使用自适应模型预测控制器模拟车道辅助- Simulink - MathWorks Australia

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Model Predictive Control Toolbox / Automated Driving

Ports

我nput

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`Curvature`— Road curvature
sCalar

road curvature, specified as1/r，，，，whereris the radius of the curve in meters.

这road curvature is:

Positive when the road curves toward the positive Y axis of the global coordinate system.
Negative when the road curves toward the negative Y axis of the global coordinate system.
zero for a straight road.

控制器将道路曲率建模为预览的测量干扰。您可以将曲率指定为：

——指定咕咕叫曲率标量信号ent control interval. The controller uses this curvature value across the prediction horizon.
veCtor signal with length less than or equal to thePrediction Horizon— Specify the current and predicted curvature values across the prediction horizon. If the length of the vector is less than the prediction horizon, then the controller uses the final curvature value in the vector for the remainder of the prediction horizon.

`纵向速度`— Ego vehicle velocity
nonnegative scalar

Ego vehicle velocity in m/s.

`lateral deviation`— Ego vehicle lateral deviation
sCalar

自我车辆横向偏差，距车道中心线米。横向偏差e₁is positive when the ego vehicle is to the right of the centerline and negative when the ego vehicle is to the left of the centerline.

`relative yaw angle`— Angle from lane centerline
sCalar

Ego vehicle longitudinal axis angle in radians from the centerline of the lane, defined as:

$e_{2} = θ_{e} - θ_{C}$

Here,θ_eis the ego vehicle angle andθ_Cis the centerline angle, with both angles defined in the global coordinate frame.

`Minimum steering angle`- 最小前转向角
sCalar

Minimum front steering angle constraint in radians. Use this input port when the minimum steering angle varies at run time.

dependencies

To enable this port, selectUse external sourcefor theMinimum steering angleparameter.

`最大转向角`— Maximum front steering angle
sCalar

Maximum front steering angle constraint in radians. Use this input port when the maximum steering angle varies at run time.

dependencies

To enable this port, selectUse external sourcefor the最大转向角parameter.

`Enable optimization`— Controller optimization enable signal
sCalar

控制器优化启用信号。当此信号为：

Nonzero, the controller performs optimization calculations and generates aSteering angle控制信号。
zero, the controller does not perform optimization calculations. In this case, theSteering angle在禁用优化时，输出信号保持其值。控制器继续更新其内部状态估计。

dependencies

要启用此端口，请选择使用外部信号启用或禁用优化parameter.

`External control signal`- 将转向角应用于自我车辆
sCalar

一个Ctual steering angle in radians applied to the ego vehicle. The controller uses this signal to estimate the ego vehicle model states. Use this input port when the control signal applied to the ego vehicle does not match the optimal control signal computed by the model predictive controller. This mismatch can occur when, for example:

这lane Keeping Assist Systemis not the active controller. Maintaining an accurate state estimate when the controller is not active prevents bumps in the control signal when the controller becomes active.
这steering actuator fails and does not provide the correct control signal to the ego vehicle.

dependencies

要启用此端口，请选择使用外部控制信号进行PFC和其他控制器之间的漫不经心转移parameter.

`vehicle dynamics matrix A`— State matrix of ego vehicle predictive model
square matrix

State matrix of ego vehicle predictive model. The number of rows in the state matrix corresponds to the number of states in the predictive model. This matrix must be square.

这ego vehicle predictive model defined byvehicle dynamics matrix A，，，，vehicle dynamics matrix B，，，，andvehicle dynamics matrix Cmust be minimal.

dependencies

要启用此端口，请选择Use vehicle modelparameter.

`vehicle dynamics matrix B`— Input-to-state matrix of ego vehicle predictive model
Column vector

我nput-to-state matrix of ego vehicle predictive model. The number of rows in this signal must match the number of rows invehicle dynamics matrix A。

这ego vehicle predictive model defined byvehicle dynamics matrix A，，，，vehicle dynamics matrix B，，，，andvehicle dynamics matrix Cmust be minimal.

dependencies

要启用此端口，请选择Use vehicle modelparameter.

