CN107963126B - Large-curvature automatic driving steering control method for multi-axle steering vehicle - Google Patents
Large-curvature automatic driving steering control method for multi-axle steering vehicle Download PDFInfo
- Publication number
- CN107963126B CN107963126B CN201610913242.9A CN201610913242A CN107963126B CN 107963126 B CN107963126 B CN 107963126B CN 201610913242 A CN201610913242 A CN 201610913242A CN 107963126 B CN107963126 B CN 107963126B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- deviation
- formula
- angle
- front wheel
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- 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
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D1/00—Steering controls, i.e. means for initiating a change of direction of the vehicle
- B62D1/24—Steering controls, i.e. means for initiating a change of direction of the vehicle not vehicle-mounted
- B62D1/28—Steering controls, i.e. means for initiating a change of direction of the vehicle not vehicle-mounted non-mechanical, e.g. following a line or other known markers
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
Abstract
The invention discloses a large-curvature automatic driving steering control method for a multi-axle steering vehicle, which comprises the following steps: s1, acquiring the transverse deviation Y of a vehicle at the current positione(t) and first heading angle deviation
Calculating the transverse deviation Y (t) and the second course angle deviation e (l, t) of the vehicle at the preset pre-aiming point position; s2, calculating an estimation quantity delta of a front wheel corner of the vehiclef(ii) a S3, estimating quantity delta of the front wheel cornerfCalculating a vehicle course input deviation e' for the feedback of the second course angle deviation e (l, t); and S4, calculating an expected front wheel steering angle u of the vehicle according to the input deviation e', and controlling the vehicle to steer. The invention has the advantages that the front wheel turning angle is used as the feedback course of the decision controller, so that the 'wheel return' strategy adopted by a driver when manual driving passes through a sharp turning path is simulated, the control performance of a controlled vehicle is better than that of a conventional control method when the controlled vehicle turns with a large curvature radius, and the advantage is more obvious along with the increase of deviation.
Description
Technical Field
The invention relates to the field of multi-axle steering vehicle control, in particular to a large-curvature automatic driving steering control method for a multi-axle steering vehicle.
Background
The multi-axle steering rubber-tyred train features that it is trackless, shares right of way with traditional car, no longer runs along fixed track, and tracks the ground mark line (virtual track). The special electric power system has the advantages of flexible bus running and low construction and maintenance cost, has the advantage of high transportation capacity, and overcomes the defects that infrastructure construction and vehicle acquisition cost are high for subways, light rails, trams and the like, and special electric power system and rail matching design are needed.
An intelligent driving auxiliary system based on monocular vision of rubber-tyred train through drawing ground identification line reference path information, realizes the virtual trajectory line of vehicle automatic tracking central authorities, as shown in figure 1, can be better alleviate driver's fatigue, guarantee vehicle driving safety. However, the minimum designed turning radius of the rubber wheel train is only 15m, so that the tracking task of completing a complex path (such as a road with a large curvature and a sharp turn) is a key index for measuring the tracking performance of the rubber wheel train path, and the rubber wheel train path tracking method is often unsatisfactory in tracking the complex road with a sharp turn and a large turn in a control algorithm with good tracking performance on a straight road.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the technical problems in the prior art, the invention provides the large-curvature automatic driving steering control method for the multi-axle steering vehicle, which utilizes the absolute direction of the corner of the front wheel as the feedback course of the decision controller, thereby simulating the 'wheel return' strategy adopted by a driver when the vehicle is driven by a person to pass through a sharp turning path, so that the control performance of the controlled vehicle is better than that of the conventional control method when the vehicle turns with a large curvature radius, and the more obvious superiority is along with the increase of deviation.
In order to solve the technical problems, the technical scheme provided by the invention is as follows: a large-curvature automatic driving steering control method for a multi-axle steering vehicle comprises the following steps:
s1, acquiring the transverse deviation Y of a vehicle at the current positione(t) and first heading angle deviation
Calculating the transverse deviation Y (t) and the second course angle deviation e (l, t) of the vehicle at the preset pre-aiming point position;
s2, calculating the estimation quantity of the front wheel corner of the vehicle
S3, estimating quantity of the front wheel corner
Calculating a vehicle course input deviation e' for the feedback of the second course angle deviation e (l, t);
and S4, calculating an expected front wheel steering angle u of the vehicle according to the input deviation e', and controlling the vehicle to steer.
