CN110908284A - Transverse control method and system for automatically driving truck - Google Patents
Transverse control method and system for automatically driving truck Download PDFInfo
- Publication number
- CN110908284A CN110908284A CN201911240558.6A CN201911240558A CN110908284A CN 110908284 A CN110908284 A CN 110908284A CN 201911240558 A CN201911240558 A CN 201911240558A CN 110908284 A CN110908284 A CN 110908284A
- Authority
- CN
- China
- Prior art keywords
- output
- truck
- model
- adaptive
- controller
- 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.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention relates to a lateral control method of an automatic driving truck, which comprises the following steps: s100: receiving an input reference track, finding a track point closest to the truck and calculating a tracking error; s200: inputting the tracking error as a state variable of the linear quadratic regulator into an LQR closed-loop feedback controller to obtain the output of the linear quadratic regulator controller; inputting the reference track into a model reference adaptive controller to obtain the output of the model reference adaptive controller; s300: combining the output of the linear quadratic regulator controller and the output of the model reference adaptive controller to obtain a steering wheel instruction, and sending the steering wheel instruction to a line control system of the truck; s400: the steps S100-S300 are looped so that the output of the truck is consistent with the desired output. The invention also provides a lateral control system of the automatic driving truck. According to the invention, through LQR + MRAC, the robustness of a control algorithm can be greatly improved, so that the truck still has good stability under different loads and special working conditions.
Description
Technical Field
The invention belongs to the field of automatic driving of motor vehicles, and particularly relates to a transverse control method and a transverse control system for an automatic driving truck.
Background
The prior art truck is shown in figure 4, and the truck is generally divided into a tractor ① and a trailer ②, wherein ③ is a mass point of the tractor ① (center of rear axle of the truck) and ④ is a mass point of the trailer ② (center of rear axle of the trailer). generally, the weight of the full load of the truck is 20-25 times that of the common car, the length of the truck is 4-5 times that of the common car, and the structure of the truck is not a uniform whole (including the tractor and the trailer), so that the control of the truck on the truck is more complicated and more demanding than that of the car during road driving.
The truck travels in a lane and needs to keep the trailer ② inside the lane in addition to keeping the tractor ① inside and outside the lane, due to the fact that the weight of the truck is different between empty, half-loaded and full, the traveling speed is different, and if the truck is not properly operated during traveling, such as improper control of the steering wheel angle and the steering angular speed, the truck can be thrown and hung during the traveling of a straight lane (figure 5) and scraped during the traveling of an over-curved lane (figure 6).
In the advanced automatic driving of the truck, good transverse stability under normal working conditions such as lane keeping and the like is required to be provided, meanwhile, the safety under special working conditions such as transverse emergency obstacle avoidance is also very important, and the emergency obstacle avoidance causes the rollover and the drift of the truck, so that the death rate is very high. Even experienced truck drivers are difficult to control effectively in time, so that stable control of the truck under special working conditions is very important. Most of the traditional transverse control methods adopt a feedforward + preview method or PID to carry out experience parameter adjustment to control, influence of vehicle dynamics on control performance is not considered or is not considered completely, particularly great influence is brought to vehicle stability by large-amplitude change of rear suspension and load, robustness of a control system is not strong enough, and stable control of the vehicle is difficult to guarantee in emergency obstacle avoidance.
The present invention has been made in view of the above circumstances.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides a lateral control method for an autonomous truck, which is a lateral stability tracking algorithm based on an upstream track, and can not only have a good lane keeping function under different loads, but also improve the control stability during emergency obstacle avoidance, and reduce the risk of major traffic accidents such as rollover.
The technical scheme of the invention is as follows: a method of lateral control of an autonomous truck comprising the steps of:
s100: receiving an input reference track, finding a track point closest to the truck and calculating a tracking error;
s200: inputting the tracking error as a state variable of the linear quadratic regulator into an LQR closed-loop feedback controller to obtain the output of the linear quadratic regulator controller; inputting the reference track into a model reference adaptive controller to obtain the output of the model reference adaptive controller;
s300: combining the output of the linear quadratic regulator controller and the output of the model reference adaptive controller to obtain a steering wheel instruction, and sending the steering wheel instruction to a line control system of the truck;
s400: the steps S100-S300 are looped so that the output of the truck is consistent with the desired output.
