WO2021238747A1 - 自动驾驶车辆横向运动控制方法、装置和自动驾驶车辆 - Google Patents
自动驾驶车辆横向运动控制方法、装置和自动驾驶车辆 Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D15/00—Steering not otherwise provided for
- B62D15/02—Steering position indicators ; Steering position determination; Steering aids
- B62D15/025—Active steering aids, e.g. helping the driver by actively influencing the steering system after environment evaluation
Definitions
- the present application relates to the technical field of automatic driving vehicles, and in particular to a method and device for controlling lateral movement of an automatic driving vehicle, and an automatic driving vehicle.
- the lateral motion control of the autonomous vehicle calculates the desired steering command in real time according to the planned trajectory and vehicle positioning information of the upper layer, and controls the vehicle to drive along the target trajectory.
- the optimal control parameter gain is generally obtained by solving the Riccati equation online, which requires a large amount of calculation. , It takes up more computing resources.
- This application aims to solve or improve at least one of the above technical problems.
- the first objective of the present application is to provide a method for controlling the lateral movement of an autonomous vehicle.
- the second objective of the present application is to provide a lateral motion control device for an automatic driving vehicle.
- the third purpose of this application is to provide an autonomous driving vehicle.
- an embodiment of the present application provides a method for controlling lateral movement of an automatic driving vehicle, which includes: setting control parameters of a linear quadratic controller; acquiring overall vehicle parameters; according to the control parameters and the overall vehicle Parameters, obtain the control parameter gain; according to the control parameter gain, obtain the real-time control parameter gain; obtain the state error feedback amount, trajectory curvature and vehicle inclination angle; obtain according to the real-time control parameter gain, state error feedback amount, trajectory curvature and vehicle inclination angle Control amount and compensation amount; according to the control amount and compensation amount, obtain the desired steering wheel angle, and output the desired steering wheel angle to the steer-by-wire system.
- This embodiment can realize the optimal control of the lateral motion of the autonomous vehicle without static error, greatly reduce the amount of calculation, and ensure the convergence of the optimal control gain, and consider the influence of road curvature, road inclination and uncertainty of overall vehicle parameters. In order to achieve high reliability and high precision tracking the desired driving trajectory.
- q 1max is the maximum value of q 1
- q 1min is the minimum value of q 1
- q 3max is the maximum value of q 3
- q 3min is the minimum value of q 3
- V x is the vehicle longitudinal speed
- V 1 is the first speed threshold
- V 2 is the second speed threshold.
- q2, q4, r are set to fixed values, q1, q3 are adjusted linearly according to the longitudinal vehicle speed V x of the vehicle, that is, the weight matrix of the control parameters of the linear quadratic controller adopts a speed linear adjustment method , Can greatly reduce the amount of calculation.
- control parameter gain is obtained by the following formula:
- k 14 [k 14(1) , k 14(2) , k 14(3) , k 14(4) ]
- m is the mass of the vehicle
- I z is the moment of inertia of the vehicle around the vertical direction
- l f is the distance from the front axle of the vehicle to the center of mass
- l r is the distance from the rear axle of the vehicle to the center of mass
- C f is the cornering stiffness of the front wheels
- C r is the cornering stiffness of the rear wheels
- V x is the longitudinal speed of the vehicle
- k 14 is the control gain convergence matrix
- k 14 is the optimal control gain convergence matrix calculated by MATLAB
- k 1 , k 2 , k 3 , and k 4 are the four control parameter gains.
- the overall parameters of the vehicle include: the mass of the vehicle and/or the moment of inertia of the vehicle around the vertical direction and/or the distance from the front axle of the vehicle to the center of mass and/or the distance from the rear axle of the vehicle to the center of mass and/or the cornering stiffness of the front wheels and/or The cornering stiffness of the rear wheel.
- the optimal control parameter gains k 1 , k 2 , k 3 , k 4 are calculated offline through the MATLAB function lqr, which greatly reduces the amount of calculation compared with the online solution of the control parameter gains.
- obtaining the real-time control parameter gain according to the control parameter gain includes: taking the speed as the independent variable, performing the control parameter gain polynomial fitting to obtain the first polynomial; according to the first polynomial and the real-time vehicle Longitudinal vehicle speed, obtain real-time control parameter gain.
- the calculation method of polynomial fitting is adopted to obtain the real-time control parameter gain, and the calculation amount is optimized.
- obtaining the real-time control parameter gain according to the control parameter gain includes: taking the speed as the independent variable, performing the third-order polynomial fitting of the control parameter gain, and obtaining the first polynomial as:
- K [k′ 1 ,k′ 2 ,k′ 3 ,k′ 4 ]
- V x is the longitudinal speed of the vehicle.
- This embodiment adopts third-order polynomial fitting to calculate the optimal control parameter gain in real time according to the actual speed of the vehicle feedback, which not only reduces the calculation amount of the optimal control parameter gain, but also ensures the convergence and the optimal control parameter gain. reliability.
- obtaining the state error feedback amount includes:
- e y is the lateral position deviation
- e ⁇ is the heading angle deviation
- X is the state error feedback amount
- V x is the longitudinal vehicle speed of the vehicle
- V y is the lateral vehicle speed of the vehicle
- (x, y) is the vehicle position at the current moment
- (x des , y des ) is the desired trajectory position
- ⁇ is the heading angle of the vehicle at the current moment
- ⁇ des is the heading angle of the desired trajectory
- ⁇ is the yaw rate of the vehicle
- ⁇ is the curvature of the desired target point.
- obtaining the control amount and the compensation amount according to the control parameter gain, real-time control parameter gain, state error, trajectory curvature and vehicle inclination angle includes:
- the feedback control amount obtained is:
- K is the real-time control parameter gain
- X is the state error feedback amount
- the obtained position error integral control quantity is:
- k i is the integral coefficient
- e y is the lateral position deviation
- T is the control period
- ⁇ 'sw_i is the value of the previous beat control period of ⁇ sw_i;
- the feedforward compensation amount for the road curvature is obtained as:
- m is the mass of the vehicle
- V x is the longitudinal speed of the vehicle
- l f is the distance from the front axle of the vehicle to the center of mass
- l r is the distance from the rear axle of the vehicle to the center of mass
- C f is the cornering stiffness of the front wheels
- C r is The cornering stiffness of the rear wheel
- R des is the trajectory radius
- L is the sum of l f and l r;
- the feedforward compensation amount obtained for the road inclination is:
- A(i,j) is the parameter corresponding to the i-th row and j-th column of matrix A
- B(i,j) is the parameter corresponding to the i-th row and jth column of matrix B
- g is the acceleration of gravity
- ⁇ the vehicle inclination angle
- This embodiment uses the road curvature provided by the desired trajectory to feed forward the road curvature Compensation, feed forward compensation for road inclination using positioning information, introduction of position deviation integral term to reduce position error, comprehensive consideration of the influence of road curvature, road inclination and uncertainty of overall vehicle parameters to achieve highly reliable and high-precision tracking of expected driving Trajectory.
- obtaining the desired steering wheel angle according to the control amount and the compensation amount, and outputting the desired steering wheel angle to the steer-by-wire system includes:
- ⁇ sw ( ⁇ sw_b + ⁇ sw_r + ⁇ sw_c + ⁇ sw_i )i
- ⁇ sw_b is the feedback control quantity
- ⁇ sw_i is the position error integral control quantity
- ⁇ sw_c is the road curvature feedforward compensation quantity
- ⁇ sw_r is the road tilt feedforward compensation quantity
- i is the steering wheel ratio.
- the sum is added, and then multiplied by the steering wheel ratio to obtain the final desired steering wheel angle, which is output to the steer-by-wire system. Realize path tracking to ensure that the steady-state error can be completely eliminated when the overall parameters of the vehicle are uncertain.
