Multi-axle distributed drive drive-by-wire platform tracking control method

Abstract

The present application relates to a kind of multi-axis distributed drive drive-by-wire platform autonomous tracking control method, belong to multi-axis vehicle control field.The present application combines the lateral displacement deviation and the heading angle deviation of the current state and desired trajectory of vehicle obtained by variable preview distance kinematic model, establishes the geometric motion equation based on pure tracking algorithm;With equivalent steering angle as control quantity, consider the correlation between gain coefficient, vehicle wheelbase and preview distance and other parameters and the actual driving state of vehicle, design improved pure tracking tracking control algorithm, make the motion state of controlled vehicle more in line with actual driver operating characteristics, improve tracking control precision and stability.

Description

Technical Field

[0001] This invention belongs to the field of multi-axle vehicle control, specifically relating to a tracking control method for a multi-axle distributed drive drive-by-wire platform. Background Technology

[0002] Multi-axle distributed drive drive-by-wire wheeled vehicles offer advantages such as good overall vehicle ground adhesion, independent controllable drive and braking torque for each wheel, and easy implementation of overall vehicle handling stability control, effectively improving vehicle driving safety. The autonomous tracking system is a crucial component for achieving autonomous driving on a drive-by-wire platform. Tracking control in multi-axle distributed drive vehicles is typically designed with a lateral trajectory tracking control and a longitudinal speed tracking control structure. The lateral trajectory tracking controller outputs the desired wheel steering angle and additional yaw torque based on the lateral and yaw angle deviations provided by the driver model. The longitudinal speed tracking controller outputs torque commands for each wheel based on the longitudinal speed deviation and the desired additional yaw torque. However, existing tracking control systems for multi-axle distributed drive vehicles still have the following problems:

[0003] 1. In current research on lateral trajectory tracking control, pure tracking algorithms based on geometric principles all face the problem of poor robustness in high-speed tracking control. This algorithm is usually applied to the autonomous driving control of low-speed vehicles, which results in a very small applicable speed range in practical applications. 2. Since pure tracking algorithms only consider the kinematic characteristics of the vehicle and not its dynamic characteristics, they will form a large tracking deviation due to vehicle dynamic factors during continuous curve tracking control. 3. Unreasonable longitudinal speed expectation values ​​can lead to low driving efficiency due to excessively low longitudinal speed expectation values, and vehicle instability due to excessively high expectation values.

[0004] Invention steps

[0005] (a) Technical problems to be solved

[0006] The technical problem to be solved by the present invention is to provide a tracking control method for a multi-axis distributed drive drive-by-wire platform to solve the problems existing in the prior art.

[0007] (II) Technical Solution

[0008] To address the aforementioned technical problems, this invention provides a tracking control method for a multi-axis distributed drive drive-by-wire platform, comprising the following steps:

[0009] Step 1: The trajectory preprocessing algorithm divides the complete reference trajectory obtained from real vehicle data collection or trajectory planning into segments according to a fixed number of trajectory points through offline processing. The segmented trajectory data is then stored in the trajectory storage unit by program writing.

[0010] Step 2: The platform motion controller selects specific segments of the reference trajectory based on the vehicle positioning information, calculates the pre-aiming distance from the vehicle speed information, and determines the pre-aiming point P(X) based on the trajectory data of that segment, the vehicle pose information, and the pre-aiming distance. P ,Y P Simultaneously, the aiming deviation is obtained, including the lateral displacement deviation y. e and heading angle deviation Among them, the lateral displacement deviation y e The distance from the vehicle's current target trajectory point P to the vehicle's direction of travel, and the heading angle deviation. This is the difference between the heading angle corresponding to the aiming point P and the current vehicle heading angle;

[0011] Step 3: The autonomous tracking algorithm outputs the steering angle control quantity and tracking system status based on the vehicle's pose information and pre-aiming deviation. The speed constraint algorithm outputs the speed constraint signal to the cruise control module based on the current road curvature and tracking system status. The internal braking monitoring module of the tracking system outputs the braking percentage signal to the platform's braking system based on the tracking system status and vehicle status signal.

[0012] Step 4: The distributed drive platform steering control module uses the steering angle control input from the tracking system, combined with vehicle speed information, to allocate the ratio of mechanical steering and differential steering in real time, and sends the control signal to the actuator.