As a further improvement of the present invention, the step S1 is a step of detecting a lateral deviation Y of the vehicle at the current positione(t) and first heading angle deviation
Are directly obtained by a lane line identification module of the vehicle.
As a further improvement of the invention, in the step S1, the lateral deviation Y (t) of the vehicle at the preset pre-aiming point position is calculated and determined according to the formula shown in the formula (1),
formula (A), (B) and1) wherein Y (l, t) is the lateral deviation of the vehicle at the preset pre-aiming point position, and Y ise(t) is the lateral deviation of the vehicle at the current position, l is the pre-line distance,
and p is the first course angle deviation, the road curvature directly determined by the lane line identification module, U is the longitudinal speed of the vehicle determined by the vehicle speed sensor, and t is time.
As a further improvement of the invention, the second heading angle deviation e (l, t) is calculated and determined according to the formula shown in the formula (2),
in the formula (2), e (l, t) is the second course angle deviation, Y (l, t) is the transverse deviation of the vehicle at the preset pre-aiming point position, and Ye(t) is the lateral deviation of the vehicle at the current position, l is the pre-line distance,
and p is the first course angle deviation, the road curvature directly determined by the lane line identification module, U is the longitudinal speed of the vehicle determined by the vehicle speed sensor, and t is time.
As a further improvement of the present invention, the estimation amount of the front wheel rotation angle of the vehicle in said step S2
Is determined by calculation through a formula shown in formula (3),
in the formula (3), the reaction mixture is,
is the estimation quantity of the front wheel rotation angle of the vehicle, delta theta is the variation quantity of the actual course angle of the vehicle in a sampling period, a is the distance between the predetermined mass center of the vehicle and the front axle of the vehicle, and b isThe method comprises the steps of determining the distance from the mass center of a vehicle to the front axle of the vehicle in advance, U being the longitudinal speed of the vehicle, T being a predetermined sampling period, m being the predetermined total mass of a first carriage, and ayFor lateral acceleration, CαfFor a predetermined sidewall deflection stiffness, C, of the vehicle front axle wheelαrFor a predetermined vehicle front axle tire sidewall stiffness.
As a further improvement of the present invention,
the formula (3) is derived from formulas shown in formula (4) and formula (5),
in the formula (4), R is the motion radius of the vehicle, a is the distance between the mass center of the vehicle and the front axle of the vehicle, b is the distance between the mass center of the vehicle and the front axle of the vehicle,
an estimate of the angle of rotation of the front wheels, αfFront axle tire sidewall deviation angle, αrIs the rear axle tire sidewall deflection angle, ayIs lateral acceleration, m is the total vehicle mass of the first section of carriage, CαfFor a predetermined sidewall deflection stiffness, C, of the vehicle front axle wheelαrA predetermined vehicle front axle tire sidewall deflection stiffness;
Δθ＝UT/R (5)
in the formula (5), Δ θ is the variation of the actual heading angle of the vehicle in the sampling period, U is the longitudinal speed of the vehicle, T is the sampling period, and R is the movement radius of the vehicle.
As a further improvement of the invention, the vehicle heading input deviation e' in the step S3 is obtained by calculation through a formula shown in formula (6),
in the formula (6), e' is the input deviation of the vehicle heading, e is the second heading angle deviation,
is an estimate of the front wheel angle of the vehicle.
As a further improvement of the present invention, the specific steps of step S4 include: the front wheel steering angle control increment Deltau of the vehicle is obtained through calculation by the formula shown in the formula (7),
in equation (7), Δ u is the front wheel steering angle control increment, u (K) is the desired front wheel steering angle control amount at the K-th sampling time (K is 0,1,2, …), and K ispIs a predetermined proportionality coefficient, KiIs a predetermined integral coefficient, KdE' (k) is a heading input deviation of a k (k is 0,1,2, …) th sampling moment, and is a preset differential coefficient;
and calculating a desired front wheel steering angle u of the vehicle through the front wheel steering angle control increment delta u of the vehicle, and controlling the vehicle to steer at the desired front wheel steering angle u of the vehicle.