- an embodiment of the present application provides a lateral motion control device for an automatic driving vehicle, including: a setting module, a first acquisition module, a second acquisition module, a third acquisition module, and a fourth acquisition module 5.
- the fifth acquisition module and the sixth acquisition module wherein the control parameters of the linear quadratic controller are set through the setting module, the first acquisition module acquires the overall vehicle parameters, and the second acquisition module acquires control based on the control parameters and the overall vehicle parameters Parameter gain, the third acquisition module acquires real-time control parameter gain according to the control parameter gain, the fourth acquisition module acquires the state error, trajectory curvature and vehicle inclination angle, and the fifth acquisition module acquires the control parameter gain, real-time control parameter gain, state error, The trajectory curvature and the vehicle inclination angle obtain the control amount and the compensation amount.
- the sixth acquisition module obtains the desired steering wheel angle according to the control amount and the compensation amount, and outputs the desired steering wheel angle to the steer-by-wire system.
- This embodiment can realize the optimal control of the lateral motion of the autonomous vehicle without static error, greatly reduce the amount of calculation, and ensure the convergence of the optimal control gain, and consider the influence of road curvature, road inclination and uncertainty of overall vehicle parameters. In order to achieve high reliability and high precision tracking the desired driving trajectory.
- an embodiment of the present application provides an automatic driving vehicle, including: a vehicle body and a control device, wherein the control device adopts the lateral motion control method of the automatic driving vehicle as in any embodiment of the present application Control the running of the vehicle body.
- the self-driving vehicle provided by the embodiment of the present application implements the steps of the lateral motion control method of the self-driving vehicle as in any embodiment of the present application, and therefore it has all the beneficial effects of the lateral motion control method of the self-driving vehicle in any embodiment of the present application , I won’t repeat it here.
- Figure 1 is a schematic diagram of the lateral motion control of an autonomous vehicle in the related art
- FIG. 2 is a flowchart of a method for controlling lateral movement of an autonomous vehicle according to an embodiment of the application
- FIG. 3 is a flowchart of a method for obtaining real-time control parameter gains according to an embodiment of the application
- FIG. 4 is a flowchart of a method for obtaining a control amount and a compensation amount according to an embodiment of the application
- FIG. 5 is a structural block diagram of an apparatus for controlling lateral movement of an automatic driving vehicle according to an embodiment of the application
- FIG. 6 is a block diagram of the structure of an autonomous vehicle according to an embodiment of the application.
- FIG. 7 is a schematic diagram of lateral motion control of an autonomous vehicle according to a specific embodiment of the application.
- FIG. 8 is a flowchart of a method for controlling lateral movement of an autonomous vehicle according to a specific embodiment of the application.
- 100 Lateral motion control device for an autonomous vehicle
- 110 Setting module
- 120 First acquisition module
- 130 Second acquisition module
- 140 Third acquisition module
- 150 Fourth acquisition module
- 160 Fifth acquisition module
- 170 Sixth acquisition module
- 200 Autonomous driving vehicle
- 210 Vehicle body
- 220 Control device.
- the lateral motion control of autonomous vehicles calculates the expected steering commands in real time according to the planned trajectory and vehicle positioning information of the upper layer, and controls the vehicle to travel along the desired trajectory.
- LQR linear quadratic optimal control
- Optimal control gain is obtained by solving the Riccati equation online, which takes up more computing resources and may not guarantee the convergence of the control gain.
- the current lateral motion control system of automatic driving vehicles mostly adopts the introduction of road curvature feedforward compensation to reduce the steady-state error, and does not consider the lateral steady-state error caused by the inclination of the road, which reduces the quality of the vehicle lateral control system.
- a related technology is a parameter adaptive lateral motion LQR control method for self-driving cars.
- the LQR control parameter adjustment strategy based on the path tracking error and the vehicle-road position relationship determines the controller parameters in the current state. According to the determined controller parameters, Calculate the steering control amount of the self-driving car and pass it to the steering actuator for execution. The difference from this embodiment is:
- the parameter adjustment strategy is different.
- This related technology uses the path tracking error and the parameter adjustment strategy of the vehicle-road position relationship.
- This embodiment uses the speed parameter adjustment strategy.
- the optimal control gain calculation process is different. This related technology solves the Riccati equation online to obtain the optimal control gain. In this embodiment, the optimal control gain is calculated offline, and then the control gain is fitted through a polynomial and updated in real time.
- the steering wheel angle is different.
- This related technology only considers the state error feedback control amount.
- the road curvature feedforward control amount, the road tilt feedforward compensation amount, and the position error integral control amount are also considered.
- the purpose of this embodiment is to solve at least one of the following problems:
- this embodiment provides a method for controlling lateral movement of an autonomous vehicle, including:
- Step S102 setting the control parameters of the linear quadratic controller
- Step S104 obtaining overall vehicle parameters
- Step S106 obtaining a control parameter gain according to the control parameter and the overall vehicle parameter
- Step S108 obtaining real-time control parameter gain according to the control parameter gain
- Step S110 obtain the state error feedback amount, the trajectory curvature and the vehicle inclination angle
- Step S112 obtaining the control amount and the compensation amount according to the real-time control parameter gain, the state error feedback amount, the trajectory curvature and the vehicle inclination angle;
- Step S114 Obtain a desired steering wheel angle according to the control amount and the compensation amount, and output the desired steering wheel angle to the steer-by-wire system.
- This embodiment can realize the optimal control of the lateral motion of the autonomous vehicle without static error, greatly reduce the amount of calculation, and ensure the convergence of the optimal control gain, and consider the influence of road curvature, road inclination and uncertainty of overall vehicle parameters. In order to achieve high reliability and high precision tracking the desired driving trajectory.
- this embodiment further includes the following technical features.
- q 1max is the maximum value of q 1
- q 1min is the minimum value of q 1
- q 3max is the maximum value of q 3
- q 3min is the minimum value of q 3
- V x is the vehicle longitudinal speed
- V 1 is the first speed threshold
- V 2 is the second speed threshold.
- q2, q4, r are set to fixed values, and only q1, q3 are linearly adjusted according to the vehicle speed V x , that is, the weight matrix of the control parameter of the linear quadratic controller adopts the speed linear adjustment Method to reduce the amount of calculation.
- this embodiment further includes the following technical features.
- control parameter gain including: the control parameter gain is obtained by the following formula:
- k 14 [k 14(1) , k 14(2) , k 14(3) , k 14(4) ]
- m is the mass of the vehicle
- I z is the moment of inertia of the vehicle around the vertical direction
- l f is the distance from the front axle of the vehicle to the center of mass
- l r is the distance from the rear axle of the vehicle to the center of mass
- C f is the cornering stiffness of the front wheels
- C r is the cornering stiffness of the rear wheels
- V x is the longitudinal speed of the vehicle
- k 14 is the control gain convergence matrix
- k 14 is the optimal control gain convergence matrix calculated by MATLAB
- k 1 , k 2 , k 3 , and k 4 are the four control parameter gains.
- the overall parameters of the vehicle include: the mass of the vehicle and/or the moment of inertia of the vehicle around the vertical direction and/or the distance from the front axle of the vehicle to the center of mass and/or the distance from the rear axle of the vehicle to the center of mass and/or the cornering stiffness of the front wheels and / Or the cornering stiffness of the rear wheel.
- the overall vehicle parameters are obtained, and the control parameter gain is obtained offline according to the overall vehicle parameters, which reduces the amount of calculation in the method of this embodiment.
- the optimal control parameter gains k 1 , k 2 , k 3 , k 4 are calculated offline through the MATLAB function lqr, which greatly reduces the amount of calculation compared with the online solution of the control parameter gains.