`vehicle dynamics matrix C`— State-to-output matrix of ego vehicle predictive model
带有两行的矩阵

State-to-output matrix of ego vehicle predictive model. The number of columns in this signal must match the number of rows invehicle dynamics matrix A。

这ego vehicle predictive model defined byvehicle dynamics matrix A，，，，vehicle dynamics matrix B，，，，andvehicle dynamics matrix Cmust be minimal.

dependencies

要启用此端口，请选择Use vehicle modelparameter.

输出

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`Steering angle`- 前转向角控制信号
sCalar

控制器生成的弧度中的前转向角控制信号。前转向角是从车辆的纵轴前轮胎的角度。转向角度朝向自我车辆的正侧轴。

参数

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参数选项卡

Ego Vehicle

`Use vehicle parameters`— Define ego vehicle model using vehicle properties
`on`（默认）|`off`

Select this parameter to define the ego vehicle model used by the MPC controller by specifying properties of the ego vehicle. The ego vehicle model is the linear model from the front steering angle to the lateral velocity and yaw angle rate. For more information, seeEgo Vehicle Predictive Model。

要定义车辆模型，请指定以下块参数：

Total mass
Yaw moment of inertia
从重心到前轮胎的纵向距离
longitudinal distance from center of gravity to rear tires
Cornering stiffness of front tires
后轮胎的转弯刚度

有关自我车辆型号的更多信息，请参见Ego Vehicle Predictive Model。

Selecting this parameter clears theUse vehicle modelparameter.

程序化使用

block Parameter:ModelType

类型：string, character vector

默认："Use vehicle parameters"

`Use vehicle model`— Define ego vehicle model using state-space matrices
`off`（默认）|`on`

选择此参数以定义MPC控制器使用的自我车辆模型的状态空间矩阵。该模型是从弧度的前转向角到横向速度的线性模型，每秒以米为单位，偏航角度速率为每秒。有关自我车辆型号的更多信息，请参见Ego Vehicle Predictive Model。

To define the initial internal model, specify the一个，，，，b，，，，andCstate-space matrices. The internal model must be a minimal realization with no direct feedthrough, and the dimensions of一个，，，，b，，，，andCmust be consistent.

Typically, the ego vehicle steering model is velocity-dependent, and therefore, it varies over time. To update the internal model at run time, use thevehicle dynamics A，，，，vehicle dynamics B，，，，andvehicle dynamics Cinput ports.

Selecting this parameter clears theUse vehicle parametersparameter.

程序化使用

block Parameter:ModelType

类型：string, character vector

默认："Use vehicle parameters"

`Total mass`- 自我车辆质量
`1575年`（默认）|positive scalar

自我车辆质量在公斤。

dependencies

To enable this parameter, select theUse vehicle parametersparameter.

程序化使用

block Parameter:vehicleMass

类型：string, character vector

默认："1575"

`Yaw moment of inertia`— Moment of inertia about the ego vehicle vertical axis
`2875`（默认）|positive scalar

Moment of inertia about the ego vehicle vertical axis in Kg·m²。

dependencies

To enable this parameter, select theUse vehicle parametersparameter.

程序化使用

block Parameter:vehicleYawInertia

类型：string, character vector

默认："2875"

`从重心到前轮胎的纵向距离`— Distance from the ego vehicle center of mass to its front tires
`1。2`（默认）|positive scalar

从质量的自我车辆中心到其前轮胎的距离，沿车辆的纵轴测量。

dependencies

To enable this parameter, select theUse vehicle parametersparameter.

程序化使用

block Parameter:lengthToFront

类型：string, character vector

默认："1.2"

`longitudinal distance from center of gravity to rear tires`— Distance from the ego vehicle center of mass to its rear tires
`1。6`（默认）|positive scalar

distance from the ego vehicle center of mass to its rear tires in meters, measured along the longitudinal axis of the vehicle.

dependencies

To enable this parameter, select theUse vehicle parametersparameter.

程序化使用

block Parameter:lengthToRear

类型：string, character vector

默认："1.6"

`Cornering stiffness of front tires`— Front tire stiffness
`19000`（默认）|positive scalar

Front tire stiffness in N/rad, defined as the relationship between the side force on the front tires and the angle of the tires to the longitudinal axis of the vehicle.

dependencies

To enable this parameter, select theUse vehicle parametersparameter.