Compared with the prior art, the invention has the advantages that: the invention utilizes the absolute direction of the front wheel corner as the feedback course of the decision controller instead of the vehicle course, thereby simulating the 'wheel return' strategy adopted by a driver when manually driving through a sharp turning path, so that the control performance of the controlled vehicle is better than that of the conventional control method when the controlled vehicle turns with a large curvature radius, and the superiority is more obvious along with the increase of deviation.
Drawings
FIG. 1 is a schematic view of an autonomous vehicle having a visually identifiable central virtual trajectory line in accordance with the present invention.
FIG. 2 is a schematic flow chart of an embodiment of the present invention.
Fig. 3 is a schematic diagram of the front wheel steering angle feedback control principle according to the embodiment of the present invention.
FIG. 4 is a schematic view of a vehicle-road geometry relationship and a camera view image plane according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of a two-degree-of-freedom front wheel steering angle estimation model of a first section of vehicle according to an embodiment of the invention.
FIG. 6 is a schematic diagram of the control result trajectory following according to the embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and specific preferred embodiments of the description, without thereby limiting the scope of protection of the invention.
As shown in fig. 1, the rubber-tyred train applied in this embodiment has an intelligent driving assistance system based on monocular vision, and automatically acquires corresponding steering parameters by extracting the ground identification line reference path information, so as to realize automatic tracking of the central virtual trajectory line of the vehicle, and perform automatic driving.
As shown in fig. 2, the method for controlling the large-curvature automatic driving steering of the multi-axle steering vehicle of the embodiment includes the following steps: s1, acquiring the transverse deviation Y of a vehicle at the current positione(t) and first heading angle deviation
Calculating the transverse deviation Y (t) and the second course angle deviation e (l, t) of the vehicle at the preset pre-aiming point position; s2, calculating the estimation quantity of the front wheel corner of the vehicle
S3, estimating quantity of front wheel corner
Calculating a vehicle course input deviation e' for feedback of the second course angle deviation e (l, t); and S4, calculating the expected front wheel steering angle u of the vehicle according to the input deviation e', and controlling the vehicle to steer. In the present embodiment, as shown in fig. 2, the lateral deviation Y of the current positione(t) is the transverse distance between the center of the front axle of the vehicle at the current position and the center of the central lane line, and the transverse deviation Y (t) at the preset pre-aiming point position is the transverse distance between the center of the front axle of the vehicle at the pre-aiming point and the central lane line, and the first course angle deviation
As a clamp between the heading and the longitudinal direction (X-axis) of the vehicleAnd the second course angle deviation e (l, t) is an included angle between a connecting line between the current position of the vehicle and the pre-aiming point and the longitudinal direction (X axis).
As shown in fig. 3, in the prior art control technique, using only the actual heading of the vehicle as feedback, the heading input bias of the decision controller can be understood as e- θd- θ, e is the second heading angle deviation, θdAnd theta is the actual heading angle of the vehicle. In the solution of the present embodiment, the estimation of the front wheel rotation angle of the vehicle is also used at the same time
As a feedback quantity of the decision controller, the input deviation e' of the decision controller is expressed as
Namely, the form shown in the formula (6).
In the present embodiment, the lateral deviation Y of the vehicle at the current position in step S1e(t) and first heading angle deviation
Are directly obtained by a lane line identification module of the vehicle. In step S1, the lateral deviation y (t) of the vehicle at the preset home position is calculated and determined according to the formula shown in formula (1),
in the formula (1), Y (l, t) is the transverse deviation of the vehicle at the preset pre-aiming point position, and Ye(t) is the lateral deviation of the current position, l is the pre-aiming distance,
and p is the first course angle deviation, the road curvature directly determined by the lane line identification module, U is the longitudinal speed of the vehicle determined by the vehicle speed sensor, and t is time. In this embodiment, the pre-aiming distance l may be directly obtained by the lane line identification module.
