CN117784661A - Multi-axis distributed drive-by-wire platform tracking control method - Google Patents
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Abstract
The invention relates to an autonomous tracking control method of a multi-axis distributed drive-by-wire platform, and belongs to the field of multi-axis vehicle control. The invention combines a variable pre-aiming distance kinematic model to acquire the lateral displacement deviation and the course angle deviation of the current state and the expected track of the vehicle, and establishes a geometric motion equation based on a pure tracking algorithm; the equivalent steering angle is used as a control quantity, and the association relation between parameters such as a gain coefficient, a vehicle wheelbase, a pretightening distance and the like and the actual running state of the vehicle is considered, so that an improved pure tracking control algorithm is designed, the running state of the controlled vehicle is more in line with the actual driver operating characteristic, and the tracking control precision and stability are improved.
Description
Technical Field
The invention belongs to the field of multi-axis vehicle control, and particularly relates to a multi-axis distributed drive-by-wire platform tracking control method.
Background
The multi-axis distributed drive-by-wire wheeled vehicle has the advantages of good ground attachment effect of the whole vehicle, independent and controllable driving and braking torque of each wheel, easy control of the control stability of the whole vehicle, and effective improvement of the running safety of the vehicle. The autonomous tracking system is an important link for realizing autonomous traveling by the drive-by-wire platform, and the tracking control of the multi-axis distributed driving vehicle is generally designed into a transverse track tracking control structure and a longitudinal speed tracking control structure, wherein the transverse track tracking controller outputs expected wheel turning angles and additional yaw moments according to transverse deviations and yaw angle deviations provided by a driver model, and the longitudinal speed tracking controller outputs various wheel turning moment instructions according to the longitudinal speed deviations and the expected additional yaw moments. However, the tracking control of the existing multi-axis distributed drive vehicle also has the following problems:
1. in the current transverse track tracking control research, pure tracking algorithms based on the geometric principle are all faced with the problem of poor robustness of high-speed tracking control, and the algorithms are usually applied to automatic driving control of a low-speed running vehicle, so that the applicable vehicle speed range of the algorithms in the actual application process is small; 2. the pure tracking algorithm only considers the kinematic characteristics of the vehicle, but does not consider the kinematic characteristics, and larger tracking deviation can be formed due to vehicle dynamics factors in the continuous curve tracking control process; 3. the expected value of the longitudinal speed is unreasonable, and the vehicle has the problems of low running efficiency caused by too small expected value of the longitudinal speed and instability caused by too high expected value.
Inventive step
First, the technical problem to be solved
The invention aims to solve the technical problems that: the multi-axis distributed drive-by-wire platform tracking control method is used for solving the problems in the prior art.
(II) technical scheme
In order to solve the technical problems, the invention provides a multi-axis distributed drive-by-wire platform tracking control method, which comprises the following steps:
step 1: the track preprocessing algorithm segments a complete reference track acquired by a real vehicle or obtained by track planning in an off-line processing mode according to a fixed number of track points, and track data after segmentation is stored in a track storage unit through program brushing;
step 2: the platform motion controller selects a specific segment of the reference track according to the vehicle positioning information, calculates a pretightening distance according to the vehicle speed information, and determines a pretightening point P (X) according to the track data, the vehicle pose information and the pretightening distance P ,Y P ) Simultaneously acquiring pretightening deviations, including lateral displacement deviations y e And course angle deviationWherein the lateral displacement deviation y e For the distance of the current pre-aiming track point P of the vehicle in the direction of travel of the vehicle, the course angle deviation +.>The difference value between the heading angle corresponding to the pre-aiming point P and the current heading angle of the vehicle;
step 3: the autonomous tracking algorithm outputs a corner control quantity and a tracking system state according to vehicle pose information and pre-aiming deviation, the speed constraint algorithm outputs a speed constraint signal to the constant-speed cruising module based on the current road curvature and the tracking system state, and the internal braking monitoring module of the tracking system outputs a braking percentage signal to a braking system of the platform according to the tracking system state and the vehicle state signal;
step 4: the steering control module of the distributed driving platform distributes the proportion of mechanical steering and differential steering in real time by combining the steering angle control quantity input by the tracking system and the vehicle speed information, and sends a control signal to the actuator;
step 5: the platform steering, driving and braking systems respectively execute corresponding control instructions to complete motion control, and the motion state quantity of the platform is fed back to each module of the autonomous tracking system to complete closed-loop control.