Further, before the step S100, a step S000 is further included: initializing, loading a dynamic model, loading a weight matrix of a linear quadratic regulator, and loading a model reference self-adaptive reference model and self-adaptive parameters.
Further, in the step S200, a step of inputting the reference trajectory to the feedforward compensator to obtain a feedforward compensation output is further included; accordingly, in the step S300, a step of obtaining a steering wheel command by combining the linear quadratic regulator controller output, the model reference adaptive controller output, and the feedforward compensation output is further included.
Further, in step S300, a dead zone modification module is provided in the adaptive controller to avoid the risk of drift and divergence of the adaptive control algorithm.
Further, in step S300, before the steering wheel command is sent to the drive-by-wire system of the truck, the steering wheel command is filtered and limited.
The invention also provides a lateral control system for an autonomous truck, comprising the following control modules:
the initialization module is used for loading a linearized vehicle dynamics model, loading a weight matrix of a linear quadratic regulator, loading a model reference adaptive reference model and adaptive parameters;
the linear quadratic regulator is used for executing a linear quadratic control algorithm and calculating control output for optimizing the performance index according to the set performance index;
the model reference self-adaptive control module comprises a reference model and a self-adaptive law, calculates expected state output by taking a reference track as the input of the reference model, and carries out online feedback correction on the deviation amount of the expected state output and the actual state variable of the truck through the self-adaptive law;
and the feedforward compensation module is used for executing a feedforward compensation algorithm, and directly searching the corresponding output control quantity (calculating the feedforward compensation quantity on line according to the dynamic characteristic) by combining a feedforward compensation table which is measured and recorded in advance according to the size of the input quantity.
Further, the linear quadratic regulator includes an LQR closed loop feedback controller.
The invention has the advantages that: 1. the robustness of the control algorithm can be greatly improved through the LQR (linear quadratic regulation) + MRAC (model reference adaptive control), so that the truck still has good stability under different loads and special working conditions, the robustness is improved, meanwhile, the flow of algorithm design is simplified (corresponding to the front, different vehicle models do not need to be frequently designed), and the design efficiency is improved.
2. The consistency of transverse control can be enhanced through the MRAC, the same instruction is given to the vehicle under different working conditions, the vehicle can keep similar running state after passing through a transverse control system, and the riding comfort is improved.
Drawings
FIG. 1 is a schematic diagram of a lateral control method of an autonomous truck according to the present invention.
FIG. 2 is a flow chart of one embodiment of a method of lateral control of an autonomous truck in accordance with the present invention.
FIG. 3 is a schematic diagram of the lateral control system of an autonomous truck according to the present invention.
Fig. 4 is a schematic view of a prior art truck configuration.
Fig. 5 is a schematic diagram of the phenomenon of the prior art that the truck is thrown and hung during the driving process of a straight lane.
Fig. 6 is a schematic view of the prior art truck showing a scraping phenomenon during an overbending trip.
Detailed Description
A method and system for lateral control of an autonomous truck according to the present invention will now be further described with reference to the accompanying fig. 1-3.
As shown in fig. 1, a schematic diagram of a lateral control method of an autonomous truck according to the present invention is mainly divided into two parallel parts: 1. after a reference track input at the upstream is received, finding a track point closest to the truck and calculating a tracking error, and then designing the LQR closed-loop feedback controller by taking the tracking error as a state variable of a linear quadratic regulator to ensure that a designed system has a phase margin of 60 degrees for example and an amplitude margin of 12db for example, which is enough to ensure the stability of the system when the error is not large; 2. and determining a model reference adaptive reference model according to the design index of the LQR, then calculating an expected state output by taking a reference track as the input of the reference model, and carrying out online feedback correction on the deviation amount of the expected state output and the actual state variable of the truck through an adaptive law so that the output of the truck is consistent with the expected output.
When the change of the transverse track is large, the change of the track is compensated in advance by adding feedforward, and the rapidity of the system is improved.