The second course angle deviation e (l, t) is calculated and determined according to the formula shown in the formula (2),
in the formula (2), e (l, t) is the second course angle deviation, Y (l, t) is the transverse deviation of the vehicle at the preset pre-aiming point position, and Ye(t) is the lateral deviation of the current position, l is the pre-aiming distance,
and p is the first course angle deviation, the road curvature directly determined by the lane line identification module, U is the longitudinal speed of the vehicle determined by the vehicle speed sensor, and t is time.
As shown in fig. 4, the geometric relationship between the vehicle and the lane line and the image plane of the camera view are described, where l is the pre-aiming distance, and the lateral deviation Y of the vehicle at the current position can be directly obtained by the calculation of the lane line recognition modulee(t) and first heading angle deviation
Lateral deviation Y of current positione(t) is the deviation of the center of the front axle of the vehicle from the center of the center lane line.
And the transverse deviation between the preview point and the lane line is shown as the formula (8), namely
In equation (7), Y (l, t) is the lateral deviation of the vehicle at the preset home position, wherein,
is the lateral deviation caused by the deviation of the lateral distance of the vehicle from the lane line and the vehicle heading angle,
the amount of lateral displacement variation caused by the curvature of the road. l is the pre-aiming distance, Ye(t) is the lateral deviation of the current position, which is directly obtained by the lane line identification module,
and directly obtaining the first course angle deviation through a lane line identification module, wherein rho is the road curvature directly determined by the lane line identification module, S is the arc length determined in the lane line identification module, and t is time.
Integrating based on S coordinate system of road surface curve, converting it into integrating based on XY coordinate system of vehicle to obtain
The first integration of the curvature ρ (X, t) of the curve to obtain the slope of the curve and the first integration of the slope of the curve to obtain the offset distance, and thus the first integration of YcAnd (l, t) performing twice integration to obtain the transverse deviation of the aiming part caused by the curvature of the road.
Let ρ (X, t) be KX, where K is the coefficient of curvature change, by assuming that the road curvature changes linearly, and hence
The formula (8) can be simplified to the form shown in the formula (9),
the parameters in formula (9) are defined as in formula (8). U is the vehicle longitudinal speed determined by the vehicle speed sensor.
Assuming that the vehicle always follows the lane line, equation (9) can be converted into the form shown in equation (10),
accordingly, according to the triangular relationship in the triangle shown in fig. 4, the second heading angle deviation shown in the formula (2) can be obtained.
In the present embodiment, the estimation amount of the front wheel rotation angle of the vehicle in step S2
Is determined by calculation through a formula shown in formula (3),
in the formula (3), the reaction mixture is,
the method comprises the steps of obtaining an estimation quantity of a front wheel rotating angle of a vehicle, obtaining delta theta as a variation quantity of an actual course angle of the vehicle in a sampling period, obtaining a distance from a predetermined vehicle mass center to a vehicle front shaft, obtaining b distance from the predetermined vehicle mass center to the vehicle front shaft, obtaining U longitudinal vehicle speed of the vehicle, obtaining T predetermined sampling period, obtaining m predetermined first total vehicle weight of a compartment, and obtaining ayFor lateral acceleration, CαfFor a predetermined sidewall deflection stiffness, C, of the vehicle front axle wheelαrFor a predetermined vehicle front axle tire sidewall stiffness. In the embodiment, the predetermined sampling period T is the sampling period of the controller of the vehicle, the longitudinal speed U of the vehicle can be measured by the wheel speed sensor, and the lateral acceleration ayCan be directly measured by a gyroscope of the train, and
as shown in fig. 5, in the present embodiment, the formula (3) is derived from the formulas shown in the formula (4) and the formula (5),
in the formula (4), R is the motion radius of the vehicle, a is the distance between the mass center of the vehicle and the front axle of the vehicle, b is the distance between the mass center of the vehicle and the front axle of the vehicle,
an estimate of the angle of rotation of the front wheels, αfFront axle tire sidewall deviation angle, αrIs the rear axle tire sidewall deflection angle, ayIs lateral acceleration, m is the total vehicle mass of the first section of carriage, CαfFor a predetermined sidewall deflection stiffness, C, of the vehicle front axle wheelαrFor a predetermined vehicle front axle tire sidewall stiffness.