- this embodiment further includes the following technical features.
- control parameter gain obtain the real-time control parameter gain, including:
- Step S202 using speed as an independent variable, perform control parameter gain polynomial fitting to obtain a first polynomial;
- Step S204 Acquire a real-time control parameter gain according to the first polynomial and the real-time vehicle longitudinal speed.
- the calculation method of polynomial fitting is adopted to obtain the real-time control parameter gain, and the calculation amount is optimized.
- this embodiment further includes the following technical features.
- obtaining the real-time control parameter gain includes: taking the speed as the independent variable, performing the third-order polynomial fitting of the control parameter gain, and obtaining the first polynomial as:
- K [k′ 1 ,k′ 2 ,k′ 3 ,k′ 4 ]
- V x is the longitudinal speed of the vehicle.
- This embodiment adopts third-order polynomial fitting to calculate the optimal control parameter gain in real time according to the actual speed of the vehicle feedback, which not only reduces the calculation amount of the optimal control parameter gain, but also ensures the convergence and the optimal control parameter gain. reliability.
- this embodiment further includes the following technical features.
- Obtaining status error feedback includes:
- e y is the lateral position deviation
- e ⁇ is the heading angle deviation
- X is the state error feedback amount
- V x is the longitudinal vehicle speed of the vehicle
- V y is the lateral vehicle speed of the vehicle
- (x, y) is the vehicle position at the current moment
- (x des , y des ) is the desired trajectory position
- ⁇ is the heading angle of the vehicle at the current moment
- ⁇ des is the heading angle of the desired trajectory
- ⁇ is the yaw rate of the vehicle
- ⁇ is the curvature of the desired target point.
- this embodiment further includes the following technical features.
- control parameter gain real-time control parameter gain, state error, trajectory curvature and vehicle inclination angle, obtain the control amount and compensation amount, including:
- Step S302 according to the real-time control parameter gain and the state error feedback amount, the feedback control amount is obtained as:
- K is the real-time control parameter gain
- X is the state error feedback amount
- Step S304 based on the position error integral, obtain the position error integral control value as:
- k i is the integral coefficient
- e y is the lateral position deviation
- T is the control period
- ⁇ 'sw_i is the value of the previous beat control period of ⁇ sw_i;
- Step S306 according to the trajectory curvature, obtain the road curvature feedforward compensation amount as:
- m is the mass of the vehicle
- V x is the longitudinal speed of the vehicle
- l f is the distance from the front axle of the vehicle to the center of mass
- l r is the distance from the rear axle of the vehicle to the center of mass
- C f is the cornering stiffness of the front wheels
- C r is The cornering stiffness of the rear wheel
- R des is the trajectory radius
- L is the sum of l f and l r;
- Step S308 according to the vehicle inclination angle, obtain the road inclination feedforward compensation amount as:
- A(i,j) is the parameter corresponding to the i-th row and j-th column of matrix A
- B(i,j) is the parameter corresponding to the i-th row and jth column of matrix B
- g is the acceleration of gravity
- ⁇ the vehicle inclination angle
- This embodiment uses the road curvature provided by the desired trajectory to feed forward the road curvature Compensation, feed forward compensation for road inclination using positioning information, introduction of position deviation integral term to reduce position error, comprehensive consideration of the influence of road curvature, road inclination and uncertainty of overall vehicle parameters to achieve highly reliable and high-precision tracking of expected driving Trajectory.
- This embodiment introduces a position deviation integral term to reduce the position error, and ensures that the steady-state error can be completely eliminated when the overall vehicle parameters are uncertain.
- this embodiment further includes the following technical features.
- obtaining the expected steering wheel angle, and outputting the steering wheel expected angle to the steer-by-wire system includes:
- ⁇ sw ( ⁇ sw_b + ⁇ sw_r + ⁇ sw_c + ⁇ sw_i )i
- ⁇ sw_b is the feedback control quantity
- ⁇ sw_i is the position error integral control quantity
- ⁇ sw_c is the road curvature feedforward compensation quantity
- ⁇ sw_r is the road tilt feedforward compensation quantity
- i is the steering wheel ratio.
- the sum is calculated, and then multiplied by the steering wheel ratio to obtain the final desired steering wheel angle, which is output to the steer-by-wire system. Realize path tracking to ensure that the steady-state error can be completely eliminated when the overall parameters of the vehicle are uncertain.
- this embodiment provides an apparatus 100 for controlling lateral movement of an autonomous vehicle, including: a setting module 110, a first acquiring module 120, a second acquiring module 130, a third acquiring module 140, and a fourth acquiring module 150.
- the fifth acquisition module 160 and the sixth acquisition module 170 wherein the control parameters of the linear quadratic controller are set through the setting module 110, the first acquisition module 120 acquires the overall vehicle parameters, and the second acquisition module 130 is based on the control parameters and
- the overall vehicle parameters obtain the control parameter gain, the third obtaining module 140 obtains the real-time control parameter gain according to the control parameter gain, the fourth obtaining module 150 obtains the state error, the trajectory curvature and the vehicle inclination angle, and the fifth obtaining module 160 obtains the gain according to the control parameter.
- the sixth acquisition module 170 obtains the desired steering wheel angle according to the control amount and compensation amount, and outputs the desired steering wheel angle to the steer-by-wire system .
- This embodiment can realize the optimal control of the lateral motion of the autonomous vehicle without static error, greatly reduce the amount of calculation, and ensure the convergence of the optimal control gain, and consider the influence of road curvature, road inclination and uncertainty of overall vehicle parameters. In order to achieve high reliability and high precision tracking the desired driving trajectory.
- this embodiment provides an automatic driving vehicle 200, including: a vehicle body 210 and a control device 220, wherein the control device adopts the lateral motion control method of an automatic driving vehicle as in any embodiment of the present application to control the vehicle.
- the vehicle body 210 travels.
- This embodiment provides a lateral motion control method for an automatic driving vehicle (that is, an optimal control method with no lateral static error), which fully reduces the amount of calculation of the optimal control gain, ensures the convergence of the control gain, and takes the road curvature into consideration. , The influence of road inclination and the uncertainty of overall parameters to achieve highly reliable and high-precision tracking of the desired driving trajectory.
- this embodiment first calculates the optimal control gain offline, then fits the control gain through a polynomial and saves it to the calculation unit, and then updates the optimal control gain and state error in real time based on the vehicle feedback speed.
- the road curvature provided by the trajectory provides feedforward compensation for the road curvature.
- the vehicle tilt angle obtained by the positioning information is used for feedforward compensation for the road tilt.
- the position deviation integral term is introduced to reduce the position error and ensure that the overall vehicle parameters are still uncertain. It can completely eliminate the steady-state error, as shown in Figure 8, the implementation steps are as follows:
- k 14 [k 14(1) , k 14(2) , k 14(3) , k 14(4) ]
- Step S404 polynomial fitting: according to the calculated optimal control gain result, the optimal control gain third-order polynomial fitting is performed through speed.
- Step S406 update the gain: calculate the optimal control gain size K in real time according to the actual speed V x fed back by the vehicle wheel speedometer.
- K [k′ 1 ,k′ 2 ,k′ 3 ,k′ 4 ]
- Step S408 update the status error: calculate the lateral position deviation e y and the lateral position deviation change rate according to the vehicle position, speed and attitude information output by the positioning system in real time, as well as the planned trajectory information Heading angle deviation e ⁇ , the rate of change of heading angle deviation State error feedback amount X.
- Step S410 Calculate the feedback control amount: multiply the state error feedback amount according to the optimal control gain and the state error feedback amount to calculate the state error feedback amount.