In order to correct the defect that the adaptive algorithm is easy to oscillate, dead zone correction is added in the adaptive law, and the risks of drifting and divergence of the adaptive control algorithm are avoided.
Specifically, the invention relates to a lateral control method of an automatic driving truck, which comprises the following steps:
s100: receiving an input reference track, finding a track point closest to the truck and calculating a tracking error;
s200: inputting the tracking error as a state variable of the linear quadratic regulator into an LQR closed-loop feedback controller to obtain the output of the linear quadratic regulator controller; inputting the reference track into a model reference adaptive controller to obtain the output of the model reference adaptive controller;
s300: combining the output of the linear quadratic regulator controller and the output of the model reference adaptive controller to obtain a steering wheel instruction, and sending the steering wheel instruction to a line control system of the truck;
s400: the steps S100-S300 are looped so that the output of the truck is consistent with the desired output.
Before executing step S100, further comprising executing step S000: and (5) initializing. Loading a dynamic model, loading a weight matrix of a linear quadratic regulator, and loading a model reference self-adaptive reference model and self-adaptive parameters.
In order to correct the defect that the adaptive algorithm is easy to oscillate, a dead zone correction operation is added in the adaptive law. In step S300, a dead zone correction module is provided in the adaptive controller to avoid the risk of drift and divergence of the adaptive control algorithm.
The invention discloses a lateral control method of an automatic driving truck, which adopts three algorithms to calculate control quantity together, wherein the three algorithms are respectively a feedforward compensation algorithm, a linear quadratic algorithm and a model reference adaptive algorithm, and are respectively explained as follows.
1) Feedforward compensation algorithm
The feedforward compensation algorithm is realized by a feedforward compensation module, and the feedforward compensation module can directly search corresponding output control quantity (the feedforward input quantity is calculated on line according to dynamic characteristics) according to the size of the input quantity and a feedforward compensation table which is measured and recorded in advance, wherein the feedforward compensation module mainly is the transmission ratio of a steering wheel of the truck.
2) Linear quadratic form algorithm
The linear quadratic algorithm is implemented by a linear quadratic controller (LQR), which is one of the optimal controls, and calculates a control output that optimizes a performance index based on a set performance index.
Control law ub＝Kx,K＝-R-1BTP，
Wherein, B is an input matrix of the modeled system; r is a control component weight matrix; p by solving for PA + ATP+Q-PBR-1BTP + Q is obtained as 0(Riccati equation), where a is the modeled system state matrix, Q is the weight matrix of the state variables, and Q is an arbitrary positive definite matrix.
3) Model reference adaptive algorithm
The model reference adaptive algorithm is implemented by a model reference adaptive module comprising a reference model and an adaptation law.
3.1 reference model
The selection of the reference model is related to the parameters of the selected modeling and the expected system performance, and the state equation of the reference model needs to satisfy Aref＝A-BKx T,Cref＝C-DKx TWherein A is a state matrix of the modeled system, B is an input matrix of the modeled system, C is an output matrix of the modeled system, D is a direct transfer matrix of the modeled system, and Kx TIs a feedback matrix in a linear quadratic regulator.
3.2 law of adaptation
The self-adapting law can generate a control component according to the error between the output of the reference model and the output of the actual system, so that the output of the system and the output of the reference model tend to be consistent.
This is a reference formula for the adaptation law: u. ofd＝Ku Tub,Ku＝g∫ubemPB, wherein emFor the system output and the reference output error value, g is the adaptive speed, representing the speed at which the system follows the reference system, ubIs the control component of the linear quadratic regulator output, P is the system stability component, according to Aref TP+PArefAnd B is an input matrix when the vehicle is modeled.
The invention relates to a transverse control method of an automatic driving truck, which not only utilizes a Linear Quadratic Regulator (LQR) and a truck dynamic model with higher precision to carry out closed-loop feedback control to ensure that the control performance of the truck during normal driving reaches the design requirement, but also utilizes a multiple-input multiple-output (MIMO) model to refer to self-adaptation (MRAC), and further strengthens that the index performance requirement of the reference model design can be still met when the interference of road surface interference, load change and un-modeled uncertainty exists by designing the reference model and self-adaptation control law according to the expected index requirement, so that the transverse control of the truck can also keep the consistency of system response when the larger interference exists, and the robustness of the system is ensured.
