between the yaw rate and the sideslip angle; and a yaw rate determination unit that determines, as a target yaw rate, the target yaw rate that minimizes a value of an evaluation function to which the acquired distance deviation, the azimuth angle deviation, and the yaw rate are input.

[0007] The generation unit may generate a continuous-time prediction model, and by discretizing the generated continuous-time prediction model, generate a discrete-time prediction model that predicts changes in each of the distance deviation, the azimuth angle deviation, the sideslip angle, and the second azimuth angle in the next unit time.

[0008] The transfer function may be a function that represents the relationship between the yaw rate and the second azimuth angle and corresponds to a reference response model that outputs the amount of change in the second azimuth angle when the yaw rate is input.

[0009] The vehicle may further include a steering angle determination unit that determines a target steering angle of the vehicle when the yaw rate and the determined target yaw rate are input, and the yaw rate determination unit may notify the steering angle determination unit of the target yaw rate.

[0010] The vehicle may have a steering control unit that operates a steering wheel of the vehicle to set the steering angle of the vehicle to the target steering angle, and the acquisition unit may acquire the distance deviation, the azimuth angle deviation, the vehicle speed, the sideslip angle, and the yaw rate after the steering control unit sets the steering angle to the target steering angle.

[0011] The acquisition unit acquires the yaw moment of inertia of the vehicle, the weight of the vehicle, a first distance from the center of gravity of the vehicle to the axle of the front wheels of the vehicle, a second distance from the center of gravity of the vehicle to the axle of the rear wheels of the vehicle, the length of the vehicle, rear wheel cornering stiffness, and a longitudinal vehicle speed in the traveling direction of the vehicle as the vehicle speed, and the transfer function may be expressed using the yaw moment of inertia, a first product of the weight, the longitudinal vehicle speed, and the first distance, a ratio of the product of the rear wheel cornering stiffness, the second distance, and the vehicle length to the longitudinal vehicle speed, a second product of the weight, the longitudinal vehicle speed, and the first distance, and a third product of the rear wheel cornering stiffness and the vehicle length.

[0012] In a second aspect of the present invention, there is provided a control method including the steps of: acquiring a distance deviation between a route point of a target route of the vehicle and the vehicle, an azimuth angle deviation between a first direction angle of the route point and a second azimuth angle of the vehicle, a yaw rate of the vehicle, a vehicle speed of the vehicle, and a sideslip angle of the vehicle, which are executed by a processor mounted on the vehicle; generating a prediction model predicting changes in each of the distance deviation, the azimuth angle deviation, the sideslip angle, and the second azimuth angle based on the distance deviation, the azimuth angle deviation, the vehicle speed, the sideslip angle, the yaw rate, and the first direction angle, and a transfer function that represents the relationship between the yaw rate and the sideslip angle and outputs the sideslip angle when the yaw rate is input; and determining, as a target yaw rate, the target yaw rate that minimizes a value of an evaluation function to which the acquired distance deviation, azimuth angle deviation, and yaw rate are input, the evaluation function including an estimated distance deviation, an estimated azimuth angle deviation, and a target yaw rate predicted from the prediction model as parameters. Effect of the Invention

[0013] According to the present invention, it is possible to improve the ability of a vehicle to track a target route. [Brief description of the drawings]

[0014] [Figure 1] FIG. 2 is a diagram for explaining the motion of a vehicle. [Diagram 2]FIG. 2 is a diagram for explaining a configuration of a control system. [Diagram 3] FIG. 4 is a block diagram showing an example of a process for determining a target steering angle. [Figure 4] FIG. [Diagram 5] FIG. 4 is a schematic diagram of a travel path of a vehicle according to a comparative example. [Figure 6] 2 is a schematic diagram showing a travel path of a vehicle traveled under the control of the control system according to the present embodiment. FIG. [Figure 7] 4 is a flowchart showing an example of a process for controlling vehicle traveling. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] [Configuration of control system S] The configuration of the control system S will be described with reference to Figs. 1 and 2. Fig. 1 is a diagram for explaining the motion of vehicle A. Fig. 2 is a diagram for explaining the configuration of the control system S. The control system S is a system for causing vehicle A to travel along a target route B by controlling the steering angle of vehicle A. The control system S includes a vehicle control device 1, and is mounted on vehicle A. The target route B is a predetermined route, and includes route points indicating multiple target travel positions for vehicle A, and a target orientation for vehicle A at each route point.