[0013] Step 5: The platform's steering, drive, and braking systems execute corresponding control commands to complete motion control. The platform's motion state is fed back to each module of the autonomous tracking system to complete closed-loop control.

[0014] In step 1, the trajectory preprocessing algorithm generates the data size of a single trajectory segment and the number of segments contained in the complete trajectory based on the storage space of the platform motion controller and the amount of reference trajectory data.

[0015] In step 2, the specific trajectory segment is selected by calculating the distance from all reference trajectory points to the vehicle's current position one by one, taking the trajectory point with the smallest distance as the nearest trajectory point, and selecting the reference trajectory segment where the nearest trajectory point is located as the specific segment of the reference trajectory.

[0016] In step 2, the minimum pre-aiming distance L is used. d0 The predicted aiming distance L is calculated based on the real-time vehicle speed v on the platform. d The calculation formula is as follows:

[0017] L d =L d0 +f(v)

[0018] The aiming deviation is calculated by taking the distances from all trajectory points within the reference trajectory segment to the vehicle's current position, selecting the distance closest to the aiming distance, and defining the trajectory point as the aiming point at the current moment as the trajectory point with an acute angle between the direction from the platform's current position to the aiming point and the platform's heading. The lateral displacement deviation ye and heading angle deviation of the platform relative to the aiming point at the current moment are then determined based on geometric positional relationships.

[0019] The rotation angle control quantity δ output by the autonomous tracking algorithm satisfies the following relationship:

[0020] δ=K·arctan(2L·sin(α) / L d )

[0021] Where K is the gain coefficient, L is the vehicle wheelbase, and α is the azimuth angle of the desired trajectory point relative to the vehicle. The gain coefficient K is defined to be directly related to the vehicle yaw distance dist, and has a maximum value K. max The specific relationship between the minimum value of 1.2 and the minimum value is shown in the following formula:

[0022]

[0023] K max The value determines the gain coefficient of the output steering angle control quantity when the vehicle has a large yaw. Establish K... max Nonlinear relationship with vehicle speed:

[0024] K max =f(v)

[0025] Each wheel of the multi-axle distributed drive platform has driving capability. When the driving of some wheels fails, the platform drive system can be degraded for use. The drive state parameter s characterizes the drive state of each axle of the platform. Therefore, when the platform drive state parameter s changes, the differential steering effect is compensated by adjusting the vehicle wheelbase. The wheelbase L and the vehicle drive state s satisfy the following relationship:

[0026] L = f(s)

[0027] In the formula, the vehicle driving state s is a discrete value.

[0028] Wherein, the lateral displacement deviation y e and heading angle deviation It is used to characterize the deviation between the platform's current pose and the reference trajectory. The larger the value, the greater the degree of deviation from the reference trajectory.

[0029] In step 3, the speed constraint algorithm is used to ensure the vehicle maintains driving stability under various paths, establishing the road curvature ρ, the tracking system state S, and the speed constraint v. max The mapping relationship is as follows:

[0030] v max =min(v ρ v S )

[0031] v ρ =f(ρ)

[0032] v S =f(S)

[0033] In the formula, the road curvature ρ is the maximum speed at which the vehicle is allowed to travel without rolling over, and the tracking system state S is the tracking system state parameter.

[0034] The braking monitoring module is used to automatically trigger the braking system to perform braking actions when the platform detects fault information or abnormal status of the tracking system during autonomous tracking. The braking percentage is related to the platform's real-time vehicle speed and yaw rate.

[0035] The distributed drive platform steering control module receives the desired steering angle information from the autonomous tracking system, combines it with the vehicle's current speed information, autonomously outputs the mechanical steering angle of the platform in the current state, as well as the additional yaw torque of differential steering, and outputs the above two control quantities to the steering system and the drive system respectively.

[0036] (III) Beneficial Effects

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] (1) The adaptability of the pure tracking algorithm under various working conditions such as straight driving and continuous curves has been optimized, as well as its reliability in medium and high speed vehicles, while improving the tracking accuracy.

[0039] (2) The method of segmented storage of reference trajectory not only improves the utilization of internal storage space of the controller, enabling it to track trajectory points with a larger amount of data under the premise of the same hardware configuration, but also reduces the computational load of the trajectory tracking controller and improves the real-time performance of the system.