Δθ＝UT/R (5)
In the formula (5), Δ θ is the variation of the actual heading angle of the vehicle in the sampling period, U is the longitudinal speed of the vehicle, T is the sampling period, and R is the movement radius of the vehicle.
The formula (4) is substituted for the formula (5), so that the variation delta theta of the actual course angle of the vehicle in the sampling period can be obtained as shown in the formula (11),
the definition of each parameter in the formula (11) is the same as that in the formulas (4) and (5).
The front wheel rotation angle estimation quantity shown in the formula (3) can be determined by carrying out deformation through the formula (11)
The form is represented.
If the course value of the automatic driving vehicle in the current control period (sampling period) is set as theta (k) and the course value of the previous period is set as theta (k-1), the variation delta theta of the actual course angle of the vehicle in the control period can be determined as shown in the formula (12),
Δθ＝θ(k)-θ(k-1) (12)
through the calculation, the front axle wheel rotation angle can be determined only by knowing the actual vehicle heading angle theta (k) in the current control period, the actual vehicle heading angle theta (k-1) in the last control period, the longitudinal speed U of the vehicle, the predetermined distance a from the vehicle center of mass to the front axle of the vehicle and the predetermined distance b from the vehicle center of mass to the front axle of the vehicle.
In the present embodiment, the vehicle heading input deviation e' in step S3 is calculated by the formula shown in formula (6),
in the formula (6), e' is the input deviation of the vehicle heading, e is the second heading angle deviation,
is an estimate of the front wheel angle of the vehicle.
In the embodiment, after the input deviation e' of the decision controller is calculated and determined, the control increment Deltau of the front wheel steering angle of the vehicle can be calculated and obtained by the formula shown in the formula (7) by adopting the increment PID algorithm,
in equation (7), Δ u is the front wheel steering angle control increment, u (K) is the desired front wheel steering angle control amount at the K-th sampling time (K is 0,1,2, …), and K ispIs a predetermined proportionality coefficient, KiIs a predetermined integral coefficient, KdE' (k) is a heading input deviation of a k (k is 0,1,2, …) th sampling moment, and is a preset differential coefficient; and calculating a desired front wheel steering angle u of the vehicle through the front wheel steering angle control increment delta u of the vehicle, and controlling the vehicle to steer at the desired front wheel steering angle u of the vehicle.
Since Δ u (k) -u (k-1) and u (k-1) are known front wheel steering angle control quantities of the previous control period, the required desired front wheel steering angle u (k) of the vehicle CAN be determined by u (k) - Δ u + u (k-1), and the desired front wheel steering angle u (k) is transmitted to the steer-by-wire system through the CAN bus for execution, so that the steering of the vehicle is controlled.
In the embodiment, the absolute direction of the front wheel rotation angle is used as the feedback course of the decision controller, rather than the vehicle course, so as to simulate the 'wheel return' strategy adopted by a driver when manually driving through a sharp turning path, so that the controlled vehicle has better control performance than the conventional control method when turning with a large curvature radius, and the superiority is more obvious along with the increase of deviation. The traveling locus of the vehicle is shown in fig. 6.
The foregoing is considered as illustrative of the preferred embodiments of the invention and is not to be construed as limiting the invention in any way. Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical spirit of the present invention should fall within the protection scope of the technical scheme of the present invention, unless the technical spirit of the present invention departs from the content of the technical scheme of the present invention.
Claims (7)
1. A large-curvature automatic driving steering control method for a multi-axle steering vehicle is characterized by comprising the following steps:
s1, acquiring the transverse deviation Y of a vehicle at the current positione(t) and first heading angle deviation
Calculating the transverse deviation Y (t) and the second course angle deviation e (l, t) of the vehicle at the preset pre-aiming point position;
s2, calculating the estimation quantity of the front wheel corner of the vehicle
S3, estimating quantity of the front wheel corner
Calculating a vehicle course input deviation e' for the feedback of the second course angle deviation e (l, t);
s4, calculating an expected front wheel steering angle u of the vehicle according to the input deviation e' and controlling the vehicle to steer;
the lateral deviation y (t) of the vehicle at the preset home position in the step S1 is calculated and determined according to the formula shown in formula (1),
in the formula (1), Y (l, t) is the transverse deviation of the vehicle at the preset pre-aiming point position, and Ye(t) is the lateral deviation of the vehicle at the current position, l is the pre-line distance,
and p is the first course angle deviation, the road curvature directly determined by the lane line identification module, U is the longitudinal speed of the vehicle determined by the vehicle speed sensor, and t is time.