[0016] In Fig. 1, an absolute coordinate system is used. In the absolute coordinate system of this embodiment, for example, the longitude direction is the X-axis and the latitude direction is the Y-axis, but the present invention is not limited to this. Hereinafter, the speed component in the vehicle width direction of the vehicle speed V of vehicle A is the lateral speed Vy. The speed component in the traveling direction perpendicular to the vehicle width direction of the vehicle speed V is the longitudinal speed Vx.

[0017] The distance deviation z is the distance between the center of gravity C, which indicates the position of vehicle A, and route point D of the target route B of vehicle A. The azimuth angle deviation θ is the difference between a first azimuth angle ψ, which indicates the direction of route point D of target route B, and a second azimuth angle φ, which indicates the direction of vehicle A. The first azimuth angle ψ is the target direction of vehicle A. The first azimuth angle ψ is, for example, the direction of the tangent to route point D.

[0018] The vehicle control device 1 is, for example, an ECU (Electronic Control Unit). The vehicle control device 1 includes a storage unit 11 and a control unit 12. The storage unit 11 is a storage medium including a ROM (Read Only Memory), a RAM (Random Access Memory), a hard disk, etc. The storage unit 11 stores a program executed by the control unit 12.

[0019] The control unit 12 is a computation resource including a processor such as a CPU (Central Processing Unit). The control unit 12 executes a program stored in the storage unit 11 to realize functions as an acquisition unit 121, a generation unit 122, a yaw rate determination unit 123, a steering angle determination unit 124, and a steering control unit 125.

[0020] The acquisition unit 121 acquires a plurality of pieces of information related to the vehicle A. For example, the acquisition unit 121 acquires a distance deviation z using coordinates indicating the position of the vehicle A identified by a positioning system that measures the position using a signal output from a satellite and coordinates indicating the position of a route point D of the target route. The acquisition unit 121 acquires a first direction angle ψ and a second azimuth angle φ, and acquires the difference between the first direction angle ψ and the second azimuth angle φ as an azimuth angle deviation θ.

[0021] The acquisition unit 121 acquires the sideslip angle β of the vehicle A. For example, the acquisition unit 121 calculates the sideslip angle β using the acceleration in the traveling direction and the acceleration in the vehicle width direction of the vehicle A detected by an acceleration sensor mounted on the vehicle A. Note that the acquisition unit 121 may acquire the sideslip angle β using another method.

[0022] The acquisition unit 121 acquires the yaw rate, vehicle speed V, and acceleration of the vehicle A. The acquisition unit 121 acquires the yaw rate of the vehicle A from, for example, a yaw rate sensor that detects the yaw rate of the vehicle A. The acquisition unit 121 acquires acceleration from an acceleration sensor, and acquires the vehicle speed V based on the acquired acceleration. The acquisition unit 121 may acquire the vehicle speed V based on a pulse signal proportional to the rotation speed of the wheels of the vehicle A. The acquisition unit 121 acquires the longitudinal vehicle speed Vx based on the acquired vehicle speed V.

[0023] The acquisition unit 121 acquires the yaw moment of inertia l of the vehicle A. z , weight m, vehicle length l, and rear wheel cornering stiffness K r In addition, the acquisition unit 121 acquires a first distance l from the center of gravity of the vehicle A to the axle of the front wheel of the vehicle A. f and the second distance l from the center of gravity of vehicle A to the rear wheel axle of vehicle A. r For example, the acquisition unit 121 acquires the weight m and the first distance l stored in the storage unit 11. f , second distance l r and the vehicle length l. The acquisition unit 121 acquires the yaw moment of inertia l based on the yaw rate and the acceleration. z , Rear wheel cornering stiffness K r The method for the acquisition unit 121 to acquire information about the vehicle A is not limited to this.