程序化使用

block Parameter:FrontTireStiffness

类型：string, character vector

默认："19000"

`后轮胎的转弯刚度`— Rear tire stiffness
`33000`（默认）|positive scalar

rear tire stiffness in N/rad, defined as the relationship between the side force on the rear tires and the angle of the tires to the longitudinal axis of the vehicle.

dependencies

To enable this parameter, select theUse vehicle parametersparameter.

程序化使用

block Parameter:rearTireStiffness

类型：string, character vector

默认：“ 33000”

`一个`— Initial state matrix of ego vehicle predictive model
square matrix

我nitial state matrix of ego vehicle predictive model. The number of rows in the state matrix corresponds to the number of states in the predictive model. This matrix must be square.

最初的自我车辆预测模型由一个，，，，b，，，，andCmust be minimal.

通常，自我车辆模型会随着时间而变化。要在运行时更新状态矩阵，请使用vehicle dynamics Ainput port.

dependencies

To enable this parameter, select theUse vehicle modelparameter.

程序化使用

block Parameter:EgoModelMatrixA

类型：string, character vector

默认：“ [-4.4021，-12.4603; 1.3913，-5.1868]”

`b`— Initial input-to-state matrix of ego vehicle predictive model
Column vector

我nitial input-to-state matrix of ego vehicle predictive model. The number of rows in this parameter must match the number of rows in一个。

最初的自我车辆预测模型由一个，，，，b，，，，andCmust be minimal.

通常，自我车辆模型会随着时间而变化。To update the input-to-state matrix at run time, use thevehicle dynamics Binput port.

dependencies

To enable this parameter, select theUse vehicle modelparameter.

程序化使用

block Parameter:EgoModelMatrixB

类型：string, character vector

默认：“ [24.1270; 15.8609]”

`C`— Initial state-to-output matrix of ego vehicle predictive model
带有两行的矩阵

我nitial state-to-output matrix of ego vehicle predictive model. The number of columns in this parameter must match the number of rows in一个。

最初的自我车辆预测模型由一个，，，，b，，，，andCmust be minimal.

通常，自我车辆模型会随着时间而变化。To update the state-to-output matrix at run time, use thevehicle dynamics Cinput port.

dependencies

To enable this parameter, select theUse vehicle modelparameter.

程序化使用

block Parameter:EgoModelMatrixC

类型：string, character vector

默认："[1,0;0,1]"

`最初的纵向速度`— Initial velocity of the ego vehicle
`15`（默认）|positive scalar

当自我车辆的初始速度模型lane-keeping assist is enabled in m/s. This velocity can differ from the actual ego vehicle initial velocity.

笔记

一个very small initial velocity, for exampleeps，，，，Can produce a nonminimal realization for the controller plant model, causing an error. To prevent this error, set the initial velocity to a larger value, for example1e-3。

程序化使用

block Parameter:初始长篇小说

类型：string, character vector

默认："15"

`模型输入和输出之间的传输滞后`— Total transport lag in ego vehicle model
`0`（默认）|nonnegative scalar

Total transport lag,τ，在自我车辆型号中以秒为单位。该滞后包括执行器，传感器和通信滞后。对于每个输入输出通道，传输滞后近似：

$\frac{1}{τ s + 1}$

程序化使用

block Parameter:TransportLag

类型：string, character vector

默认："0"

lane Keeping Controller Constraints

`Minimum steering angle`- 最小前转向角
`-0.26`（默认）|之间的标量`-pi/2`and`pi/2`

Minimum front steering angle constraint in radians.

我f the minimum steering angle varies over time, add theMinimum steering angle通过选择输入端口块Use external source。

dependencies

This parameter must be less than the最大转向角parameter.

程序化使用

block Parameter:MinSteering

类型：string, character vector

默认：“ -0.26”

`最大转向角`— Maximum front steering angle
`0.26`（默认）|之间的标量`-pi/2`and`pi/2`

Maximum front steering angle constraint in radians.