2. The large curvature automatic driving steering control method for a multi-axle steering vehicle according to claim 1, characterized in that: the lateral deviation Y of the vehicle at the current position in the step S1e(t) and first heading angle deviation
Are directly obtained by a lane line identification module of the vehicle.
3. The large curvature automatic driving steering control method for a multi-axle steering vehicle according to claim 2, characterized in that: the second course angle deviation e (l, t) is calculated and determined according to a formula shown in a formula (2),
in the formula (2), e (l, t) is the second course angle deviation, Y (l, t) is the transverse deviation of the vehicle at the preset pre-aiming point position, and Ye(t) is the lateral deviation of the vehicle at the current position, l is the pre-line distance,
and p is the first course angle deviation, the road curvature directly determined by the lane line identification module, U is the longitudinal speed of the vehicle determined by the vehicle speed sensor, and t is time.
4. Multi-axle steering vehicle according to any of claims 1 to 3The large-curvature automatic driving steering control method is characterized by comprising the following steps: the estimation amount of the front wheel rotation angle of the vehicle in said step S2
Is determined by calculation through a formula shown in formula (3),
in the formula (3), the reaction mixture is,
the method comprises the steps of obtaining an estimation quantity of a front wheel rotating angle of a vehicle, obtaining delta theta as a variation quantity of an actual course angle of the vehicle in a sampling period, obtaining a distance from a predetermined vehicle mass center to a vehicle front shaft, obtaining b distance from the predetermined vehicle mass center to the vehicle front shaft, obtaining U longitudinal vehicle speed of the vehicle, obtaining T predetermined sampling period, obtaining m predetermined first total vehicle weight of a compartment, and obtaining ayFor lateral acceleration, CαfFor a predetermined sidewall deflection stiffness, C, of the vehicle front axle wheelαrFor a predetermined vehicle front axle tire sidewall stiffness.
5. The large curvature autopilot steering control method for a multi-axle steered vehicle as defined in claim 4, characterized in that: the formula (3) is derived from formulas shown in formula (4) and formula (5),
in the formula (4), R is the motion radius of the vehicle, a is the distance between the mass center of the vehicle and the front axle of the vehicle, b is the distance between the mass center of the vehicle and the front axle of the vehicle,
an estimate of the angle of rotation of the front wheels, αfFront axle tire sidewall deviation angle, αrIs the rear axle tire sidewall deflection angle, ayIs in a lateral directionAcceleration, m is the total vehicle mass of the first section of the carriage, CαfFor a predetermined sidewall deflection stiffness, C, of the vehicle front axle wheelαrA predetermined vehicle front axle tire sidewall deflection stiffness;
Δθ＝UT/R (5)
in the formula (5), Δ θ is the variation of the actual heading angle of the vehicle in the sampling period, U is the longitudinal speed of the vehicle, T is the sampling period, and R is the movement radius of the vehicle.
6. The large curvature autopilot steering control method for a multi-axle steered vehicle as defined in claim 5, characterized in that: the vehicle heading input deviation e' in the step S3 is calculated by the formula shown in formula (6),
in the formula (6), e' is the input deviation of the vehicle heading, e is the second heading angle deviation,
is an estimate of the front wheel angle of the vehicle.