The track preprocessing algorithm in the step 1 generates the data size of a single-section track and the number of fragments contained in a complete track according to the storage space of the platform motion controller and the reference track data size.
And 2, selecting a specific track section, calculating the distances from all the reference track points to the current position of the vehicle one by one, marking the track point with the minimum distance as the nearest track point, and selecting the reference track section where the nearest track point is positioned as the specific section of the reference track.
Wherein in the step 2, the minimum pretightening distance L is passed d0 Calculating a pretightening distance L with a platform real-time vehicle speed v d The calculation formula is as follows:
L d ＝L d0 +f(v)
the pre-aiming deviation is calculated by calculating the distance from all track points in a reference track section to the current position of the vehicle, selecting the distance value closest to the pre-aiming distance, taking the direction from the position point of the platform to the track point and the included angle of the heading of the platform as an acute angle, recording the track point as the pre-aiming point at the current moment, and determining the lateral displacement deviation ye and heading angle deviation of the platform relative to the pre-aiming point at the current moment according to the geometric position relation
The rotation angle control quantity delta output by the autonomous tracking algorithm meets the following relation:
δ＝K·arctan(2L·sin(α)/L d )
wherein K is a gain coefficient, L is a vehicle wheelbase, alpha is an azimuth angle of a desired track point relative to the vehicle, the gain coefficient K is defined to be directly related to a vehicle deflection distance dist, and a maximum value K exists max The specific relation with the minimum value of 1.2 is as follows:
K max the magnitude of the value determines the gain coefficient of the corresponding output rotation angle control quantity when the vehicle has larger deflection quantity, and K is established max Nonlinear relation with vehicle speed:
K max ＝f(v)
each wheel of the multi-axis distributed driving platform has driving capability, when part of wheel driving fails, the platform driving system can be used in a degrading mode, and the driving state parameter s represents the driving state of each axis of the platform, so that when the driving state parameter s of the platform changes, the vehicle wheelbase is adjusted to compensate the differential steering effect, and the following relation is satisfied between the wheelbase L and the driving state s of the vehicle:
L＝f(s)
in the formula, the vehicle driving state s is a discrete value.
Wherein the lateral displacement deviation y e And course angle deviationThe method is used for representing the deviation between the current pose of the platform and the reference track, and the larger the numerical value is, the higher the degree of deviation from the reference track is.
The speed constraint algorithm in the step 3 is used for ensuring that the vehicle keeps running stability under various paths, and establishing a road curvature rho, a tracking system state S and a speed constraint v max The mapping relation of (2) is as follows:
v max ＝min(v ρ v S )
v ρ ＝f(ρ)
v S ＝f(S)
wherein, the road curvature rho is the highest speed of the vehicle allowed to run under the condition that no side turning occurs, and the tracking system state S is a tracking system state parameter.
The automatic triggering braking system is used for automatically triggering the braking system to execute braking action when the platform monitors fault information fed back by the platform or the state of the tracking system is abnormal in the autonomous tracking running process, and the braking percentage of the automatic triggering braking system is related to the real-time speed and the yaw rate of the platform.
The distributed driving platform steering control module is used for receiving expected corner information from the autonomous tracking system, combining current vehicle speed information of the vehicle, outputting the magnitude of a mechanical steering angle of the platform in a current state and an additional yaw moment of differential steering by the autonomous output platform, and outputting the two control quantities to the steering system and the driving system respectively.
(III) beneficial effects
Compared with the prior art, the invention has the following beneficial effects:
(1) The adaptability of the pure tracking algorithm under various working conditions such as straight running, continuous curve and the like is optimized, the reliability of the pure tracking algorithm applied to medium-high speed vehicles is improved, and meanwhile the tracking precision is improved;
(2) The adoption of the method for sectionally storing the reference track not only improves the utilization rate of the internal storage space by the controller, but also has the capability of tracking the track point with larger data quantity on the premise of the same hardware configuration; and the operation amount of the track tracking controller is reduced, and the real-time performance of the system is improved.
(3) The invention completes the longitudinal speed planning and braking control based on fault analysis and road curvature analysis, and improves the safety and the degree of autonomy of the autonomous tracking control method.