- Step S412 Calculate the road curvature feedforward compensation amount: calculate the road curvature compensation amount according to the trajectory curvature provided by the planning system.
- Step S414 Calculate the road inclination feedforward compensation amount: according to the vehicle inclination angle provided by the positioning system, calculate the road inclination feedforward compensation amount.
- Step S416 Calculate the position error integral control value: introduce the position error integral, and use the incremental integral calculation to obtain the integral value caused by the position error.
- Step S420 Calculate the steering wheel angle: sum up the calculation results of the feedback control quantity, the road curvature feedforward control quantity, the road tilt feedforward compensation quantity, and the position integral control quantity, and then multiply it by the steering wheel ratio to obtain the final desired steering wheel angle. Output to the steer-by-wire system to realize path tracking.
- ⁇ sw ( ⁇ sw_b + ⁇ sw_r + ⁇ sw_c + ⁇ sw_i )i
- i is the transmission ratio of the steering mechanism
- m is the mass of the vehicle
- Iz is the moment of inertia of the car around the vertical direction
- ⁇ is the yaw rate of the car
- l f and l r respectively represent the front and rear axles of the car to the center of mass
- C f and C r are the cornering stiffness of the front and rear wheels respectively
- ⁇ is the inclination angle provided by positioning
- ⁇ , ⁇ des are the heading angle of the vehicle at the current moment and the heading angle of the desired trajectory
- (x, y) and (x des , y des ) are respectively the position of the vehicle at the current moment and the position of the desired trajectory
- k i is the integral coefficient
- T is the control period
- ⁇ 'sw_i is the value of the previous beat control period of ⁇ sw_i
- A( i, j) and B(i, j) are the parameters corresponding to the i-th row and
- This embodiment can realize the optimal control of the automatic driving vehicle without static error in the lateral direction, greatly reduce the calculation amount, and ensure the convergence of the optimal control gain, and consider the influence of road curvature, road inclination and uncertainty of overall parameters. In order to achieve high reliability and high precision tracking the desired driving trajectory.
- This embodiment provides a method without static error, which introduces a position deviation integral term to reduce the position error, and ensures that the steady-state error can be completely eliminated when the overall vehicle parameters are uncertain.
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Abstract
一种自动驾驶车辆横向运动控制方法、装置和自动驾驶车辆,自动驾驶车辆横向运动控制方法包括:设置线性二次型控制器的控制参数(S102),获取车辆总体参数(S104),根据控制参数和车辆总体参数,获取控制参数增益(S106),根据控制参数增益,获取实时控制参数增益(S108),获取状态误差反馈量、轨迹曲率和车辆倾斜角(S110),根据实时控制参数增益、状态误差反馈量、轨迹曲率和车辆倾斜角,获取控制量和补偿量(S112),根据控制量和补偿量,获取方向盘期望角度,将方向盘期望角度输出至线控转向***(S114)。方法能够实现自动驾驶车辆横向运动无静差最优控制,极大的降低计算量,并保证最优控制增益的收敛性,实现高可靠、高精度地跟踪期望行驶轨迹。
Description
本申请要求于2020年05月26日在中国国家知识产权局提交的申请号为“202010456541.0”、发明名称为“自动驾驶车辆横向运动控制方法、装置和自动驾驶车辆”的中国专利申请的优先权,其全部内容通过引用结合在本申请中。
本申请涉及自动驾驶车辆的技术领域,具体而言,涉及一种自动驾驶车辆横向运动控制方法、装置和自动驾驶车辆。
自动驾驶车辆横向运动控制根据上层的规划轨迹和车辆定位信息,实时计算期望的转向指令,控制车辆沿目标轨迹行驶,目前,最优控制参数增益一般通过在线求解黎卡提方程得到,计算量大,需占用较多的计算资源。
发明内容
本申请旨在解决或改善上述技术问题的至少之一。