7. The large curvature autopilot steering control method for a multi-axle steered vehicle as defined in claim 6, characterized in that: the specific steps of step S4 include: the front wheel steering angle control increment Deltau of the vehicle is obtained through calculation by the formula shown in the formula (7),
in equation (7), Δ u is the front wheel steering angle control increment, u (K) is the desired front wheel steering angle control amount at the K-th sampling time (K is 0,1,2, …), and K ispIs a predetermined proportionality coefficient, KiIs a predetermined integral coefficient, KdE' (k) is a heading input deviation of a k (k is 0,1,2, …) th sampling moment, and is a preset differential coefficient;
and calculating a desired front wheel steering angle u of the vehicle through the front wheel steering angle control increment delta u of the vehicle, and controlling the vehicle to steer at the desired front wheel steering angle u of the vehicle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610913242.9A CN107963126B (en) | 2016-10-19 | 2016-10-19 | Large-curvature automatic driving steering control method for multi-axle steering vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610913242.9A CN107963126B (en) | 2016-10-19 | 2016-10-19 | Large-curvature automatic driving steering control method for multi-axle steering vehicle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107963126A CN107963126A (en) | 2018-04-27 |
| CN107963126B true CN107963126B (en) | 2020-03-17 |
Family
ID=61997090
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610913242.9A Active CN107963126B (en) | 2016-10-19 | 2016-10-19 | Large-curvature automatic driving steering control method for multi-axle steering vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107963126B (en) |
Families Citing this family (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108646747A (en) * | 2018-06-05 | 2018-10-12 | 上海交通大学 | Agri-vehicle path tracking control method |
| CN111610780B (en) * | 2019-02-25 | 2023-03-21 | 广州汽车集团股份有限公司 | Automatic driving vehicle path tracking control method and device |
| CN109976341B (en) * | 2019-03-21 | 2022-09-23 | 驭势科技（北京）有限公司 | Method for automatically driving vehicle to attach to road network, vehicle-mounted equipment and storage medium |
| CN110109451B (en) * | 2019-04-10 | 2022-07-12 | 东南大学 | Novel geometric path tracking algorithm considering path curvature |
| CN110316249B (en) * | 2019-07-25 | 2020-06-26 | 北京智行者科技有限公司 | Transverse control method and device |
| CN110398969B (en) * | 2019-08-01 | 2022-09-27 | 北京主线科技有限公司 | Domain steering control method and device during self-adaptive prediction of automatic driving vehicle |
| CN111158377B (en) * | 2020-01-15 | 2021-04-27 | 浙江吉利汽车研究院有限公司 | Transverse control method and system for vehicle and vehicle |
| CN111487966B (en) * | 2020-04-13 | 2022-09-09 | 哈尔滨工程大学 | Self-adaptive path tracking control method for unmanned surface vehicle based on waypoints |
| CN111923916B (en) * | 2020-07-24 | 2022-03-29 | 江苏大学 | Human-simulated steering modeling method and steering control system based on visual perception behaviors and ANFIS |
| CN112100565B (en) * | 2020-08-31 | 2022-09-06 | 中国第一汽车股份有限公司 | Road curvature determination method, device, equipment and storage medium |
| CN112622895B (en) * | 2020-12-30 | 2022-07-08 | 采埃孚商用车系统(青岛)有限公司 | Prediction control method applied to trajectory control of automatic driving |
| CN113552888B (en) * | 2021-07-29 | 2022-07-19 | 中国第一汽车股份有限公司 | Driving track control method, device, equipment and medium applied to unmanned vehicle |
| CN113581289B (en) * | 2021-09-01 | 2022-11-01 | 国汽智控(北京)科技有限公司 | Zero offset dynamic calibration method and device, electronic equipment and storage medium |
| CN113696969B (en) * | 2021-09-02 | 2022-07-26 | 浙江吉利控股集团有限公司 | Vehicle steering control method and system and vehicle |
| CN114620050B (en) * | 2022-03-28 | 2023-01-03 | 徐州华宝能源科技有限公司 | Fatigue driving detection method and system based on big data and cloud computing |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006005124A1 (en) * | 2004-07-13 | 2006-01-19 | Gene Kostecki | Multiaxle vehicle with steerable rear wheels |
| DE102005048321B4 (en) * | 2005-10-08 | 2008-10-09 | Linseal Gmbh | driving device |