Drawings
FIG. 1 is a diagram illustrating the implementation of the method according to the present invention;
FIG. 2 is a schematic diagram of a driver model with varying pretightening distance;
FIG. 3 is a graph showing the experimental results of the present invention.
Detailed Description
For the purposes of clarity, steps, and advantages of the present invention, a detailed description of the embodiments of the present invention will be described in detail below with reference to the drawings and examples.
The embodiment provides a multi-axis distributed drive-by-wire platform tracking control method, which comprises the following steps:
step 1: the track preprocessing algorithm segments a complete reference track acquired by a real vehicle or obtained by track planning in an off-line processing mode according to a fixed number of track points, and track data after segmentation is stored in a track storage unit through program brushing;
step 2: platform motion controller based onThe vehicle positioning information selects a specific segment of the reference track, calculates a pretightening distance from the vehicle speed information, and determines a pretightening point P (X) according to the track data of the segment, the vehicle pose information and the pretightening distance P ,Y P ) Simultaneously acquiring pretightening deviations, including lateral displacement deviations y e And course angle deviationWherein the lateral displacement deviation y e For the distance of the current pre-aiming track point P of the vehicle in the direction of travel of the vehicle, the course angle deviation +.>The difference value between the heading angle corresponding to the pre-aiming point P and the current heading angle of the vehicle;
step 3: the autonomous tracking algorithm outputs a corner control quantity and a tracking system state according to vehicle pose information and pre-aiming deviation, the speed constraint algorithm outputs a speed constraint signal to the constant-speed cruising module based on the current road curvature and the tracking system state, and the internal braking monitoring module of the tracking system outputs a braking percentage signal to a braking system of the platform according to the tracking system state and the vehicle state signal;
step 4: the steering control module of the distributed driving platform distributes the proportion of mechanical steering and differential steering in real time by combining the steering angle control quantity input by the tracking system and the vehicle speed information, and sends a control signal to the actuator;
step 5: the platform steering, driving and braking systems respectively execute corresponding control instructions to complete motion control, and the motion state quantity of the platform is fed back to each module of the autonomous tracking system to complete closed-loop control.
The track preprocessing algorithm in the step 1 generates the data size of a single-section track and the number of fragments contained in a complete track according to the storage space of the platform motion controller and the reference track data size.
And 2, selecting a specific track section, calculating the distances from all the reference track points to the current position of the vehicle one by one, marking the track point with the minimum distance as the nearest track point, and selecting the reference track section where the nearest track point is positioned as the specific section of the reference track.
Wherein in the step 2, the minimum pretightening distance L is passed d0 Calculating a pretightening distance L with a platform real-time vehicle speed v d The calculation formula is as follows:
L d ＝L d0 +f(v)
the pre-aiming deviation is calculated by calculating the distance from all track points in a reference track section to the current position of the vehicle, selecting the distance value closest to the pre-aiming distance, taking the direction from the position point of the platform to the track point and the included angle between the direction of the platform and the heading of the platform as an acute angle, recording the track point as the pre-aiming point at the current moment, and determining the lateral displacement deviation y of the platform relative to the pre-aiming point at the current moment according to the geometric position relation e Deviation of course angle
The rotation angle control quantity delta output by the autonomous tracking algorithm meets the following relation:
δ＝K·arctan(2L·sin(α)/L d )
wherein K is a gain coefficient, L is a vehicle wheelbase, alpha is an azimuth angle of a desired track point relative to the vehicle, the gain coefficient K is defined to be directly related to a vehicle deflection distance dist, and a maximum value K exists max The specific relation with the minimum value of 1.2 is as follows:
K max the magnitude of the value determines the gain coefficient of the corresponding output rotation angle control quantity when the vehicle has larger deflection quantity, and K is established max Nonlinear relation with vehicle speed:
K max ＝f(v)
each wheel of the multi-axis distributed driving platform has driving capability, when part of wheel driving fails, the platform driving system can be used in a degrading mode, and the driving state parameter s represents the driving state of each axis of the platform, so that when the driving state parameter s of the platform changes, the vehicle wheelbase is adjusted to compensate the differential steering effect, and the following relation is satisfied between the wheelbase L and the driving state s of the vehicle:
L＝f(s)
in the formula, the vehicle driving state s is a discrete value.