为此,本申请的第一目的在于提供一种自动驾驶车辆横向运动控制方法。
本申请的第二目的在于提供一种自动驾驶车辆横向运动控制装置。
本申请的第三目的在于提供一种自动驾驶车辆。
为实现本申请的第一目的,本申请的实施例提供了一种自动驾驶车辆横向运动控制方法,包括:设置线性二次型控制器的控制参数;获取车辆总体参数;根据控制参数和车辆总体参数,获取控制参数增益;根据控制参数增益,获取实时控制参数增益;获取状态误差反馈量、轨迹曲率和车辆倾斜角;根据实时控制参数增益、状态误差反馈量、轨迹曲率和车辆倾斜角,获取控制量和补偿量;根据控制量和补偿量,获取方向盘期望角度,将方 向盘期望角度输出至线控转向***。
本实施例能够实现自动驾驶车辆横向运动无静差最优控制,极大的降低计算量,并保证最优控制增益的收敛性,并考虑道路曲率、道路倾斜和车辆总体参数不确定的影响,以达到高可靠、高精度地跟踪期望行驶轨迹。
另外,本申请上述实施例提供的技术方案还可以具有如下附加技术特征:
上述技术方案中,设置线性二次型控制器的控制参数包括:控制参数包括第一加权矩阵Q和第二加权矩阵R,Q=diag[q
1,q
2,q
3,q
4],R=[r],其中,q
1、q
2、q
3、q
4和r分别为五个控制参数,q
2、q
4和r设定为固定值,q
1、q
3根据下式获取:
其中,q
1max为q
1最大值,q
1min为q
1最小值,q
3max为q
3最大值,q
3min为q
3最小值,V
x为车辆纵向车速,V
1为第一速度阈值,V
2为第二速度阈值。
本实施例中,将q2,q4,r设定为固定值,q1,q3根据车辆纵向车速V
x进行参数线性调整,即线性二次型控制器的控制参数的权重矩阵采用速度线性的调整方法,能够大大降低运算量。
上述任一技术方案中,根据控制参数和车辆总体参数,获取控制参数增益,包括:控制参数增益通过下式获取:
k
14=[k
14(1),k
14(2),k
14(3),k
14(4)]
k
14=lqr(A,B,Q,R)
k
1=k
14(1)
k
2=k
14(2)
k
3=k
14(3)
k
4=k
14(4)
其中,m为整车质量,I
z为车辆绕垂直方向的转动惯量,l
f为车辆前轴到质心的距离,l
r为车辆后轴到质心的距离,C
f为前轮的侧偏刚度,C
r为后轮的侧偏刚度,V
x为车辆纵向车速,k
14为控制增益收敛矩阵,k
14(1)、k
14(2)、k
14(3)、k
14(4)分别为控制增益收敛矩阵中四个元素,k
14为通过MATLAB计算得到的最优控制增益收敛矩阵,k
1,k
2,k
3,k
4分别为四个控制参数增益。
车辆总体参数包括:整车质量和/或车辆绕垂直方向的转动惯量和/或车辆前轴到质心的距离和/或车辆后轴到质心的距离和/或前轮的侧偏刚度和/或后轮的侧偏刚度。根据整车总体参数和被控对象模型,通过MATLAB函数lqr离线计算最优控制参数增益k
1、k
2、k
3、k
4,与在线求解控制参数增益相比,大大降低了运算量。
上述任一技术方案中,根据控制参数增益,获取实时控制参数增益,包括:以速度为自变量,进行控制参数增益多项式拟合,得到第一多项式;根据第一多项式和实时车辆纵向车速,获取实时控制参数增益。
基于离线获取的控制参数增益,采用多项式拟合的计算方法,获取实时控制参数增益,对计算量进行了优化。
上述任一技术方案中,根据控制参数增益,获取实时控制参数增益,包括:以速度为自变量,进行控制参数增益三阶多项式拟合,得到第一多项式为:
其中,a
31,a
21,a
11,a
01为控制参数k
1的三阶多项式系数,a
32,a
22,a
12,a
02为控制参数k
2的三阶多项式系数,a
33,a
23,a
13,a
03为控制参数k
3的三阶多项式系数,a
34,a
24,a
14,a
04为控制参数k
4的三阶多项式系数,v表示速度;根据第一多项式和实时车辆纵向车速,获取实时控制参数增益K:
K=[k′
1,k′
2,k′
3,k′
4]
其中,V
x为车辆纵向车速。
本实施例采用三阶多项式拟合,根据车辆反馈的实际速度实时计算最优控制参数增益大小,既减小了最优控制参数增益的计算量,又保证了最优控制参数增益的收敛性和可靠性。
上述任一技术方案中,获取状态误差反馈量包括:
通过下式获取状态误差反馈量:
e
y=(y-y
des)cos(ψ
des)-(x-x
des)sin(ψ
des)
e
ψ=ψ-ψ
des
其中,e
y为横向位置偏差,
为横向位置偏差变化率,e
ψ为航向角偏差,
为航向角偏差变化率,X为状态误差反馈量,V
x为车辆纵向车速,V
y为车辆横向车速,(x,y)为当前时刻车辆位置,(x
des,y
des)为期望轨迹位置,ψ为当前时刻车辆的航向角,ψ
des为期望轨迹的航向角,ω表示车辆的横摆角速度,ρ 表示期望目标点曲率。
通过更新状态误差,反馈至控制增益,使得横向控制效果更加精确。
上述任一技术方案中,根据控制参数增益、实时控制参数增益、状态误差、轨迹曲率和车辆倾斜角,获取控制量和补偿量,包括:
根据实时控制参数增益和状态误差反馈量,获取反馈控制量为:
δ
sw_b=-KX
其中,K为实时控制参数增益,X为状态误差反馈量;
基于位置误差积分,获取位置误差积分控制量为:
δ
sw_i=δ'
sw_i+k
ie
yT
其中,k
i为积分系数,e
y为横向位置偏差,T为控制周期,δ'
sw_i为δ
sw_i的前一拍控制周期数值;
根据轨迹曲率,获取道路曲率前馈补偿量为:
其中,m为整车质量,V
x为车辆纵向车速,l
f为车辆前轴到质心的距离,l
r为车辆后轴到质心的距离,C
f为前轮的侧偏刚度,C
r为后轮的侧偏刚度,R
des为轨迹半径,L为l
f与l
r之和;
根据车辆倾斜角,获取道路倾斜前馈补偿量为:
其中,A(i,j)为矩阵A第i行第j列对应的参数,B(i,j)为矩阵B第i行第j列对应的参数,g为重力加速度,γ车辆倾斜角。
通过上述公式给出了反馈控制量、位置误差积分控制量、道路曲率前馈补偿量、道路倾斜前馈补偿量的具体获取方式,本实施例采用期望轨迹提供的道路曲率对道路曲率进行前馈补偿,采用定位信息对道路倾斜进行前馈补偿,引入位置偏差积分项减小位置误差,综合考虑道路曲率、道路倾斜和车辆总体参数不确定的影响,以达到高可靠、高精度地跟踪期望行驶轨迹。
上述任一技术方案中,根据控制量和所述补偿量,获取方向盘期望角度,将方向盘期望角度输出至线控转向***,包括:
根据下式获取方向盘期望角度:
δ
sw=(δ
sw_b+δ
sw_r+δ
sw_c+δ
sw_i)i
其中,δ
sw_b为反馈控制量,δ
sw_i为位置误差积分控制量,δ
sw_c为道路曲率前馈补偿量,δ
sw_r为道路倾斜前馈补偿量,i为方向盘比率。
根据反馈控制量、道路曲率前馈控制量、道路倾斜前馈补偿量、位置积分控制量的计算结果进行求和,再乘以方向盘比率,得到最终的方向盘期望角度,输出给线控转向***,实现路径跟踪,保证车辆总体参数存在不确定的时候仍能完全消除稳态误差。
为实现本申请的第二目的,本申请的实施例提供了一种自动驾驶车辆横向运动控制装置,包括:设置模块、第一获取模块、第二获取模块、第三获取模块、第四获取模块、第五获取模块和第六获取模块,其中,通过设置模块设置线性二次型控制器的控制参数,第一获取模块获取车辆总体参数,第二获取模块根据控制参数和车辆总体参数,获取控制参数增益,第三获取模块根据控制参数增益,获取实时控制参数增益,第四获取模块获取状态误差、轨迹曲率和车辆倾斜角,第五获取模块根据控制参数增益、实时控制参数增益、状态误差、轨迹曲率和车辆倾斜角,获取控制量和补偿量,第六获取模块根据控制量和补偿量,获取方向盘期望角度,将方向盘期望角度输出至线控转向***。
本实施例能够实现自动驾驶车辆横向运动无静差最优控制,极大的降低计算量,并保证最优控制增益的收敛性,并考虑道路曲率、道路倾斜和车辆总体参数不确定的影响,以达到高可靠、高精度地跟踪期望行驶轨迹。