如果最大转向角随时间变化，请添加最大转向角通过选择输入端口块Use external source。

dependencies

此参数必须大于Minimum steering angleparameter.

程序化使用

block Parameter:MaxSteering

类型：string, character vector

默认：“ 0.26”

Model Predictive Controller Settings

`Sample time`- 控制器样本时间
`0。1`（默认）|positive scalar

Controller sample time in seconds.

程序化使用

block Parameter:Ts

类型：string, character vector

默认："0.1"

`预测范围`— Controller prediction horizon
`10`（默认）|positive integer

控制器的公关ediction horizon steps. The controller prediction time is the product of the sample time and the prediction horizon.

程序化使用

block Parameter:PredictionHorizon

类型：string, character vector

默认："30"

`Controller behavior`— Closed-loop controller performance
`0。5`（默认）|之间的标量`0`and`1`

Closed-loop controller performance. The default parameter value provides a balanced controller design. Specifying a:

Smaller value produces a more robust controller with smoother control actions.
较大的价值会产生更具侵略性的控制器，并具有更快的响应时间。

When you modify this parameter, the change is applied to the controller immediately.

程序化使用

block Parameter:ControllerBehavior

类型：string, character vector

默认："0.5"

block Tab

`使用次优的解决方案`— Apply suboptimal solution after specified number of iterations
`off`（默认）|`on`

Configure the controller to apply a suboptimal solution after a specified maximum number of iterations, which guarantees the worst-case execution time for your controller.

For more information, seeSuboptimal QP Solution。

dependencies

选择此参数后，指定Maximum iteration numberparameter.

程序化使用

block Parameter:suboptimal

类型：string, character vector

默认："off"

`Maximum iteration number`— Maximum optimization iterations
`10`（默认）|positive integer

Maximum number of controller optimization iterations.

dependencies

To enable this parameter, select the使用次优的解决方案parameter.

程序化使用

block Parameter:Maxiter

类型：string, character vector

默认："10"

`使用外部信号启用或禁用优化`— Add port for enabling optimization
`off`（默认）|`on`

To add theEnable optimization输入块到块，选择此参数。

程序化使用

block Parameter:optmode

类型：string, character vector

默认："off"

`使用外部信号进行LKA和其他控制器之间的易于转移`— Add external control signal input port
`off`（默认）|`on`

To add theExternal control signal输入块到块，选择此参数。

程序化使用

block Parameter:trackmode

类型：string, character vector

默认："off"

`Create LKA subsystem`— Create custom controller
button

Generate a custom LKA subsystem, which you can modify for your application. The controller configuration data for the custom controller is exported to the MATLAB^®workspace as a structure.

You can modify the custom controller subsystem to:

修改默认MPC设置或使用高级MPC功能。
Modify the default controller initial conditions.

Model Examples

lane Keeping Assist System Using Model Predictive Control

设计一个MPC控制器，该控制器可以通过调节前转向角来保持自我车辆沿着直线或弯曲道路的中心行驶。

Open Script

lane Keeping Assist with Lane Detection

design an MPC-based lane-keeping assist system that uses lane detection and road curvature previewing from the Automated Driving Toolbox™.

Open Model

一个lgorithms

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Ego Vehicle Predictive Model

这default ego vehicle predictive model is the following state-space model:

$\begin{array}{l} 一个 = [[\begin{matrix} - 2 （（ C_{F} + C_{r} ） / m / v_{X} & - v_{X} - 2 （（ C_{F} l_{F} - C_{r} l_{r} ） / m / v_{X} \\ - 2 （（ C_{F} l_{F} - C_{r} l_{r} ） / 我_{z} / v_{X} & - 2 （（ C_{F} l_{F}^{2} + C_{r} l_{r}^{2} ） / 我_{z} / v_{X} \end{matrix} 这是给予的 \\ b = 2 C_{F} [[\begin{matrix} 1 / m \\ l_{F} / 我_{z} \end{matrix} 这是给予的 \\ C = [[\begin{matrix} 1 & 0 \\ 0 & 1 \end{matrix} 这是给予的 \\ d = [[\begin{matrix} 0 \\ 0 \end{matrix} 这是给予的 \end{array}$