[0024] When vehicle A travels along target route B along a road with a curvature radius R, the desired rate of change of azimuth angle of vehicle A is expressed by the following formula (1) using a first azimuth angle ψ. Also, the desired acceleration in the vehicle width direction of vehicle A is expressed by the following formula (2) using a distance deviation z.

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[0025] The acquisition unit 121 acquires a relational expression representing the amount of change in distance deviation z (first-order differential of distance deviation z) from equation (2). Specifically, the acquisition unit 121 acquires a relational expression representing the amount of change in distance deviation z shown in equation (5) from equation (3) and equation (2) representing the azimuth angle deviation θ. The azimuth angle deviation θ expressed in the following equation (3) is the difference between the second azimuth angle φ of vehicle A and the first azimuth angle ψ of route point D. Equation (4) is an equation obtained by differentiating both sides of equation (3). Equation (5) is an equation obtained by substituting equation (4) into equation (2) and integrating both sides.

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[0026] In addition, in FIG. 1, when the side slip angle β of the vehicle A is in a small range (β<<1), the following formula (6) holds.

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[0027] Here, we will explain the transfer function that expresses the relationship between yaw rate and sideslip angle. The transfer function is a function that outputs sideslip angle when yaw rate is input. For example, the transfer function is z and weight m, longitudinal vehicle speed Vx and first distance l f and the rear wheel cornering stiffness K r , second distance l r and the ratio of the product of the vehicle length l to the longitudinal vehicle speed Vx, and the weight m, the longitudinal vehicle speed Vx, and the first distance l f and the rear wheel cornering stiffness K r and the third product of the vehicle length l. Specifically, the transfer function is expressed by the following equation (7) using the change in the sideslip angle β and the second azimuth angle φ (the first-order differential of the second azimuth angle φ represented by φ with a dot).

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[0028] The generation unit 122 generates a prediction model that predicts the state of the vehicle A. Specifically, the generation unit 122 generates a continuous-time prediction model that predicts changes in each of the distance deviation z, the azimuth angle deviation θ, the side slip angle β, and the second azimuth angle φ based on the distance deviation z, the azimuth angle deviation θ, the vehicle speed V, the side slip angle β, the target yaw rate r, and the first azimuth angle ψ, and a transfer function. More specifically, the generation unit 122 generates a continuous-time state equation expressed by the following equation (9) as the prediction model.

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[0029] In addition, the coefficient matrix A in equation (9) C , B cu and B. cv varies depending on the target route and the target vehicle speed. Therefore, the generation unit 122 discretizes the prediction model to generate a prediction model that predicts changes in the unit time ahead (one step ahead). For example, the generation unit 122 generates a discrete-time prediction model by discretizing a continuous-time prediction model. Specifically, the generation unit 122 generates a discrete-time state equation that predicts changes in each of the distance deviation z, the azimuth angle deviation, the sideslip angle, and the second azimuth angle in the unit time ahead by discretizing the continuous-time state equation expressed by Equation (9) online. A method for deriving the discrete-time state equation will be described below.

[0030] The state equations after discretization are expressed by the following equations (10) to (13), where T is the sampling period.

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[0031] Since the solution of the continuous-time state equation is the following equation (14), by substituting equation (14) into equation (10), the following equations (15) and (16) can be derived.

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[0032] When the zero-order hold is expressed as in the following equation (17), the state variable for each sampling period can be expressed as in the following equation (18) by substituting equation (17) into equation (16).

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[0033] Furthermore, the following equation (19) can be derived from equation (18).

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[0034] Next, by defining the following equations (20) and (21), the discrete-time state equation can be expressed as the following equation (22).

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[0035] By comparing the coefficients of equations (10) and (22), the coefficient matrix of the state equation can be expressed as the following equations (23) and (24).

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[0036] In this way, the discrete-time state equation is derived from the continuous-time state equation, which is a continuous-time prediction model. However, since equation (24) is an equation including integrals, it includes the motion of vehicle A in past periods. As a result, it is not possible to generate a prediction model for the current period with high accuracy. In response to this, the generation unit 122 derives the following equations (26) and (27) by using the following equation (25), which holds true for the matrix exponential functions of A and B.