为实现本申请的第三目的,本申请的实施例提供了一种自动驾驶车辆,包括:车辆本体和控制装置,其中,控制装置采用如本申请任一实施例的自动驾驶车辆横向运动控制方法控制所述车辆本体行驶。
本申请实施例提供的自动驾驶车辆实现如本申请任一实施例的自动驾驶车辆横向运动控制方法的步骤,因而其具有如本申请任一实施例的自动驾驶车辆横向运动控制方法的全部有益效果,在此不再赘述。
本申请的附加方面和优点将在下面的描述部分中变得明显,或通过本申请的实践了解到。
本申请的上述和/或附加的方面和优点从结合下面附图对实施例的描述中将变得明显和容易理解,其中:
图1为相关技术的自动驾驶车辆横向运动控制示意图;
图2为本申请一个实施例的自动驾驶车辆横向运动控制方法流程图;
图3为本申请一个实施例的获取实时控制参数增益的方法流程图;
图4为本申请一个实施例的获取控制量和补偿量的方法流程图;
图5为本申请一个实施例的自动驾驶车辆横向运动控制装置结构框图;
图6为本申请一个实施例的自动驾驶车辆结构框图;
图7为本申请一个具体实施例的自动驾驶车辆横向运动控制示意图;
图8为本申请一个具体实施例的自动驾驶车辆横向运动控制方法流程图。
其中,图1至图8中附图标记与部件名称之间的对应关系为:
100:自动驾驶车辆横向运动控制装置,110:设置模块,120:第一获取模块,130:第二获取模块,140:第三获取模块,150:第四获取模块,160:第五获取模块,170:第六获取模块,200:自动驾驶车辆,210:车辆本体,220:控制装置。
为了能够更清楚地理解本申请的上述目的、特征和优点,下面结合附图和具体实施方式对本申请进行进一步的详细描述。需要说明的是,在不冲突的情况下,本申请的实施例及实施例中的特征可以相互组合。
在下面的描述中阐述了很多具体细节以便于充分理解本申请,但是,本申请还可以采用其他不同于在此描述的其他方式来实施,因此,本申请的保护范围并不受下面公开的具体实施例的限制。
下面参照图1至图8描述本申请一些实施例的一种自动驾驶车辆横向运动控制方法、装置和自动驾驶车辆。
自动驾驶车辆横向运动控制根据上层的规划轨迹和车辆定位信息,实时计算期望的转向指令,控制车辆沿期望轨迹行驶,当前自动驾驶车辆横 向运动控制广泛采用线性二次型最优控制(LQR),如图1所示,主要存在以下问题:
(1)最优控制增益一般通过在线求解黎卡提方程得到最优控制增益,需占用较多的计算资源,且不一定能保证控制增益收敛。
(2)当前自动驾驶车辆横向运动控制***大多采用引入道路曲率前馈补偿减小稳态误差,未考虑道路倾斜引起的横向稳态误差,降低了车辆横向控制***的品质。
(3)车辆总体参数存在一定的不确定性,不能保证完全消除车辆横向控制的稳态误差。
相关技术的一种自动驾驶汽车的参数自适应横向运动LQR控制方法,基于路径跟踪误差和车-路位置关系的LQR控制参数调整策略确定当前状态下的控制器参数,根据确定的控制器参数,计算自动驾驶汽车的转向控制量,将其传递给转向执行器执行,其与本实施例的不同在于:
(1)参数调整策略不同,此相关技术用路径跟踪误差和车-路位置关系的参数调整策略,本实施例用速度大小的参数调整策略。
(2)最优控制增益计算过程不同,此相关技术在线求解黎卡提方程得到最优控制增益,本实施例则是通过离线计算最优控制增益,再通过多项式拟合控制增益并实时更新。
(3)方向盘转角不同。此相关技术只考虑了状态误差反馈控制量,本实施例在状态误差反馈控制量的基础上,还考虑了道路曲率前馈控制量、道路倾斜前馈补偿量、位置误差积分控制量。
综上所述,本实施例的目的在于解决以下问题的至少之一:
(1)在线求解黎卡提方程得到最优控制增益,计算量大;
(2)未考虑道路倾斜引起的横向稳态误差。
实施例1:
如图2所示,本实施例提供了一种自动驾驶车辆横向运动控制方法,包括:
步骤S102,设置线性二次型控制器的控制参数;
步骤S104,获取车辆总体参数;
步骤S106,根据控制参数和车辆总体参数,获取控制参数增益;
步骤S108,根据控制参数增益,获取实时控制参数增益;
步骤S110,获取状态误差反馈量、轨迹曲率和车辆倾斜角;
步骤S112,根据实时控制参数增益、状态误差反馈量、轨迹曲率和车辆倾斜角,获取控制量和补偿量;
步骤S114,根据控制量和补偿量,获取方向盘期望角度,将方向盘期望角度输出至线控转向***。
本实施例能够实现自动驾驶车辆横向运动无静差最优控制,极大的降低计算量,并保证最优控制增益的收敛性,并考虑道路曲率、道路倾斜和车辆总体参数不确定的影响,以达到高可靠、高精度地跟踪期望行驶轨迹。
实施例2:
除上述实施例的技术特征以外,本实施例进一步地包括了以下技术特征。
上述技术方案中,设置线性二次型控制器的控制参数包括:控制参数包括第一加权矩阵Q和第二加权矩阵R,Q=diag[q
1,q
2,q
3,q
4],R=[r],其中,q
1、q
2、q
3、q
4和r分别为五个控制参数,q
2、q
4和r设定为固定值,q
1、q
3根据下式获取:
其中,q
1max为q
1最大值,q
1min为q
1最小值,q
3max为q
3最大值,q
3min为q
3最小值,V
x为车辆纵向车速,V
1为第一速度阈值,V
2为第二速度阈值。
本实施例中,将q2,q4,r设定为固定值,只针对q1,q3根据车辆速度V
x进行参数线性调整,即线性二次型控制器的控制参数的权重矩阵采用速度线性的调整方法,降低运算量。
实施例3:
除上述实施例的技术特征以外,本实施例进一步地包括了以下技术特征。
根据控制参数和车辆总体参数,获取控制参数增益,包括:控制参数增益通过下式获取:
k
14=[k
14(1),k
14(2),k
14(3),k
14(4)]
k
14=lqr(A,B,Q,R)
k
1=k
14(1)
k
2=k
14(2)
k
3=k
14(3)
k
4=k
14(4)
其中,m为整车质量,I
z为车辆绕垂直方向的转动惯量,l
f为车辆前轴到质心的距离,l
r为车辆后轴到质心的距离,C
f为前轮的侧偏刚度,C
r为后轮的侧偏刚度,V
x为车辆纵向车速,k
14为控制增益收敛矩阵,k
14(1)、k
14(2)、k
14(3)、k
14(4)分别为控制增益收敛矩阵中四个元素,k
14为通过MATLAB计算得到的最优控制增益收敛矩阵,k
1,k
2,k
3,k
4分别为四个控制参数增益。
其中,车辆总体参数包括:整车质量和/或车辆绕垂直方向的转动惯量和/或车辆前轴到质心的距离和/或车辆后轴到质心的距离和/或前轮的侧偏刚度和/或后轮的侧偏刚度。获取车辆总体参数,根据车辆总体参数离线获取控制参数增益,降低本实施例方法的运算量。
根据整车总体参数和被控对象模型,通过MATLAB函数lqr离线计算最优控制参数增益k
1、k
2、k
3、k
4,与在线求解控制参数增益相比,大大降低了运算量。
实施例4:
如图3所示,除上述实施例的技术特征以外,本实施例进一步地包括了以下技术特征。
根据控制参数增益,获取实时控制参数增益,包括:
步骤S202,以速度为自变量,进行控制参数增益多项式拟合,得到第一多项式;
步骤S204,根据第一多项式和实时车辆纵向车速,获取实时控制参数增益。
基于离线获取的控制参数增益,采用多项式拟合的计算方法,获取实时控制参数增益,对计算量进行了优化。
实施例5:
除上述实施例的技术特征以外,本实施例进一步地包括了以下技术特征。
根据控制参数增益,获取实时控制参数增益,包括:以速度为自变量,进行控制参数增益三阶多项式拟合,得到第一多项式为:
其中,a
31,a
21,a
11,a
01为控制参数k
1的三阶多项式系数,a
32,a
22,a
12,a
02为控制参数k
2的三阶多项式系数,a
33,a
23,a
13,a
03为控制参数k
3的三阶多项式系数,a
34,a
24,a
14,a
04为控制参数k