| CN101293533A (en) * | 2008-05-12 | 2008-10-29 | 吉林大学 | Steering control system and control method for multi-shaft steering vehicle |
| RU2423281C1 (en) * | 2010-03-16 | 2011-07-10 | Государственное образовательное учреждение высшего профессионального образования "Тюменский государственный нефтегазовый университет" | Wheeled transport system |
| CN203651885U (en) * | 2013-11-29 | 2014-06-18 | 北汽福田汽车股份有限公司 | Auxiliary steering control system for heavy vehicles |
| CN104977933A (en) * | 2015-07-01 | 2015-10-14 | 吉林大学 | Regional path tracking control method for autonomous land vehicle |
| CN105667588A (en) * | 2016-03-17 | 2016-06-15 | 南通润邦重机有限公司 | Multi-shaft full-wheel steering device and engineering machine with multi-shaft full-wheel steering device |
-
2016
- 2016-10-19 CN CN201610913242.9A patent/CN107963126B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006005124A1 (en) * | 2004-07-13 | 2006-01-19 | Gene Kostecki | Multiaxle vehicle with steerable rear wheels |
| DE102005048321B4 (en) * | 2005-10-08 | 2008-10-09 | Linseal Gmbh | driving device |
| CN101293533A (en) * | 2008-05-12 | 2008-10-29 | 吉林大学 | Steering control system and control method for multi-shaft steering vehicle |
| RU2423281C1 (en) * | 2010-03-16 | 2011-07-10 | Государственное образовательное учреждение высшего профессионального образования "Тюменский государственный нефтегазовый университет" | Wheeled transport system |
| CN203651885U (en) * | 2013-11-29 | 2014-06-18 | 北汽福田汽车股份有限公司 | Auxiliary steering control system for heavy vehicles |
| CN104977933A (en) * | 2015-07-01 | 2015-10-14 | 吉林大学 | Regional path tracking control method for autonomous land vehicle |
| CN105667588A (en) * | 2016-03-17 | 2016-06-15 | 南通润邦重机有限公司 | Multi-shaft full-wheel steering device and engineering machine with multi-shaft full-wheel steering device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107963126A (en) | 2018-04-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107963126B (en) | Large-curvature automatic driving steering control method for multi-axle steering vehicle | |
| US8805604B2 (en) | Method for adjusting a steering system in a vehicle | |
| CN111610780B (en) | Automatic driving vehicle path tracking control method and device | |
| CN109520498B (en) | Virtual turnout system and method for virtual rail vehicle | |
| US9090285B2 (en) | Method for providing a lanekeeping assistance based on modifying mechanical sources of steering torques | |
| JP6055525B1 (en) | Vehicle travel control device | |
| Pérez et al. | Cascade architecture for lateral control in autonomous vehicles | |
| WO2018072647A1 (en) | Method and system utilized by multi-axle articulated vehicle tracking central lane line | |
| CN104742959B (en) | The track of vehicle keeps control device | |
| US10077072B2 (en) | Vehicle steering device and vehicle steering control method with reduced lateral speed | |
| US9610976B2 (en) | Lane departure prevention control system for vehicle | |
| CN107901917B (en) | A kind of automatic driving vehicle Trajectory Tracking Control method based on sliding coupling estimation of trackslipping | |
| US20180170377A1 (en) | Lane keep assist device | |
| CN111806426B (en) | Vehicle control device | |
| CN104709348A (en) | Collision avoidance control integrated with EPS controller | |
| JP5285384B2 (en) | Automatic driving system and automatic driving method | |
| CN105283363A (en) | Departure prevention support apparatus | |
| US20230211786A1 (en) | Path-controlling module, associated path-controlling device and associated method | |
| US20180037234A1 (en) | Method and device for estimating the friction values of a wheel of a vehicle against a substrate | |
| CN110712676A (en) | Rack force estimation for steering system | |
| CN106218708A (en) | Vehicular steering control apparatus | |
| CN111634331B (en) | Steering control method, device and system for automatic driving vehicle | |
| CN111559379A (en) | Road friction coefficient estimation using steering system signals | |
| US20220324466A1 (en) | Device for predictively controlling the movement of a motor vehicle | |
| US11912360B2 (en) | Vehicle control method, vehicle control system, and vehicle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |