CN-122018497-A - Heavy-duty vehicle track tracking control method, device, equipment and medium

CN122018497ACN 122018497 ACN122018497 ACN 122018497ACN-122018497-A

Abstract

The embodiment of the disclosure provides a heavy-duty vehicle track tracking control method, device, equipment and medium, which can establish a vehicle transverse dynamics model capable of describing vehicle dynamic characteristics and a track tracking error model considering uncertainty according to vehicle state data and reference track data to output a state quantity of a target heavy-duty vehicle containing error information, acquire a closed loop control system feedback gain, a minimum robust invariant set and a state quantity tolerance set and a control quantity tolerance set of a nominal system of the target heavy-duty vehicle obtained through offline calculation, output a nominal control quantity of the target heavy-duty vehicle according to the state quantity and the reference track data under the constraint of the minimum robust invariant set, the state quantity tolerance set and the control quantity tolerance set, and generate an actual control quantity of the target heavy-duty vehicle based on the feedback gain and the nominal control quantity so as to enable the target heavy-duty vehicle to run along the reference track data, thereby improving the running reliability of the heavy-duty vehicle on an unstructured road.

Inventors

GU YUQI
LI JUNQIU
YANG YONGXI
SUN YIFAN
XIONG WEI

Assignees

北京理工大学

Dates

Publication Date: 20260512
Application Date: 20251020

Claims (10)