4的三阶多项式系数,v表示速度;根据第一多项式和实时车辆纵向车速,获取实时控制参数增益K:
K=[k′
1,k′
2,k′
3,k′
4]
其中,V
x为车辆纵向车速。
本实施例采用三阶多项式拟合,根据车辆反馈的实际速度实时计算最优控制参数增益大小,既减小了最优控制参数增益的计算量,又保证了最优控制参数增益的收敛性和可靠性。
实施例6:
除上述实施例的技术特征以外,本实施例进一步地包括了以下技术特征。
获取状态误差反馈量包括:
通过下式获取状态误差反馈量:
e
y=(y-y
des)cos(ψ
des)-(x-x
des)sin(ψ
des)
e
ψ=ψ-ψ
des
其中,e
y为横向位置偏差,
为横向位置偏差变化率,e
ψ为航向角偏差,
为航向角偏差变化率,X为状态误差反馈量,V
x为车辆纵向车速,V
y为车辆横向车速,(x,y)为当前时刻车辆位置,(x
des,y
des)为期望轨迹位置,ψ为当前时刻车辆的航向角,ψ
des为期望轨迹的航向角,ω表示车辆的横摆角速度,ρ表示期望目标点曲率。
通过更新状态误差,反馈至控制增益,使得横向控制效果更加精确。
实施例7:
如图4所示,除上述实施例的技术特征以外,本实施例进一步地包括了以下技术特征。
根据控制参数增益、实时控制参数增益、状态误差、轨迹曲率和车辆倾斜角,获取控制量和补偿量,包括:
步骤S302,根据实时控制参数增益和状态误差反馈量,获取反馈控制量为:
δ
sw_b=-KX
其中,K为实时控制参数增益,X为状态误差反馈量;
步骤S304,基于位置误差积分,获取位置误差积分控制量为:
δ
sw_i=δ'
sw_i+k
ie
yT
其中,k
i为积分系数,e
y为横向位置偏差,T为控制周期,δ'
sw_i为δ
sw_i的前一拍控制周期数值;
步骤S306,根据轨迹曲率,获取道路曲率前馈补偿量为:
其中,m为整车质量,V
x为车辆纵向车速,l
f为车辆前轴到质心的距离,l
r为车辆后轴到质心的距离,C
f为前轮的侧偏刚度,C
r为后轮的侧偏刚度,R
des为轨迹半径,L为l
f与l
r之和;
步骤S308,根据车辆倾斜角,获取道路倾斜前馈补偿量为:
其中,A(i,j)为矩阵A第i行第j列对应的参数,B(i,j)为矩阵B第i行第j列对应的参数,g为重力加速度,γ车辆倾斜角。
通过上述公式给出了反馈控制量、位置误差积分控制量、道路曲率前馈补偿量、道路倾斜前馈补偿量的具体获取方式,本实施例采用期望轨迹提供的道路曲率对道路曲率进行前馈补偿,采用定位信息对道路倾斜进行前馈补偿,引入位置偏差积分项减小位置误差,综合考虑道路曲率、道路倾斜和车辆总体参数不确定的影响,以达到高可靠、高精度地跟踪期望行驶轨迹。
本实施例引入位置偏差积分项以减小位置误差,保证车辆总体参数存在不确定的时候仍能完全消除稳态误差。
实施例8:
除上述实施例的技术特征以外,本实施例进一步地包括了以下技术特征。
根据控制量和所述补偿量,获取方向盘期望角度,将方向盘期望角度输出至线控转向***,包括:
根据下式获取方向盘期望角度:
δ
sw=(δ
sw_b+δ
sw_r+δ
sw_c+δ
sw_i)i
其中,δ
sw_b为反馈控制量,δ
sw_i为位置误差积分控制量,δ
sw_c为道路曲率前馈补偿量,δ
sw_r为道路倾斜前馈补偿量,i为方向盘比率。
根据反馈控制量、道路曲率前馈控制量、道路倾斜前馈补偿量、位置积分控制量的计算结果进行求和,再乘以方向盘比率,得到最终的方向盘期望角度,输出给线控转向***,实现路径跟踪,保证车辆总体参数存在不确定的时候仍能完全消除稳态误差。
实施例9:
如图5所示,本实施例提供了一种自动驾驶车辆横向运动控制装置100,包括:设置模块110、第一获取模块120、第二获取模块130、第三获取模块140、第四获取模块150、第五获取模块160和第六获取模块170,其中,通过设置模块110设置线性二次型控制器的控制参数,第一获取模块120获取车辆总体参数,第二获取模块130根据控制参数和车辆总体参数,获取控制参数增益,第三获取模块140根据控制参数增益,获取实时控制参数增益,第四获取模块150获取状态误差、轨迹曲率和车辆倾斜角,第五获取模块160根据控制参数增益、实时控制参数增益、状态误差、轨迹曲率和车辆倾斜角,获取控制量和补偿量,第六获取模块170根据控制量和补偿量,获取方向盘期望角度,将方向盘期望角度输出至线控转向***。
本实施例能够实现自动驾驶车辆横向运动无静差最优控制,极大的降低计算量,并保证最优控制增益的收敛性,并考虑道路曲率、道路倾斜和车辆总体参数不确定的影响,以达到高可靠、高精度地跟踪期望行驶轨迹。
实施例10:
如图6所示,本实施例提供了一种自动驾驶车辆200,包括:车辆本体210和控制装置220,其中,控制装置采用如本申请任一实施例的自动驾驶车辆横向运动控制方法控制所述车辆本体210行驶。
具体实施例:
本实施例提供了一种自动驾驶车辆横向运动控制方法(即一种横向无静差的最优控制方法),充分减少最优控制增益的计算量,保证控制增益的收敛性,并考虑道路曲率、道路倾斜和总体参数不确定的影响,以达到高可靠、高精度地跟踪期望行驶轨迹。
如图7所示,本实施例首先通过离线计算最优控制增益,然后通过多项式拟合控制增益并保存到计算单元,然后通过车辆反馈的速度实时更新最优控制增益和状态误差量,通过期望轨迹提供的道路曲率对道路曲率进行前馈补偿,通过定位信息得到的车辆倾斜角对道路倾斜进行前馈补偿,引入位置偏差积分项以减小位置误差,保证车辆总体参数存在不确定的时候仍能完全消除稳态误差,如图8所示,实施步骤如下:
步骤S402,离线计算最优控制增益:计算权重矩阵,横向运动LQR控制器涉及的控制参数包括加第一权矩阵Q和第二加权矩阵R,具体有Q=diag[q1,q2,q3,q4],R=[r],即q1,q2,q3,q4和r共5个参数;根据控制参数关联的物理量确定其重要程度,将q2,q4,r设定为固定值,只针对q1,q3根据车辆速度V
x进行参数调整;
离线计算:根据整车总体参数和被控对象模型,通过MATLAB函数lqr离线计算最优控制增益k
1、k
2、k
3、k
4。
k
14=[k
14(1),k
14(2),k
14(3),k
14(4)]
k
14=lqr(A,B,Q,R)
k
1=k
14(1)
k
2=k
14(2)
k
3=k
14(3)
k
4=k
14(4)
步骤S404,多项式拟合:根据计算的最优控制增益结果,通过速度进行最优控制增益三阶多项式拟合。
步骤S406,更新增益:根据车辆轮速计反馈的实际速度V
x实时计算最优控制增益大小K。
K=[k′
1,k′
2,k′
3,k′
4]
步骤S408,更新状态误差:根据定位***实时输出的车辆位置、速度和姿态信息,以及规划期望的轨迹信息,计算横向位置偏差e
y,横向位置偏差变化率
航向角偏差e
ψ,航向角偏差变化率
状态误差反馈量X。
e
y=(y-y
des)cos(ψ
des)-(x-x
des)sin(ψ
des)
e
ψ=ψ-ψ
des
步骤S410,计算反馈控制量:根据最优控制增益和状态误差反馈量进行相乘,计算得到状态误差反馈量。
δ
sw_b=-KX
步骤S412,计算道路曲率前馈补偿量:根据规划***提供轨迹曲率,计算得到道路曲率补偿量。
步骤S414,计算道路倾斜前馈补偿量:根据定位***提供车辆倾斜角,计算得到道路倾斜前馈补偿量。
步骤S416,计算位置误差积分控制量:引入位置误差的积分,采用增量式积分计算得到位置误差引起的积分量。
δ
sw_i=δ'
sw_i+k
ie
yT
步骤S420,计算方向盘角度:根据反馈控制量、道路曲率前馈控制量、道路倾斜前馈补偿量、位置积分控制量的计算结果进行求和,再乘以方向盘比率,得到最终的方向盘期望角度,输出给线控转向***,实现路径跟踪。
δ
sw=(δ
sw_b+δ
sw_r+δ
sw_c+δ
sw_i)i