Wherein the lateral displacement deviation y e And course angle deviationThe method is used for representing the deviation between the current pose of the platform and the reference track, and the larger the numerical value is, the higher the degree of deviation from the reference track is.
The speed constraint algorithm in the step 3 is used for ensuring that the vehicle keeps running stability under various paths, and establishing a road curvature rho, a tracking system state S and a speed constraint v max The mapping relation of (2) is as follows:
v max ＝min(v ρ v S )
v ρ ＝f(ρ)
v S ＝f(S)
wherein, the road curvature rho is the highest speed of the vehicle allowed to run under the condition that no side turning occurs, and the tracking system state S is a tracking system state parameter.
The automatic triggering braking system is used for automatically triggering the braking system to execute braking action when the platform monitors fault information fed back by the platform or the state of the tracking system is abnormal in the autonomous tracking running process, and the braking percentage of the automatic triggering braking system is related to the real-time speed and the yaw rate of the platform.
The distributed driving platform steering control module is used for receiving expected corner information from the autonomous tracking system, combining current vehicle speed information of the vehicle, outputting the magnitude of a mechanical steering angle of the platform in a current state and an additional yaw moment of differential steering by the autonomous output platform, and outputting the two control quantities to the steering system and the driving system respectively.
In the step 1, the complete reference track is segmented according to a fixed number of track points, and according to the storage space of the platform motion controller and the reference track data quantity, the segmentation principle is that two adjacent sections of reference tracks at least comprise track point arrays with the distance of 50m for connection.
The reference trajectory segment storage may solve the following problems: 1) The storage space of the central controller is insufficient due to the large dimension of the reference track variable; 2) The smaller dimension reference track point is intercepted, so that the operand of the track tracking controller can be reduced, and the control instantaneity is improved.
In the step 1, a specific section of a reference track is selected according to the vehicle positioning information, and the principle of closest distance to the vehicle is adopted for the selection of the specific section of the reference track. And calculating the distances from all the reference track points to the current position of the vehicle one by one, and taking the track point with the minimum distance as the nearest track point. The reference track segment is generally selected as the reference track segment where the nearest track point is located, and when the nearest track point is in the joint area of the current segment, the next adjacent reference track segment is selected as the output reference track.
Fig. 3 (a) depicts the travel path of a test vehicle tracked using the improved autonomous tracking control method presented herein, with a starting point located in the east longitude 115.93 °, north latitude 40.367 °. The section of track comprises an off-road undulating road, a pavement road, a linear runway and a curve, and the applicability of the autonomous tracking control method under different working conditions can be fully verified. The straight line segment with the latitude value smaller than 40.368 is an off-road undulating path; the curve part and the straight line section with the latitude value larger than 40.368 are cement paving roads, and the mileage of the whole runway is 2.65km.
Fig. 3 (b-c) shows the real-time vehicle speed and the desired speed of the test vehicle during the tracking process. It can be seen that the speed change trend of the vehicle in the whole process accords with the test road condition change trend.
From the dimension analysis of the test time, the speed of the vehicle is kept at about 33km/h within the first 40 percent of the duration, and the vibration of an actual speed curve is large, because the speed information is acquired by the speed of the wheel fed back by the hub motor, the hub motor adopts torque control, the wheels can take off, impact and the like when moving on the off-road fluctuation road, and the actual output speed has instantaneous abrupt change. In addition, the vehicle speed is affected by a significant change in running resistance when the vehicle runs on off-road.
The middle 10% of the time length is that the speed is automatically reduced to about 18km/h when the vehicle runs to a curve, and a small section of straight line track exists in the middle of the curve, so that the expected speed is increased to 24km/h in a short time. The actual vehicle speed curve basically accords with the variation trend of the expected vehicle speed, but has overshoot of about 2 km/h.
The vehicle runs to the cement straight runway within the last 50 percent of the duration, the expected speed is 42km/h, and the actual speed is basically consistent with the value. The expected speed of the vehicle in the section is 25km/h in a short time value, because small steering occurs at a certain position of the cement straight runway when track points are acquired before the test, so that the planned speed of the vehicle is reduced and then automatically recovered. Further, in the time range of 280s-300s, the desired vehicle speed is first reduced to 20km/h, then restored to 42km/h, and the actual vehicle speed is reduced to zero. The method is characterized in that the reference track led into the chassis controller is a complete track, only one section of runway is tested in the experiment, constant-speed cruising is canceled at the remote control end when the experiment is finished, and braking intervention is applied.