Example 1
Fig. 2 is a flowchart of an embodiment of a lateral control method for an automatic drive truck according to the present invention, which specifically includes the following steps:
step S000: and (5) initializing the system. Loading a linearized vehicle dynamics model, loading a weight matrix of a linear quadratic regulator, and loading a model reference adaptive reference model, adaptive parameters and other control algorithm models.
The dynamic model of the vehicle is built in the form of a state equation as follows:
the parameters are truck parameters and can be measured and calculated.
The linear quadratic regulator weight matrices are all diagonal matrices, and the form is as follows:
the reference model is represented here by an equation of state, which decouples the model into two separate subsystems with respect to the lateral position of the lane and the yaw angle of the vehicle, where wyAs natural frequency of the lane transverse position system, KxyIs a lane railDamping coefficient, w, to the position systemyIs the natural frequency, K, of the yaw angle system of the vehiclexyThe damping coefficient of the vehicle yaw angle position system.
Step S100: receiving and processing the input reference track, finding the track point nearest to the truck, calculating and calculating the tracking error, wherein the tracking error is used as an error state variable e, namely
e＝r-y,em＝xrefY, where r is the input trace, y is the output trace, emError value, x, for system output trajectory and reference model outputrefIs the output trajectory of the reference model.
Step S200: calculating a linear quadratic regulator control law and a model reference adaptive control law according to a linear model of the truck, and performing dead zone correction on the output of the adaptive law; inputting the tracking error e serving as a state variable of the linear quadratic regulator into an LQR closed-loop feedback controller to obtain the output of the linear quadratic regulator controller; and inputting the reference track into the model reference adaptive controller to obtain the output of the model reference adaptive controller.
Linear quadratic regulator controller output: u. ofb＝Kx,K＝-R-1BTP，
Model reference adaptive controller output: u. ofd＝Ku Tub,Ku＝g∫ubemPB。
Step S300: and combining the output of the linear quadratic regulator controller, the output of the model reference adaptive controller and the output of the feedforward compensation table to obtain a steering wheel instruction, filtering and limiting the output steering wheel instruction, and then sending the steering wheel instruction to the truck drive-by-wire at a fixed frequency.
Step S400: the steps S100-S300 are looped so that the output of the truck is consistent with the desired output.
The invention also provides a system for implementing the lateral control method of the automatic driving truck, as shown in figure 3.
The algorithm adopted by the invention has strong redundant fault-tolerant capability, when the dynamics of the truck is close to the parameters of design modeling, the output of the truck can be consistent with the expectation through LQR feedback correction, at the moment, the LQR can ensure that the system has good performance, the error between the expected output of the MRAC reference model and the feedback state of the truck is very small, and the self-adaptation law hardly works. When the dynamics of the truck is greatly changed due to the fact that interference, hanging or loading is heavy on the road surface, the LQR is reduced in control quality due to the fact that the model is large in change, the error between the actual output and the expected output of the reference model is large under the condition, at the moment, the error is further adjusted on line through self-adaptive law feedback correction of the MRAC, the response of the truck approaches to the reference input, the robustness is improved, and meanwhile the consistency of the lateral control performance of the truck is further guaranteed. When the change of the transverse track is large, the change of the track is compensated in advance by adding feedforward, and the rapidity of the system is improved.
The above-described embodiments are intended to illustrate rather than to limit the invention, and any modifications and variations of the present invention are within the spirit of the invention and the scope of the claims.
Claims (8)
1. A method of lateral control of an autonomous truck, characterized by the steps of:
s100: receiving an input reference track, finding a track point closest to the truck and calculating a tracking error;
s200: inputting the tracking error as a state variable of the linear quadratic regulator into an LQR closed-loop feedback controller to obtain the output of the linear quadratic regulator controller; inputting the reference track into a model reference adaptive controller to obtain the output of the model reference adaptive controller;
s300: combining the output of the linear quadratic regulator controller and the output of the model reference adaptive controller to obtain a steering wheel instruction, and sending the steering wheel instruction to a line control system of the truck;
s400: the steps S100-S300 are looped so that the output of the truck is consistent with the desired output.