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[0037] The generation unit 122 generates a discrete-time prediction model using equations (25) to (27). In this manner, the generation unit 122 operates, whereby the generation unit 122 can generate a highly accurate discrete-time prediction model. Furthermore, the generation unit 122 can omit calculating the motion of the vehicle A in past periods, and therefore can shorten the calculation time. As a result, the generation unit 122 can generate a highly accurate prediction model at a constant period.

[0038] The yaw rate determination unit 123 determines a target yaw rate r of the vehicle A. The yaw rate determination unit 123 determines a target yaw rate r that minimizes an evaluation function based on a discrete-time prediction model. Specifically, the yaw rate determination unit 123 determines a target yaw rate r that minimizes an evaluation function to which the acquired distance deviation z, azimuth deviation, and yaw rate are input, and includes, as parameters, an estimated distance deviation z, an estimated azimuth deviation, and a target yaw rate predicted from the prediction model. More specifically, the yaw rate determination unit 123 determines a target yaw rate r that minimizes an evaluation function to which the acquired distance deviation z, azimuth deviation, and yaw rate are input. More specifically, the yaw rate determination unit 123 determines a weight Q of a lateral deviation. z and the weight of the azimuth deviation Q θ and the weight R of the target yaw rate r r and the terminal weight of the lateral deviation Q zfinal and the terminal weight of the azimuth deviation Q θfinal and the following equation (28), the target yaw rate r is determined, where p is the length of the prediction horizon from the current time to a time a predetermined time ahead.

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[0039] The yaw rate determination unit 123 determines the target yaw rate r for each step from k=0 to P-1, which minimizes the evaluation function J. When the target yaw rate r for each step is determined, the slip angle corresponding to the target yaw rate r is estimated. In other words, when the estimated yaw rate for each step is determined, the influence of the slip angle corresponding to the target yaw rate r of the previous step is taken into consideration. This allows the yaw rate determination unit 123 to determine the target yaw rate r that can generate a slip angle that allows traveling along the target path B. Then, the yaw rate determination unit 123 notifies the steering angle determination unit 124 of the target yaw rate r for one step ahead (k=1) out of the multiple target yaw rates r determined by minimizing the evaluation function J.

[0040] The steering angle determination unit 124 determines the target steering angle δ of the vehicle A based on the target yaw rate r determined by the yaw rate determination unit 123. For example, when the yaw rate of the vehicle A represented by dotted φ acquired by the acquisition unit 121 and the target yaw rate r determined by the yaw rate determination unit 123 are input, the steering angle determination unit 124 determines the target steering angle δ of the vehicle A. Figure 3 is a block diagram showing an example of a process for determining the target steering angle δ. The block diagram is designed so that the difference between the yaw rate of the vehicle A and the standard response is small.

[0041] The normative response model T in Figure 3 d is the reference response model T corresponding to the transfer function expressed by equation (8). d C in Figure 3 ff is the feedforward controller. C fb is a feedback controller. The area G enclosed by the dashed line is a reference governor. The reference governor shapes the target signal through numerical optimization so as to satisfy the constraints on input and output and achieve good transient response. The constraints can be determined as appropriate through experiments, etc.

[0042] In addition, the feedforward controller C ff, feedback controller C fb , and the plant P are expressed by the following equations: K, K P and K I is a constant.

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[0043] The steering angle determination unit 124 determines the target steering angle using the formulas (29), (30), and (31). Then, the steering angle determination unit 124 notifies the steering control unit 125 of the determined target steering angle.

[0044] The steering control unit 125 operates the steering wheel 2 of the vehicle A to set the steering angle of the vehicle A to the target steering angle determined by the steering angle determination unit 124. For example, the steering control unit 125 controls an actuator that rotates the steering wheel 2 to rotate the steering wheel 2 so that the steering angle becomes the target steering angle. After the steering control unit 125 sets the steering angle to the target steering angle, the acquisition unit 121 again acquires the distance deviation z, the azimuth angle deviation θ, the vehicle speed V, the sideslip angle β, and the yaw rate.