[0040] (3) Based on fault analysis and road curvature analysis, this invention completes longitudinal speed planning and braking control, which improves the safety and autonomy of the autonomous tracking control method. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating the execution process of the method provided by the present invention.

[0042] Figure 2 A schematic diagram of a driver model with variable pre-aiming distance;

[0043] Figure 3 The figure shows the experimental results of this invention. Detailed Implementation

[0044] To make the objectives, steps, and advantages of the present invention clearer, the specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples.

[0045] This embodiment provides a tracking control method for a multi-axis distributed drive drive-by-wire platform, including the following steps:

[0046] Step 1: The trajectory preprocessing algorithm divides the complete reference trajectory obtained from real vehicle data collection or trajectory planning into segments according to a fixed number of trajectory points through offline processing. The segmented trajectory data is then stored in the trajectory storage unit by program writing.

[0047] Step 2: The platform motion controller selects specific segments of the reference trajectory based on the vehicle positioning information, calculates the pre-aiming distance from the vehicle speed information, and determines the pre-aiming point P(X) based on the trajectory data of that segment, the vehicle pose information, and the pre-aiming distance. P ,Y P Simultaneously, the aiming deviation is obtained, including the lateral displacement deviation y. e and heading angle deviation Among them, the lateral displacement deviation y e The distance from the vehicle's current target trajectory point P to the vehicle's direction of travel, and the heading angle deviation. This is the difference between the heading angle corresponding to the aiming point P and the current vehicle heading angle;

[0048] Step 3: The autonomous tracking algorithm outputs the steering angle control quantity and tracking system status based on the vehicle's pose information and pre-aiming deviation. The speed constraint algorithm outputs the speed constraint signal to the cruise control module based on the current road curvature and tracking system status. The internal braking monitoring module of the tracking system outputs the braking percentage signal to the platform's braking system based on the tracking system status and vehicle status signal.

[0049] Step 4: The distributed drive platform steering control module uses the steering angle control input from the tracking system, combined with vehicle speed information, to allocate the ratio of mechanical steering and differential steering in real time, and sends the control signal to the actuator.

[0050] Step 5: The platform's steering, drive, and braking systems execute corresponding control commands to complete motion control. The platform's motion state is fed back to each module of the autonomous tracking system to complete closed-loop control.

[0051] In step 1, the trajectory preprocessing algorithm generates the data size of a single trajectory segment and the number of segments contained in the complete trajectory based on the storage space of the platform motion controller and the amount of reference trajectory data.

[0052] In step 2, the specific trajectory segment is selected by calculating the distance from all reference trajectory points to the vehicle's current position one by one, taking the trajectory point with the smallest distance as the nearest trajectory point, and selecting the reference trajectory segment where the nearest trajectory point is located as the specific segment of the reference trajectory.

[0053] In step 2, the minimum pre-aiming distance L is used. d0 The platform calculates the pre-aiming distance L based on the real-time vehicle speed v. d The calculation formula is as follows:

[0054] L d =L d0 +f(v)

[0055] The aiming deviation is calculated by taking the distances from all trajectory points within the reference trajectory segment to the vehicle's current position, selecting the distance closest to the aiming distance, and defining the trajectory point as the aiming point at the current moment as the trajectory point with an acute angle to the platform's heading. The lateral displacement deviation y of the platform relative to the aiming point at the current moment is then determined based on geometric positional relationships. e Heading angle deviation

[0056] The rotation angle control quantity δ output by the autonomous tracking algorithm satisfies the following relationship:

[0057] δ=K·arctan(2L·sin(α) / L d )

[0058] Where K is the gain coefficient, L is the vehicle wheelbase, and α is the azimuth angle of the desired trajectory point relative to the vehicle. The gain coefficient K is defined to be directly related to the vehicle yaw distance dist, and has a maximum value K. max The specific relationship between the minimum value of 1.2 and the minimum value is shown in the following formula:

[0059]

[0060] K max The value determines the gain coefficient of the output steering angle control quantity when the vehicle has a large yaw. Establish K... max Nonlinear relationship with vehicle speed:

[0061] K max =f(v)

[0062] Each wheel of the multi-axle distributed drive platform has driving capability. When the driving of some wheels fails, the platform drive system can be degraded for use. The drive state parameter s characterizes the drive state of each axle of the platform. Therefore, when the platform drive state parameter s changes, the differential steering effect is compensated by adjusting the vehicle wheelbase. The wheelbase L and the vehicle drive state s satisfy the following relationship:

[0063] L = f(s)

[0064] In the formula, the vehicle driving state s is a discrete value.