Here:

v_Xis the longitudinal velocity of the car. At the start of the simulation, this velocity is equal to the我nitial condition for longitudinal velocityparameter. At run time, this velocity is equal to the纵向速度input signal.
mis theTotal massparameter.
我_zis theYaw moment of inertiaparameter.
l_Fis the从重心到前轮胎的纵向距离parameter.
l_ris thelongitudinal distance from center of gravity to rear tiresparameter.
C_Fis theCornering stiffness of front tiresparameter.
C_ris the后轮胎的转弯刚度parameter.

该模型的输入是弧度的转向角，输出是以每秒米为单位的横向速度，而每秒弧度的偏航角速率为偏航角速率。

To define a different ego vehicle predictive model, select theUse vehicle model参数，并指定初始状态空间模型。然后，使用该状态空间矩阵的运行时值vehicle dynamics A，，，，vehicle dynamics B，，，，andvehicle dynamics C输入信号。

这Controller creates its internal predictive model by augmenting the ego vehicle dynamic model. The augmented model includes the road curvature as a measured disturbance input signal.

初始状态

by default, the model predictive controller assumes the following initial conditions for the ego vehicle:

纵向速度is equal to the最初的纵向速度parameter.
侧速度为零。
Steering angle is zero.
Yaw angle rate is zero.

如果您的模型中的初始条件与这些条件不符，则Steering angleoutput can exhibit an initial bump at the start of the simulation.

To modify the controller initial conditions to match your simulation, create a custom lane-keeping control system by, on theblock选项卡，单击Create LKA subsystem。

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版本历史记录

也可以看看

blocks

一个daptive MPC Controller|一个daptive Cruise Control System|路径跟随控制系统

lane Keeping Assist System

desCription

Ports

我nput

Curvature— Road curvaturesCalar

纵向速度— Ego vehicle velocitynonnegative scalar

lateral deviation— Ego vehicle lateral deviationsCalar

relative yaw angle— Angle from lane centerlinesCalar

Minimum steering angle- 最小前转向角sCalar

dependencies

最大转向角— Maximum front steering anglesCalar

dependencies

Enable optimization— Controller optimization enable signalsCalar

dependencies

External control signal- 将转向角应用于自我车辆sCalar

dependencies

vehicle dynamics matrix A— State matrix of ego vehicle predictive modelsquare matrix

dependencies

vehicle dynamics matrix B— Input-to-state matrix of ego vehicle predictive modelColumn vector

dependencies

vehicle dynamics matrix C— State-to-output matrix of ego vehicle predictive model带有两行的矩阵

dependencies

输出

Steering angle- 前转向角控制信号sCalar

参数

参数选项卡

Use vehicle parameters— Define ego vehicle model using vehicle propertieson（默认）|off

程序化使用

Use vehicle model— Define ego vehicle model using state-space matricesoff（默认）|on

程序化使用

Total mass- 自我车辆质量1575年（默认）|positive scalar

dependencies

程序化使用

Yaw moment of inertia— Moment of inertia about the ego vehicle vertical axis2875（默认）|positive scalar

dependencies

程序化使用

从重心到前轮胎的纵向距离— Distance from the ego vehicle center of mass to its front tires1。2（默认）|positive scalar

dependencies

程序化使用

longitudinal distance from center of gravity to rear tires— Distance from the ego vehicle center of mass to its rear tires1。6（默认）|positive scalar

dependencies

程序化使用

Cornering stiffness of front tires— Front tire stiffness19000（默认）|positive scalar