[0045] In this way, the vehicle control device 1 mounted on vehicle A determines the target yaw rate r and the target steering angle δ based on the acquired information about vehicle A, and rotates the steering wheel 2 of vehicle A to the determined target steering angle δ. While vehicle A is traveling, the vehicle control device 1 repeatedly executes processes from acquiring information about vehicle A to rotating the steering wheel 2, thereby making it possible to cause vehicle A to travel along the target route B.

[0046] The effects of the control system S according to this embodiment will be described with reference to Fig. 4, Fig. 5, and Fig. 6. Fig. 4 is a schematic diagram of a target route B. As shown in Fig. 4, the target route B is a route with a large curvature that includes a mixture of straight lines and curves.

[0047] FIG. 5 is a schematic diagram of a travel locus E1 of a vehicle according to a comparative example. The vehicle according to the comparative example traveled under the control of a control system (hereinafter referred to as a "comparative example system") different from the control system S according to the present embodiment. The comparative example system controlled the vehicle to travel along the target route B. The areas in the travel locus E1 that are filled in dark gray are areas where the magnitude of the distance deviation z between the vehicle and the target route B is equal to or less than a predetermined value. The areas in the travel locus E1 that are filled in light gray are areas where the magnitude of the distance deviation z is greater than a predetermined value. The predetermined value is, for example, 10 centimeters. As shown in FIG. 5, the travel locus E1 has areas filled in light gray (areas surrounded by dashed lines). In other words, the vehicle that traveled under the control of the comparative example system cannot follow the target route B because the distance deviation z is greater than 10 centimeters, particularly at areas where a straight line switches to a curve or at a curve with a large curvature.

[0048] Fig. 6 is a schematic diagram of a travel path E2 of a vehicle A that traveled under the control of a control system S according to the present embodiment. As shown in Fig. 6, the travel path E2 is entirely filled in dark gray, and there are no parts filled in light gray. In other words, the distance deviation z of the vehicle A that traveled under the control of the control system S was always 10 centimeters or less.

[0049] The control system S uses a prediction model based on a transfer function of the sideslip angle β with respect to the yaw rate, making it possible to predict the sideslip angle β according to changes in the yaw rate. In other words, the prediction model is a model that predicts changes in the sideslip angle β when the yaw rate is changed. The yaw rate that minimizes the evaluation function J based on the prediction model, which includes the distance deviation z, the azimuth angle deviation θ, and the yaw rate as parameters, is a yaw rate that reduces the distance deviation z and the azimuth angle deviation θ, and is a yaw rate that can generate a sideslip angle that turns the vehicle A along the target path B.

[0050] In other words, by setting the yaw rate of vehicle A to the target yaw rate r determined by the control system S, a sideslip angle β is generated in vehicle A that allows vehicle A to turn along target route B, so vehicle A turns along target route B. As a result, vehicle A travels along target route B, so the control system S can improve the ability of vehicle A to follow target route B, which has a large curvature.

[0051] [Processing to control driving] 7 is a flowchart showing an example of a process for controlling the traveling of the vehicle A. The process for controlling the traveling of the vehicle A is executed while the vehicle A is traveling.

[0052] The acquisition unit 121 acquires information about the vehicle A (step S1). Specifically, the acquisition unit 121 acquires a distance deviation z between the vehicle A and a route point D of the target route B of the vehicle A, an azimuth angle deviation θ between the first azimuth angle ψ and the second azimuth angle φ, a sideslip angle β, and a yaw rate. In addition, the acquisition unit 121 acquires a yaw moment of inertia l of the vehicle A. z , the weight m of vehicle A, the first distance l from the center of gravity of vehicle A to the front wheel axle of vehicle A f , a second distance l from the center of gravity of vehicle A to the rear wheel axle of vehicle A r , the vehicle length l of vehicle A, and the rear wheel cornering stiffness K r Get the.