[0065] Wherein, the lateral displacement deviation y e and heading angle deviation It is used to characterize the deviation between the platform's current pose and the reference trajectory. The larger the value, the greater the degree of deviation from the reference trajectory.

[0066] In step 3, the speed constraint algorithm is used to ensure the vehicle maintains driving stability under various paths, establishing the road curvature ρ, the tracking system state S, and the speed constraint v. max The mapping relationship is as follows:

[0067] v max =min(v ρ v S )

[0068] v ρ =f(ρ)

[0069] v S =f(S)

[0070] In the formula, the road curvature ρ is the maximum speed at which the vehicle is allowed to travel without rolling over, and the tracking system state S is the tracking system state parameter.

[0071] The braking monitoring module is used to automatically trigger the braking system to perform braking actions when the platform detects fault information or abnormal status of the tracking system during autonomous tracking. The braking percentage is related to the platform's real-time vehicle speed and yaw rate.

[0072] The distributed drive platform steering control module receives the desired steering angle information from the autonomous tracking system, combines it with the vehicle's current speed information, autonomously outputs the mechanical steering angle of the platform in the current state, as well as the additional yaw torque of differential steering, and outputs the above two control quantities to the steering system and the drive system respectively.

[0073] In step 1, the complete reference trajectory is divided into segments according to a fixed number of trajectory points. Based on the storage space of the platform motion controller and the amount of reference trajectory data, the segmentation principle is that adjacent reference trajectory segments must contain at least a 50m distance between them for connection.

[0074] Segmented storage of the reference trajectory can solve the following problems: 1) The central controller has insufficient storage space due to the large dimension of the reference trajectory variables; 2) Extracting reference trajectory points with smaller dimensions can reduce the computational load of the trajectory tracking controller and improve the real-time control performance.

[0075] In step 1, specific segments of the reference trajectory are selected based on the vehicle positioning information, following the principle of proximity to the vehicle's location. The distance from each reference trajectory point to the vehicle's current location is calculated, and the trajectory point with the smallest distance is recorded as the nearest trajectory point. The reference trajectory segment is generally selected based on the segment containing the nearest trajectory point. If the nearest trajectory point is within the connecting area of ​​the current segment, the next adjacent reference trajectory segment is selected as the output reference trajectory.

[0076] Figure 3 (a) Describes the driving trajectory of the test vehicle using the improved autonomous tracking control method proposed in this paper. The starting point is located at 115.93°E, 40.367°N. This trajectory includes off-road undulating surfaces, paved surfaces, straight tracks, and curves, which can fully verify the applicability of the autonomous tracking control method under different working conditions. Among them, the straight sections with latitude values ​​less than 40.368° are off-road undulating surfaces; the curves and the straight sections with latitude values ​​greater than 40.368° are paved surfaces. The total track length is 2.65km.

[0077] Figure 3 (bc) shows the real-time speed and expected speed of the test vehicle during the tracking process. It can be seen that the speed change trend of the vehicle throughout the process is consistent with the change trend of the test road conditions.

[0078] Analyzing the test duration, the vehicle speed remained around 33 km / h for the first 40% of the test, with significant fluctuations in the actual speed curve. This is because the vehicle speed information was collected from the wheel speed feedback by the in-wheel motors, which use torque control. When the wheels move on undulating off-road terrain, they experience jumps and impacts, resulting in instantaneous changes in the actual output speed. Furthermore, the significant changes in driving resistance on off-road terrain also affect the vehicle speed.

[0079] The middle 10% of the time involves the vehicle automatically reducing its speed to approximately 18 km / h when approaching a curve. There is a short straight section in the middle of the curve, causing the expected speed to briefly rise to 24 km / h. The actual speed curve generally matches the trend of the expected speed, but there is an overshoot of approximately 2 km / h.