FIG. 3 (d) is a plot of the desired rotational angle output by the autonomous tracking control system. It can be seen that the expected rotation angle of the test vehicle output on the linear runway is relatively gentle, the numerical value of the test vehicle is changed severely under off-road conditions, and the amplitude is basically kept within the range of-5 degrees to 5 degrees. The positioning system of the test vehicle is fixed at the highest position of the whole vehicle, and the vehicle is seriously inclined left and right when running on an off-road, so that the positioning accuracy is about 10cm lower than a normal value, and the expected rotation angle change is obvious. When the vehicle turns over, the expected turning angle value reaches about 18 degrees, the absolute value of the expected turning angle on the cement runway is not more than 2 degrees, and the change curve shows that the expected turning angle has oscillation change, because the experimental vehicle does not maintain a steering system and a suspension system, and the steering wheel has a clearance of +/-1 degree under the condition that the vehicle is stationary.
Fig. 3 (e) is a tracking bias during autonomous tracking of an experimental vehicle. It can be seen that the tracking deviation of the vehicle on the straight off-road and the curve position is not more than + -40 cm, and the tracking deviation on the straight cement road is not more than + -20 cm. Furthermore, the tracking deviation is not a continuously varying curve, since the tracking deviation of this experiment is defined as the distance of the vehicle position to the nearest track point, and the distance between adjacent reference track points is typically 10cm, and thus the actual tracking deviation should be smaller than the measured value.
Fig. 3 (f) is a graph of the variation of gain factor in a modified pure tracking algorithm. The value of the gain coefficient is related to the tracking deviation and the vehicle speed, and it can be seen that the gain coefficient is obviously changed under off-road conditions, and the value of the gain coefficient is relatively larger at the position of the curve due to the influence of the tracking deviation.
Fig. 3 (g) is a graph showing the change of the pretightening distance parameter in the improved pure tracking algorithm, and it can be seen that the pretightening distance change is basically consistent with the real-time vehicle speed change.
FIG. 3 (h) is a track segment change selected by the autonomous tracking control system in the chassis controller. The number of complete trace points is 18256, and if it is completely stored in one variable, the chassis controller will have a problem of insufficient storage space. The autonomous tracking control method performs track division according to the standard that the total number of track points of each track is 3000 and the number of track points of the joint part is 900, and finally the total number of track segments is 9. As can be seen from the figure, in the annular runway, the autonomous tracking system can autonomously complete the automatic engagement of the adjacent track segments, thereby completing the autonomous tracking of the complete track.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (10)
1. The multi-axis distributed drive-by-wire platform tracking control method is characterized by comprising the following steps of:
step 1: the track preprocessing algorithm segments a complete reference track acquired by a real vehicle or obtained by track planning in an off-line processing mode according to a fixed number of track points, and track data after segmentation is stored in a track storage unit through program brushing;
step 2: the platform motion controller selects a specific segment of the reference track according to the vehicle positioning information, calculates a pretightening distance according to the vehicle speed information, and determines a pretightening point P (X) according to the track data, the vehicle pose information and the pretightening distance P ,Y P ) Simultaneously acquiring pretightening deviations, including lateral displacement deviations y e And course angle deviationWherein the lateral displacement deviation y e For the distance of the current pre-aiming track point P of the vehicle in the direction of travel of the vehicle, the course angle deviation +.>The difference value between the heading angle corresponding to the pre-aiming point P and the current heading angle of the vehicle;
step 3: the autonomous tracking algorithm outputs a corner control quantity and a tracking system state according to vehicle pose information and pre-aiming deviation, the speed constraint algorithm outputs a speed constraint signal to the constant-speed cruising module based on the current road curvature and the tracking system state, and the internal braking monitoring module of the tracking system outputs a braking percentage signal to a braking system of the platform according to the tracking system state and the vehicle state signal;
step 4: the steering control module of the distributed driving platform distributes the proportion of mechanical steering and differential steering in real time by combining the steering angle control quantity input by the tracking system and the vehicle speed information, and sends a control signal to the actuator;
step 5: the platform steering, driving and braking systems respectively execute corresponding control instructions to complete motion control, and the motion state quantity of the platform is fed back to each module of the autonomous tracking system to complete closed-loop control.