dependencies

程序化使用

后轮胎的转弯刚度— Rear tire stiffness33000（默认）|positive scalar

dependencies

程序化使用

一个— Initial state matrix of ego vehicle predictive modelsquare matrix

dependencies

程序化使用

b— Initial input-to-state matrix of ego vehicle predictive modelColumn vector

dependencies

程序化使用

C— Initial state-to-output matrix of ego vehicle predictive model带有两行的矩阵

dependencies

程序化使用

最初的纵向速度— Initial velocity of the ego vehicle15（默认）|positive scalar

程序化使用

模型输入和输出之间的传输滞后— Total transport lag in ego vehicle model0（默认）|nonnegative scalar

程序化使用

Minimum steering angle- 最小前转向角-0.26（默认）|之间的标量-pi/2andpi/2

dependencies

程序化使用

最大转向角— Maximum front steering angle0.26（默认）|之间的标量-pi/2andpi/2

dependencies

程序化使用

Sample time- 控制器样本时间0。1（默认）|positive scalar

程序化使用

预测范围— Controller prediction horizon10（默认）|positive integer

程序化使用

Controller behavior— Closed-loop controller performance0。5（默认）|之间的标量0and1

程序化使用

block Tab

使用次优的解决方案— Apply suboptimal solution after specified number of iterationsoff（默认）|on

dependencies

程序化使用

Maximum iteration number— Maximum optimization iterations10（默认）|positive integer

dependencies

程序化使用

`Curvature`— Road curvature
sCalar

`纵向速度`— Ego vehicle velocity
nonnegative scalar

`lateral deviation`— Ego vehicle lateral deviation
sCalar

`relative yaw angle`— Angle from lane centerline
sCalar

`Minimum steering angle`- 最小前转向角
sCalar

`最大转向角`— Maximum front steering angle
sCalar

`Enable optimization`— Controller optimization enable signal
sCalar

`External control signal`- 将转向角应用于自我车辆
sCalar

`vehicle dynamics matrix A`— State matrix of ego vehicle predictive model
square matrix

`vehicle dynamics matrix B`— Input-to-state matrix of ego vehicle predictive model
Column vector

`vehicle dynamics matrix C`— State-to-output matrix of ego vehicle predictive model
带有两行的矩阵

`Steering angle`- 前转向角控制信号
sCalar

`Use vehicle parameters`— Define ego vehicle model using vehicle properties
`on`（默认）|`off`

`Use vehicle model`— Define ego vehicle model using state-space matrices
`off`（默认）|`on`

`Total mass`- 自我车辆质量
`1575年`（默认）|positive scalar

`Yaw moment of inertia`— Moment of inertia about the ego vehicle vertical axis
`2875`（默认）|positive scalar

`从重心到前轮胎的纵向距离`— Distance from the ego vehicle center of mass to its front tires
`1。2`（默认）|positive scalar

`longitudinal distance from center of gravity to rear tires`— Distance from the ego vehicle center of mass to its rear tires
`1。6`（默认）|positive scalar

`Cornering stiffness of front tires`— Front tire stiffness
`19000`（默认）|positive scalar

`后轮胎的转弯刚度`— Rear tire stiffness
`33000`（默认）|positive scalar

`一个`— Initial state matrix of ego vehicle predictive model
square matrix

`b`— Initial input-to-state matrix of ego vehicle predictive model
Column vector

`C`— Initial state-to-output matrix of ego vehicle predictive model
带有两行的矩阵

`最初的纵向速度`— Initial velocity of the ego vehicle
`15`（默认）|positive scalar

`模型输入和输出之间的传输滞后`— Total transport lag in ego vehicle model
`0`（默认）|nonnegative scalar

`Minimum steering angle`- 最小前转向角
`-0.26`（默认）|之间的标量`-pi/2`and`pi/2`

`最大转向角`— Maximum front steering angle
`0.26`（默认）|之间的标量`-pi/2`and`pi/2`

`Sample time`- 控制器样本时间
`0。1`（默认）|positive scalar

`预测范围`— Controller prediction horizon
`10`（默认）|positive integer

`Controller behavior`— Closed-loop controller performance
`0。5`（默认）|之间的标量`0`and`1`

`使用次优的解决方案`— Apply suboptimal solution after specified number of iterations
`off`（默认）|`on`

`Maximum iteration number`— Maximum optimization iterations
`10`（默认）|positive integer

`使用外部信号启用或禁用优化`— Add port for enabling optimization
`off`（默认）|`on`

`使用外部信号进行LKA和其他控制器之间的易于转移`— Add external control signal input port
`off`（默认）|`on`

`Create LKA subsystem`— Create custom controller
button

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