以上式子中,i为转向机构传动比,m代表整车质量;Iz代表汽车绕垂直方向的转动惯量;ω表示汽车的横摆角速度;l
f和l
r分别代表汽车前、后轴到质心的距离;C
f和C
r分别为前轮和后轮的侧偏刚度,γ为定位提供的倾斜角,ψ、ψ
des分别为当前时刻车辆的航向角和期望轨迹的航向角,(x,y)、(x
des,y
des)分别为当前时刻车辆的位置和期望轨迹的位置,k
i为积分系数,T为控制周期,δ'
sw_i为δ
sw_i的前一拍控制周期数值,A(i,j)、B(i,j)分别为矩阵A和B的第i行第j列对应的参数,g是重力加速度。
综上,本申请实施例的有益效果为:
1.本实施例能够实现自动驾驶车辆横向无静差最优控制,极大的降低计算量,并保证最优控制增益的收敛性,并考虑道路曲率、道路倾斜和总体参数 不确定的影响,以达到高可靠、高精度地跟踪期望行驶轨迹。
2.本实施例提供一种无静差方法,引入位置偏差积分项以减小位置误差,保证车辆总体参数存在不确定的时候仍能完全消除稳态误差。
3.根据车辆反馈的实际速度实时计算最优控制增益大小,既减小了最优控制增益的计算量,又保证了最优控制增益的收敛性和可靠性。
在本申请中,术语“第一”、“第二”、“第三”仅用于描述的目的,而不能理解为指示或暗示相对重要性;术语“多个”则指两个或两个以上,除非另有明确的限定。术语“安装”、“相连”、“连接”、“固定”等术语均应做广义理解,例如,“连接”可以是固定连接,也可以是可拆卸连接,或一体地连接;“相连”可以是直接相连,也可以通过中间媒介间接相连。对于本领域的普通技术人员而言,可以根据具体情况理解上述术语在本申请中的具体含义。
本申请的描述中,需要理解的是,术语“上”、“下”、“左”、“右”、“前”、“后”等指示的方位或位置关系为基于附图所示的方位或位置关系,仅是为了便于描述本申请和简化描述,而不是指示或暗示所指的装置或单元必须具有特定的方向、以特定的方位构造和操作,因此,不能理解为对本申请的限制。
在本说明书的描述中,术语“一个实施例”、“一些实施例”、“具体实施例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或特点包含于本申请的至少一个实施例或示例中。在本说明书中,对上述术语的示意性表述不一定指的是相同的实施例或实例。而且,描述的具体特征、结构、材料或特点可以在任何的一个或多个实施例或示例中以合适的方式结合。
以上仅为本申请的优选实施例而已,并不用于限制本申请,对于本领域的技术人员来说,本申请可以有各种更改和变化。凡在本申请的精神和原则之内,所作的任何修改、等同替换、改进等,均应包含在本申请的保护范围之内。
Claims (10)
- 一种自动驾驶车辆横向运动控制方法,其中,包括:设置线性二次型控制器的控制参数;获取车辆总体参数;根据所述控制参数和所述车辆总体参数,获取控制参数增益;根据所述控制参数增益,获取实时控制参数增益;获取状态误差反馈量、轨迹曲率和车辆倾斜角;根据所述实时控制参数增益、所述状态误差反馈量、所述轨迹曲率和所述车辆倾斜角,获取控制量和补偿量;根据所述控制量和所述补偿量,获取方向盘期望角度,将所述方向盘期望角度输出至线控转向***。
- 根据权利要求1所述的自动驾驶车辆横向运动控制方法,其中,根据所述控制参数和所述车辆总体参数,获取控制参数增益,包括:所述控制参数增益通过下式获取:k 14=[k 14(1),k 14(2),k 14(3),k 14(4)]k 14=lqr(A,B,Q,R)k 1=k 14(1)k 2=k 14(2)k 3=k 14(3)k 4=k 14(4)其中,m为整车质量,I z为车辆绕垂直方向的转动惯量,l f为车辆前轴到质心的距离,l r为车辆后轴到质心的距离,C f为前轮的侧偏刚度,C r为后轮的侧偏刚度,V x为车辆纵向车速,k 14为控制增益收敛矩阵,k 14(1)、k 14(2)、k 14(3)、k 14(4)分别为控制增益收敛矩阵中四个元素,k 1,k 2,k 3,k 4分别为四个控制参数增益。
- 根据权利要求1所述的自动驾驶车辆横向运动控制方法,其中,所述根据控制参数增益,获取实时控制参数增益,包括:以速度为自变量,进行控制参数增益多项式拟合,得到第一多项式;根据所述第一多项式和实时车辆纵向车速,获取实时控制参数增益。
- 根据权利要求4所述的自动驾驶车辆横向运动控制方法,其中,根据控制参数增益,获取实时控制参数增益,包括:以速度为自变量,进行控制参数增益三阶多项式拟合,得到所述第一多项式为:其中,a 31,a 21,a 11,a 01为控制参数k 1的三阶多项式系数,a 32,a 22,a 12,a 02为控制参数k 2的三阶多项式系数,a 33,a 23,a 13,a 03为控制参数k 3的三阶多项式系数,a 34,a 24,a 14,a 04为控制参数k 4的三阶多项式系数,v表示速度;根据所述第一多项式和所述实时车辆纵向车速,获取实时控制参数增益K:K=[k′ 1,k′ 2,k′ 3,k′ 4]其中,V x为车辆纵向车速。
- 根据权利要求1所述的自动驾驶车辆横向运动控制方法,其中,所述根据所述控制参数增益、所述实时控制参数增益、所述状态误差、所述轨迹曲率和所述车辆倾斜角,获取控制量和补偿量,包括:根据所述实时控制参数增益和状态误差反馈量,获取反馈控制量为:δ sw_b=-KX其中,K为实时控制参数增益,X为状态误差反馈量;基于位置误差积分,获取位置误差积分控制量为:δ sw_i=δ' sw_i+k ie yT其中,k i为积分系数,e y为横向位置偏差,T为控制周期,δ' sw_i为δ sw_i的前一拍控制周期数值;根据轨迹曲率,获取道路曲率前馈补偿量为:其中,m为整车质量,V x为车辆纵向车速,l f为车辆前轴到质心的距离,l r为车辆后轴到质心的距离,C f为前轮的侧偏刚度,C r为后轮的侧偏刚度,R des为轨迹半径,L为l f与l r之和;根据车辆倾斜角,获取道路倾斜前馈补偿量为:其中,A(i,j)为矩阵A第i行第j列对应的参数,B(i,j)为矩阵B第i行第j列对应的参数,g为重力加速度,γ车辆倾斜角。
- 根据权利要求1所述的自动驾驶车辆横向运动控制方法,其中,所述根据所述控制量和所述补偿量,获取方向盘期望角度,将所述方向盘期望角度输出至线控转向***,包括:根据下式获取所述方向盘期望角度:δ sw=(δ sw_b+δ sw_r+δ sw_c+δ sw_i)i其中,δ sw_b为反馈控制量,δ sw_i为位置误差积分控制量,δ sw_c为道路曲率前馈补偿量,δ sw_r为道路倾斜前馈补偿量,i为方向盘比率。
- 一种自动驾驶车辆横向运动控制装置(100),其中,包括:设置模块(110);第一获取模块(120);第二获取模块(130);第三获取模块(140);第四获取模块(150);第五获取模块(160);第六获取模块(170);其中,通过所述设置模块(110)设置线性二次型控制器的控制参数,所述第一获取模块(120)获取车辆总体参数,所述第二获取模块(130)根据所述控制参数和所述车辆总体参数,获取控制参数增益,所述第三获取模块(140)根据所述控制参数增益,获取实时控制参数增益,所述第四获取模块(150)获取状态误差、轨迹曲率和车辆倾斜角,所述第五获取模块(160)根据所述控制参数增益、所述实时控制参数增益、所述状态误差、所述轨迹曲率和所述车辆倾斜角,获取控制量和补偿量,所述第六获取模块(170)根据所述控制量和所述补偿量,获取方向盘期望角度,将所述方向盘期望角度输出至线控转向***。
- 一种自动驾驶车辆(200),其中,包括:车辆本体(210);控制装置(220);其中,所述控制装置(220)采用如权利要求1至8中任一项所述的自动驾驶车辆横向运动控制方法控制所述车辆本体(210)行驶。
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