2. The method for controlling tracking of a multi-axis distributed drive-by-wire platform according to claim 1, wherein the track preprocessing algorithm in step 1 generates the data size of the single-segment track and the number of segments included in the complete track according to the storage space of the platform motion controller and the reference track data size.
3. The tracking control method of the multi-axis distributed drive-by-wire platform according to claim 1, wherein in the step 2, a specific track segment is selected, distances from all reference track points to the current position of the vehicle are calculated one by one, a track point with the smallest distance is recorded as a nearest track point, and the reference track segment where the nearest track point is located is selected as the specific segment of the reference track.
4. The method for controlling tracking of a multi-axis distributed drive-by-wire platform according to claim 1, wherein the minimum pretightening distance L is used in the step 2 d0 Calculating a pretightening distance L with a platform real-time vehicle speed v d The calculation formula is as follows:
L d ＝L d0 +f(v)
5. the tracking control method of a multi-axis distributed drive-by-wire platform according to claim 3, wherein the pre-aiming deviation is obtained by calculating the distances from all track points in a reference track section to the current position of the vehicle, selecting the closest distance value to the pre-aiming distance, setting the included angle between the direction from the position point of the platform to the track point and the heading of the platform as an acute angle, recording the track point as the pre-aiming point at the current moment, and determining the lateral displacement deviation y of the platform relative to the pre-aiming point at the current moment according to the geometric position relation e Deviation of course angle
6. The multi-axis distributed drive-by-wire platform tracking control method according to claim 1, wherein the rotation angle control amount δ outputted by the autonomous tracking algorithm satisfies the following relationship:
δ＝K·arctan(2L·sin(α)/L d )
wherein K is a gain coefficient, L is a vehicle wheelbase, alpha is an azimuth angle of a desired track point relative to the vehicle, and the gain coefficient K is defined to be directly related to a vehicle deflection distance distAnd there is a maximum value K max The specific relation with the minimum value of 1.2 is as follows:
K max the magnitude of the value determines the gain coefficient of the corresponding output rotation angle control quantity when the vehicle has larger deflection quantity, and K is established max Nonlinear relation with vehicle speed:
K max ＝f(v)
each wheel of the multi-axis distributed driving platform has driving capability, when part of wheel driving fails, the platform driving system can be used in a degrading mode, and the driving state parameter s represents the driving state of each axis of the platform, so that when the driving state parameter s of the platform changes, the vehicle wheelbase is adjusted to compensate the differential steering effect, and the following relation is satisfied between the wheelbase L and the driving state s of the vehicle:
L＝f(s)
in the formula, the vehicle driving state s is a discrete value.
7. The multi-axis distributed drive-by-wire platform tracking control method of claim 1, wherein the lateral displacement bias y e And course angle deviationThe method is used for representing the deviation between the current pose of the platform and the reference track, and the larger the numerical value is, the higher the degree of deviation from the reference track is.
8. The method for controlling the tracking of the multi-axis distributed drive-by-wire platform according to claim 1, wherein the speed constraint algorithm in the step 3 is used for ensuring that the vehicle keeps running stability under various paths, and establishing the road curvature ρ, the tracking system state S and the speed constraint v max The mapping relation of (2) is as follows:
v max ＝min(v ρ v S )
v ρ ＝f(ρ)
v S ＝f(S)
wherein, the road curvature rho is the highest speed of the vehicle allowed to run under the condition that no side turning occurs, and the tracking system state S is a tracking system state parameter.
9. The method for controlling the tracking of the multi-axis distributed drive-by-wire platform according to claim 1, wherein the brake monitoring module is used for automatically triggering the brake system to execute a braking action when the platform monitors the fault information fed back by the platform or the state of the tracking system is abnormal in the autonomous tracking running process, and the braking percentage is related to the real-time speed and the yaw rate of the platform.
10. The method of claim 1, wherein the distributed drive-by-wire platform tracking control module is configured to receive desired rotation angle information from the autonomous tracking system, combine current vehicle speed information of the vehicle, mechanically steer the autonomous output platform in a current state, and an additional yaw moment of differential steering, and output the two control amounts to the steering system and the driving system, respectively.
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