[0053] The generating unit 122 generates a prediction model that predicts a change in the sideslip angle β (step S2). Specifically, the generating unit 122 generates a continuous-time prediction model that predicts changes in each of the distance deviation z, the azimuth angle deviation θ, the sideslip angle β, and the second azimuth angle φ based on the distance deviation z, the azimuth angle deviation θ, the vehicle speed V, the sideslip angle β, the yaw rate, and the first azimuth angle ψ, and the transfer function expressed by equation (8). More specifically, the generating unit 122 generates a continuous-time state equation expressed by equation (9) as the prediction model.

[0054] The yaw rate determination unit 123 determines a target yaw rate that minimizes an evaluation function J that is expressed by equation (28) and corresponds to the prediction model (step S3). Specifically, the yaw rate determination unit 123 determines a target yaw rate r that minimizes an evaluation function that includes the estimated distance deviation z, the estimated azimuth angle deviation, and the target yaw rate r predicted from the prediction model as parameters and to which the acquired distance deviation z, azimuth angle deviation, and yaw rate are input.

[0055] The steering angle determination unit 124 outputs the target steering angle δ corresponding to the target yaw rate r determined by the yaw rate determination unit 123 (step S4). Specifically, the steering angle determination unit 124 determines the target steering angle δ by substituting the yaw rate acquired by the acquisition unit 121 and the target yaw rate r determined by the yaw rate determination unit 123 into a relational expression for determining the target steering angle of the vehicle A, and outputs the determined target steering angle δ to the steering control unit 125.

[0056] After the target steering angle δ is output, the acquisition unit 121 determines whether or not the vehicle A has stopped (step S5). If the vehicle A has stopped (Yes in step S5), the acquisition unit 121 ends the process of controlling the traveling of the vehicle A. If the vehicle A is traveling (No in step S5), the acquisition unit 121 returns to step S1 and acquires information about the vehicle A. Note that if the stopped vehicle A starts traveling, the acquisition unit 121 starts the process of controlling the traveling of the vehicle A.

[0057] [Effects of Control System S] As described above, the control system S acquires the distance deviation z between the route point D of the target route B of the vehicle A and the vehicle A, the azimuth angle deviation θ between the first direction angle ψ of the route point D and the second azimuth angle φ of the vehicle A, the yaw rate, the vehicle speed V, and the sideslip angle β of the vehicle A. Next, the control system S generates a transfer function that represents the relationship between the yaw rate and the sideslip angle β and outputs the sideslip angle β when the yaw rate is input. Next, the control system S generates a prediction model that predicts the changes in each of the distance deviation z, the azimuth angle deviation θ, the sideslip angle β, and the second azimuth angle φ based on the generated transfer function, the distance deviation z, the azimuth angle deviation θ, the vehicle speed V, the sideslip angle β, the yaw rate, and the first direction angle ψ. Then, the control system S determines the target yaw rate r that minimizes the value of the evaluation function J to which the acquired distance deviation z, the azimuth angle deviation θ, and the yaw rate are input, including the estimated distance deviation z, the estimated azimuth angle deviation θ, and the target yaw rate r predicted from the prediction model as parameters.

[0058] In this way, the control system S can predict the sideslip angle β according to the change in the yaw rate by using a prediction model based on the transfer function of the sideslip angle β to the yaw rate. The evaluation function J, which includes the distance deviation z, the azimuth angle deviation θ, and the yaw rate as parameters calculated from the prediction model, includes the change in the sideslip angle β due to the change in the yaw rate. Therefore, the yaw rate that minimizes the evaluation function J is the yaw rate that reduces the distance deviation z between the center of gravity C of the vehicle A and the path point D due to the sideslip angle β. In other words, the control system S can determine the target yaw rate r that generates the sideslip angle β that turns the vehicle A along the target path B. As a result, the vehicle A travels along the target path B with a large curvature, so the control system S can improve the tracking ability of the vehicle A to the target path B.