[0080] For the latter 50% of the test, the vehicle traveled on a straight concrete track with an expected speed of 42 km / h, which was largely consistent with the actual speed. Within this section, the expected speed briefly dipped to 25 km / h. This was due to a slight turn at a certain point on the straight concrete track during trajectory point acquisition, causing the planned speed to decrease before automatically recovering. Furthermore, within the 280-300 second timeframe, the expected speed initially dropped to 20 km / h before recovering to 42 km / h, while the actual speed dropped to zero. This was because the reference trajectory imported into the chassis controller was a complete trajectory; this experiment only tested a section of the track, and at the end of the experiment, cruise control was disabled on the remote control terminal, and braking intervention was applied.

[0081] Figure 3 (d) shows the variation curve of the desired steering angle output by the autonomous tracking control system. It can be seen that the desired steering angle output by the test vehicle changes relatively smoothly on a straight track, but its value changes drastically under off-road conditions, although the amplitude remains basically within the range of -5° to 5°. This is because the positioning system of the test vehicle is fixed at the highest position of the vehicle, and the vehicle tilts significantly to the left and right when driving on off-road terrain, resulting in a positioning accuracy that is about 10cm lower than the normal value, thus causing a significant change in the desired steering angle. When the vehicle is cornering, the desired steering angle reaches about 18°, while the absolute value of the desired steering angle on a concrete track is no greater than 2°. The variation curve shows that the desired steering angle exhibits oscillating changes. This is because the test vehicle did not undergo maintenance of the steering and suspension systems, and the steering wheels have a ±1° play when the vehicle is stationary.

[0082] Figure 3 (e) shows the tracking deviation during the autonomous tracking process of the experimental vehicle. It can be seen that the tracking deviation on straight off-road tracks and curves is no greater than ±40cm, and on straight concrete roads, it is no greater than ±20cm. Furthermore, the tracking deviation is not a continuously changing curve. This is because the tracking deviation in this experiment is defined as the distance from the vehicle's position to the nearest trajectory point, while the distance between adjacent reference trajectory points is generally 10cm. Therefore, the actual tracking deviation should be smaller than the measured value.

[0083] Figure 3 (f) shows the change curve of the gain coefficient in the improved pure tracking algorithm. The value of the gain coefficient is related to the tracking deviation and vehicle speed. It can be seen that the gain coefficient changes more significantly under off-road conditions, and its value is relatively large at the curve due to the influence of tracking deviation.

[0084] Figure 3 (g) To improve the change curve of the aiming distance parameter in the pure tracking algorithm, it can be seen that the change of aiming distance is basically consistent with the change of real-time vehicle speed.

[0085] Figure 3(h) represents the trajectory segment changes selected by the autonomous tracking control system in the chassis controller. The total number of complete trajectory points is 18256. If all of these points were stored in a single variable, the chassis controller would experience insufficient storage space. The autonomous tracking control method in this paper divides the trajectory according to a standard of 3000 trajectory points per segment and 900 trajectory points in connecting sections, resulting in a total of 9 trajectory segments. As shown in the figure, in a circular track, the autonomous tracking system can automatically connect adjacent trajectory segments, thus completing the autonomous tracking of the complete trajectory.