1. A heavy-duty vehicle trajectory tracking control method, characterized by comprising: acquiring vehicle state data of a target heavy-duty vehicle and reference track data of the target heavy-duty vehicle; Establishing a vehicle transverse dynamics model capable of describing vehicle dynamic characteristics and a track tracking error model considering uncertainty according to the vehicle state data and the reference track data, and outputting the state quantity of the target heavy-load vehicle containing error information through the vehicle transverse dynamics model and the track tracking error model; Acquiring a feedback gain, a minimum robust invariant set and a state quantity tolerance set and a control quantity tolerance set of a nominal system of a target heavy-duty vehicle, which are obtained through offline calculation; Outputting a nominal control quantity of the target heavy-duty vehicle according to the state quantity and the reference track data under the constraint of the minimum robust invariant set, the state quantity allowable set and the control quantity allowable set, and generating an actual control quantity of the target heavy-duty vehicle based on the feedback gain and the nominal control quantity; and controlling the target heavy-duty vehicle based on the actual control quantity so that the target heavy-duty vehicle runs along the reference track data.
2. The heavy-duty vehicle trajectory tracking control method according to claim 1, characterized in that said creating a vehicle transverse dynamics model capable of describing vehicle dynamics and a trajectory tracking error model considering uncertainty from said vehicle state data and said reference trajectory data, outputting a state quantity of said target heavy-duty vehicle containing error information through said vehicle transverse dynamics model and said trajectory tracking error model, comprises: establishing a vehicle transverse dynamics model capable of describing vehicle dynamic characteristics based on the vehicle state data, and outputting a centroid side deviation angle and a yaw angle through the vehicle transverse dynamics model; And establishing a track tracking error model considering uncertainty based on the centroid slip angle, the yaw angle and the reference track data, and outputting the state quantity of the target heavy-duty vehicle containing error information through the track tracking error model.
3. The heavy-duty vehicle trajectory tracking control method of claim 2, wherein the vehicle state data includes a front and rear tire nominal cornering stiffness of the target heavy-duty vehicle, a front and rear axle to center of mass distance, a front wheel turning angle, a vehicle mass, a vehicle longitudinal speed, a direct yaw moment, a rotational inertia of a vehicle body about an axis; The building a vehicle transverse dynamics model capable of describing vehicle dynamic characteristics based on the vehicle state data, and outputting a centroid slip angle and a yaw angle through the vehicle transverse dynamics model comprises the following steps: Based on the nominal cornering stiffness of the front and rear tires, the distances from the front and rear axles to the centroid, the front wheel rotation angle, the whole vehicle mass, the vehicle longitudinal speed, the direct yaw moment and the rotational inertia of the vehicle body around the axle, a vehicle transverse dynamics model capable of describing the vehicle dynamic characteristics is established; And outputting a centroid slip angle and a yaw angle through the vehicle transverse dynamics model.
4. The heavy-duty vehicle trajectory tracking control method according to claim 2, characterized in that the establishing a trajectory tracking error model taking uncertainty into consideration based on the centroid slip angle, the yaw angle, and the reference trajectory data, and outputting a state quantity of the target heavy-duty vehicle including error information through the trajectory tracking error model, includes: determining a lateral error and a heading angle error of the target heavy-duty vehicle; Establishing a track tracking error model considering uncertainty based on the centroid slip angle, the yaw angle, the transverse error, the course angle error, the system control quantity of the target heavy-duty vehicle, the measurable curvature disturbance indicated by the reference track data, the system state matrix change quantity caused by uncertainty and the non-measurable disturbance caused by uncertainty; And outputting the state quantity of the target heavy-duty vehicle containing error information through the track tracking error model.
5. The heavy-duty vehicle trajectory tracking control method according to claim 1, characterized in that the minimum robust invariant set is obtained by: Model predictive control algorithm based on robust invariant set, aiming at the system of the target heavy-duty vehicle, adopting polyhedron invariant set to process asymmetric interference and linear inequality constraint so as to keep the theoretical track of the whole vehicle within an expected limit, and obtaining the minimum robust invariant set; the model predictive control algorithm based on the robust invariant set adopts the model predictive control algorithm based on the robust invariant set of the support function.
6. The heavy-duty vehicle trajectory tracking control method according to claim 1, characterized in that the feedback gain is obtained by: In the off-line calculation process, designing an error system cost function in an infinite time domain according to the dynamic characteristics of the heavy-load vehicle of the target heavy-load vehicle; And solving the cost function of the error system in the infinite time domain through a Li-Carl equation to obtain the feedback gain of the closed-loop control system of the target heavy-duty vehicle.
7. The heavy-duty vehicle trajectory tracking control method according to claim 1, wherein said outputting the nominal control amount of the target heavy-duty vehicle according to the state amount and the reference trajectory data includes: determining a nominal system output tracking error and a control input from the state quantity and the reference trajectory data; And outputting a nominal control sequence in a prediction time domain by taking the minimum of the output tracking error of the nominal system and the control input weight as targets and using the nominal control sequence as the nominal control quantity of the target heavy-duty vehicle through a preset design function.
8. A heavy-duty vehicle trajectory tracking control device, characterized by comprising: the data acquisition module is used for acquiring vehicle state data of the target heavy-duty vehicle and reference track data of the target heavy-duty vehicle; the model construction module is used for establishing a vehicle transverse dynamics model capable of describing vehicle dynamic characteristics and a track tracking error model considering uncertainty according to the vehicle state data and the reference track data, and outputting the state quantity of the target heavy-load vehicle containing error information through the vehicle transverse dynamics model and the track tracking error model; The off-line processing module is used for acquiring the feedback gain, the minimum robust invariant set and the state quantity tolerance set and the control quantity tolerance set of the nominal system of the target heavy-duty vehicle, which are obtained through off-line calculation; The on-line processing module is used for outputting the nominal control quantity of the target heavy-duty vehicle according to the state quantity and the reference track data under the constraint of the minimum robust invariant set, the state quantity allowable set and the control quantity allowable set, and generating the actual control quantity of the target heavy-duty vehicle based on the feedback gain and the nominal control quantity; And the tracking control module is used for controlling the target heavy-duty vehicle based on the actual control quantity so as to enable the target heavy-duty vehicle to run along the reference track data.
9. An electronic device comprising a memory storing a computer program and a processor that when executing the computer program implements the heavy-duty vehicle trajectory tracking control method of any one of claims 1 to 8.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the heavy-duty vehicle trajectory tracking control method of any one of claims 1 to 8.