[0059] Although the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments, and various modifications and changes are possible within the scope of the gist of the present invention. For example, all or part of the device can be configured by distributing or integrating functionally or physically in any unit. In addition, new embodiments resulting from any combination of multiple embodiments are also included in the embodiments of the present invention. The effect of the new embodiment resulting from the combination combines the effect of the original embodiment. [Explanation of symbols]

[0060] 1 Vehicle control device 11 Storage section 12 Control section 121 Acquisition Department 122 Generation part 123 Yaw rate determination unit 124 Steering angle determination unit 125 Steering control unit A vehicle

Claims

1. an acquisition unit that acquires a distance deviation between a route point of a target route of the vehicle and the vehicle, an azimuth angle deviation between a first azimuth angle of the route point and a second azimuth angle of the vehicle, a yaw rate of the vehicle, a vehicle speed of the vehicle, and a sideslip angle of the vehicle; a generating unit that generates a prediction model that predicts changes in each of the distance deviation, the azimuth angle deviation, the sideslip angle, and the second azimuth angle based on the distance deviation, the azimuth angle deviation, the vehicle speed, the sideslip angle, the yaw rate, and the first azimuth angle, and a transfer function that represents a relationship between the yaw rate and the sideslip angle; a yaw rate determination unit that determines the target yaw rate to be the target yaw rate that minimizes a value of an evaluation function to which the obtained distance deviation, azimuth angle deviation, and yaw rate are input, the evaluation function including the estimated distance deviation, the estimated azimuth angle deviation, and the target yaw rate predicted from the prediction model as parameters; A control system having

2. The generation unit is generating said predictive model in continuous time; By discretizing the generated continuous-time prediction model, a discrete-time prediction model is generated that predicts a change in each of the distance deviation, the azimuth angle deviation, the sideslip angle, and the second azimuth angle in a unit time future. The control system of claim 1 .

3. the transfer function represents a relationship between the yaw rate and the second azimuth angle, and corresponds to a reference response model that outputs a change amount of the second azimuth angle when the yaw rate is input; The control system of claim 2.

4. a steering angle determination unit that determines a target steering angle of the vehicle when the yaw rate and the determined target yaw rate are input; The yaw rate determination unit notifies the steering angle determination unit of the target yaw rate. The control system of claim 1 .

5. a steering control unit that operates a steering wheel of the vehicle to set a steering angle of the vehicle to the target steering angle; The acquisition unit acquires the distance deviation, the azimuth angle deviation, the vehicle speed, the sideslip angle, and the yaw rate after the steering control unit sets the steering angle to the target steering angle.

5. The control system of claim 4.

6. the acquisition unit acquires a yaw moment of inertia of the vehicle, a weight of the vehicle, a first distance from a center of gravity of the vehicle to an axle of a front wheel of the vehicle, a second distance from a center of gravity of the vehicle to an axle of a rear wheel of the vehicle, a vehicle length of the vehicle, a rear wheel cornering stiffness, and a longitudinal vehicle speed in a traveling direction of the vehicle as the vehicle speed; The transfer function is the yaw moment of inertia; a first product of the weight, the longitudinal vehicle speed, and the first distance; a ratio of a product of the rear wheel cornering stiffness, the second distance, and the vehicle length to the longitudinal vehicle speed; A second product of the weight, the longitudinal vehicle speed, and the first distance; a third product of the rear wheel cornering stiffness and the vehicle length; is expressed using A control system according to any one of claims 1 to 5.

7. Executed by a processor installed in the vehicle, acquiring a distance deviation between a route point of a target route of the vehicle and the vehicle, an azimuth angle deviation between a first azimuth angle of the route point and a second azimuth angle of the vehicle, a yaw rate of the vehicle, a vehicle speed of the vehicle, and a sideslip angle of the vehicle; generating a prediction model for predicting changes in each of the distance deviation, the azimuth angle deviation, the sideslip angle, and the second azimuth angle based on the distance deviation, the azimuth angle deviation, the vehicle speed, the sideslip angle, the yaw rate, the first azimuth angle, and a transfer function that represents a relationship between the yaw rate and the sideslip angle and outputs the sideslip angle when the yaw rate is input; determining, as a target yaw rate, a target yaw rate that minimizes a value of an evaluation function to which the obtained distance deviation, azimuth angle deviation, and yaw rate are input, the evaluation function including, as parameters, an estimated distance deviation, an estimated azimuth angle deviation, and a target yaw rate predicted from the prediction model; The control method includes:

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