[0086] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A tracking control method for a multi-axis distributed drive drive-by-wire platform, characterized in that, Includes the following steps: Step 1: The trajectory preprocessing algorithm divides the complete reference trajectory obtained from real vehicle data collection or trajectory planning into segments according to a fixed number of trajectory points through offline processing. The segmented trajectory data is then stored in the trajectory storage unit by program writing. Step 2: The platform motion controller selects specific segments of the reference trajectory based on the vehicle positioning information, calculates the pre-aiming distance from the vehicle speed information, and determines the pre-aiming point P(X) based on the trajectory data of that segment, the vehicle pose information, and the pre-aiming distance. P ,Y P Simultaneously, the aiming deviation is obtained, including lateral displacement deviation. and heading angle deviation Among them, lateral displacement deviation The distance from the vehicle's current target trajectory point P to the vehicle's direction of travel, and the heading angle deviation. This is the difference between the heading angle corresponding to the aiming point P and the current vehicle heading angle; Step 3: The autonomous tracking algorithm outputs the steering angle control quantity and tracking system status based on the vehicle's pose information and pre-aiming deviation. The speed constraint algorithm outputs the speed constraint signal to the cruise control module based on the current road curvature and tracking system status. The internal braking monitoring module of the tracking system outputs the braking percentage signal to the platform's braking system based on the tracking system status and vehicle status signal. Step 4: The distributed drive platform steering control module uses the steering angle control input from the tracking system, combined with vehicle speed information, to allocate the ratio of mechanical steering and differential steering in real time, and sends the control signal to the actuator. Step 5: The platform's steering, drive, and braking systems execute corresponding control commands to complete motion control. The platform's motion state is fed back to each module of the autonomous tracking system to complete closed-loop control. Among them, the rotation control quantity output by the autonomous tracking algorithm The following relationship must be satisfied: in, This is the gain coefficient. This refers to the vehicle's wheelbase. Define the gain coefficient for the azimuth angle of the desired trajectory point relative to the vehicle. Vehicle deviation distance Directly related, and has a maximum value. The specific relationship between the minimum value of 1.2 and the minimum value is shown in the following formula: The value determines the gain coefficient of the output steering angle control quantity when the vehicle has a large yaw. Nonlinear relationship with vehicle speed: Each wheel of the multi-axis distributed drive-by-wire platform has driving capability. When the driving of some wheels fails, the platform's drive system can be degraded for use, and the drive status parameters... This characterizes the drive state of each axis of the platform, and therefore the platform drive state parameters When changes occur, the differential steering effect is compensated by adjusting the vehicle's wheelbase. With vehicle driving status The following relationship exists between them: In the formula, the vehicle driving state These are discrete values.

2. The multi-axis distributed drive wire-controlled platform tracking control method as described in claim 1, characterized in that, In step 1, the trajectory preprocessing algorithm generates the data size of a single trajectory segment and the number of segments contained in the complete trajectory based on the storage space of the platform motion controller and the amount of reference trajectory data.

3. The multi-axis distributed drive drive-by-wire platform tracking control method as described in claim 1, characterized in that, In step 2, the specific trajectory segment is selected by calculating the distance from all reference trajectory points to the vehicle's current position one by one, taking the trajectory point with the smallest distance as the nearest trajectory point, and selecting the reference trajectory segment where the nearest trajectory point is located as the specific segment of the reference trajectory.

4. The multi-axis distributed drive drive-by-wire platform tracking control method as described in claim 1, characterized in that, In step 2, the minimum pre-aiming distance is used. Real-time vehicle speed on the platform Calculate the aiming distance The calculation formula is as follows: 。 5. The multi-axis distributed drive drive-by-wire platform tracking control method as described in claim 3, characterized in that, The pre-aiming deviation is calculated by taking the distances from all trajectory points within the reference trajectory segment to the vehicle's current position, selecting the distance closest to the pre-aiming distance, and defining the trajectory point whose direction from the platform's current position to that point is an acute angle with the platform's heading as the pre-aiming point at the current moment. The lateral displacement deviation of the platform relative to the pre-aiming point at the current moment is then determined based on geometric positional relationships. , heading angle deviation .

6. The multi-axis distributed drive drive-by-wire platform tracking control method as described in claim 1, characterized in that, The lateral displacement deviation and heading angle deviation It is used to characterize the deviation between the platform's current pose and the reference trajectory. The larger the value, the greater the degree of deviation from the reference trajectory.

7. The multi-axis distributed drive drive-by-wire platform tracking control method as described in claim 1, characterized in that, In step 3, the speed constraint algorithm is used to ensure vehicle stability under various paths and to establish road curvature. Tracking system status to speed constraints The mapping relationship is as follows: In the formula, the road curvature The maximum speed at which the vehicle is allowed to travel without rolling over; the status of the traction system. These are the state parameters of the tracking system.

8. The multi-axis distributed drive drive-by-wire platform tracking control method as described in claim 1, characterized in that, The braking monitoring module is used to automatically trigger the braking system to perform braking actions when the platform detects fault information or abnormal status of the tracking system during autonomous tracking. The braking percentage is related to the platform's real-time vehicle speed and yaw rate.

9. The multi-axis distributed drive drive-by-wire platform tracking control method as described in claim 1, characterized in that, The distributed drive platform steering control module is used to receive the desired steering angle information from the autonomous tracking system, combine it with the vehicle's current speed information, autonomously output the mechanical steering angle of the platform in the current state, as well as the additional yaw torque of differential steering, and output the above two control quantities to the steering system and drive system respectively.

Images