Description

Heavy-duty vehicle track tracking control method, device, equipment and medium Technical Field The disclosure relates to the technical field of vehicle control processing, in particular to a heavy-duty vehicle track tracking control method, device, equipment and medium. Background For the intelligent heavy-duty vehicle running under the unstructured road, the grip force and stability of the vehicle are reduced due to the topography such as pothole roads and soft soil, in addition, the load of the heavy-duty vehicle is continuously changed due to the carrying requirement of the heavy-duty vehicle, the dynamic characteristics of the vehicle are also changed along with the load, and the fact that the vehicle state parameters are difficult to accurately obtain becomes a key factor for restricting the formation of a safe and reliable control strategy. In the related art, as the curvature of the unstructured road and the attachment of the road surface are complex and changeable, the disturbance factors of uncertainty are more and more, and the instability risk exists in the process of following the curve and avoiding the obstacle, the heavy-duty vehicle is difficult to effectively run on the unstructured road based on the preset reference track, and the running reliability of the heavy-duty vehicle on the unstructured road is reduced. Disclosure of Invention The embodiment of the disclosure mainly aims to provide a heavy-duty vehicle track tracking control method, device, equipment and medium, which can improve the running reliability of a heavy-duty vehicle on an unstructured road. To achieve the above object, a first aspect of an embodiment of the present disclosure provides a heavy-duty vehicle track tracking control method, including: acquiring vehicle state data of a target heavy-duty vehicle and reference track data of the target heavy-duty vehicle; Establishing a vehicle transverse dynamics model capable of describing vehicle dynamic characteristics and a track tracking error model considering uncertainty according to the vehicle state data and the reference track data, and outputting the state quantity of the target heavy-load vehicle containing error information through the vehicle transverse dynamics model and the track tracking error model; Acquiring a feedback gain, a minimum robust invariant set and a state quantity tolerance set and a control quantity tolerance set of a nominal system of a target heavy-duty vehicle, which are obtained through offline calculation; Outputting a nominal control quantity of the target heavy-duty vehicle according to the state quantity and the reference track data under the constraint of the minimum robust invariant set, the state quantity allowable set and the control quantity allowable set, and generating an actual control quantity of the target heavy-duty vehicle based on the feedback gain and the nominal control quantity; and controlling the target heavy-duty vehicle based on the actual control quantity so that the target heavy-duty vehicle runs along the reference track data. In some embodiments, the establishing a vehicle transverse dynamics model capable of describing vehicle dynamics and a track tracking error model considering uncertainty according to the vehicle state data and the reference track data, and outputting the state quantity of the target heavy-load vehicle including error information through the vehicle transverse dynamics model and the track tracking error model includes: establishing a vehicle transverse dynamics model capable of describing vehicle dynamic characteristics based on the vehicle state data, and outputting a centroid side deviation angle and a yaw angle through the vehicle transverse dynamics model; And establishing a track tracking error model considering uncertainty based on the centroid slip angle, the yaw angle and the reference track data, and outputting the state quantity of the target heavy-duty vehicle containing error information through the track tracking error model. In some embodiments, the vehicle state data includes a front and rear tire nominal cornering stiffness, a front axle and rear axle to center of mass distance, a front wheel turn angle, a vehicle mass, a vehicle longitudinal speed, a direct yaw moment, a rotational inertia of the vehicle body about the axle, of the target heavy-duty vehicle; The building a vehicle transverse dynamics model capable of describing vehicle dynamic characteristics based on the vehicle state data, and outputting a centroid slip angle and a yaw angle through the vehicle transverse dynamics model comprises the following steps: Based on the nominal cornering stiffness of the front and rear tires, the distances from the front and rear axles to the centroid, the front wheel rotation angle, the whole vehicle mass, the vehicle longitudinal speed, the direct yaw moment and the rotational inertia of the vehicle body around the axle, a vehicle transverse dynamics model capable of describing the