2. The lateral control method of an autonomous truck as claimed in claim 1, characterized in that before the step S100, it further comprises a step S000: initializing, loading a dynamic model, loading a weight matrix of a linear quadratic regulator, and loading a model reference self-adaptive reference model and self-adaptive parameters.
3. The lateral control method of an autonomous truck as set forth in claim 1, further comprising a step of inputting the reference trajectory to a feedforward compensator to obtain a feedforward compensated output in the step S200; accordingly, in the step S300, a step of obtaining a steering wheel command by combining the linear quadratic regulator controller output, the model reference adaptive controller output, and the feedforward compensation output is further included.
4. A method for lateral control of an autonomous truck as claimed in any of claims 1-3, characterized in that in step S300 there is a dead-zone correction module in the adaptive controller for dead-zone correction, avoiding the risk of drift and divergence of the adaptive control algorithm.
5. The lateral control method of an autonomous truck as claimed in any of claims 1-3, wherein the steering wheel command is filtered and limited before being sent to the drive-by-wire system of the truck in step S300.
6. The lateral control method of an autonomous truck as recited in claim 4, wherein the steering wheel command is filtered and limited before being issued to the drive-by-wire system of the truck in step S300.
7. A lateral control system for an autonomous truck, comprising the following control modules:
the initialization module is used for loading a linearized vehicle dynamics model, loading a weight matrix of a linear quadratic regulator, loading a model reference adaptive reference model and adaptive parameters;
the linear quadratic regulator is used for executing a linear quadratic control algorithm and calculating control output for optimizing the performance index according to the set performance index;
the model reference self-adaptive control module comprises a reference model and a self-adaptive law, calculates expected state output by taking a reference track as the input of the reference model, and carries out online feedback correction on the deviation amount of the expected state output and the actual state variable of the truck through the self-adaptive law;
and the feedforward compensation module is used for executing a feedforward compensation algorithm, and directly searching the corresponding output control quantity by combining a feedforward compensation table which is measured and recorded in advance according to the size of the input quantity.
8. The lateral control system of the autonomous truck of claim 7 wherein the linear quadratic regulator comprises an LQR closed loop feedback controller.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911240558.6A CN110908284A (en) | 2019-12-06 | 2019-12-06 | Transverse control method and system for automatically driving truck |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911240558.6A CN110908284A (en) | 2019-12-06 | 2019-12-06 | Transverse control method and system for automatically driving truck |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110908284A true CN110908284A (en) | 2020-03-24 |
Family
ID=69823069
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911240558.6A Pending CN110908284A (en) | 2019-12-06 | 2019-12-06 | Transverse control method and system for automatically driving truck |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110908284A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111638712A (en) * | 2020-05-26 | 2020-09-08 | 三一专用汽车有限责任公司 | Transverse motion control method and device for automatic driving vehicle and automatic driving vehicle |
| CN111731382A (en) * | 2020-06-30 | 2020-10-02 | 中国第一汽车股份有限公司 | Vehicle lateral control method, system, vehicle and storage medium |
| CN112622895A (en) * | 2020-12-30 | 2021-04-09 | 威伯科汽车控制系统（中国）有限公司 | Prediction control method applied to trajectory control of automatic driving |
| WO2021109554A1 (en) * | 2019-12-04 | 2021-06-10 | Suzhou Zhijia Science & Technologies Co., Ltd. | Longitudinal control system and method for autonomous vehicle based on feed forward control |
| CN112947474A (en) * | 2021-03-22 | 2021-06-11 | 中国第一汽车股份有限公司 | Method and device for adjusting transverse control parameters of automatic driving vehicle |
| CN113183957A (en) * | 2021-05-24 | 2021-07-30 | 前海七剑科技(深圳)有限公司 | Vehicle control method, device and equipment and automatic driving vehicle |
| CN114368380A (en) * | 2022-01-06 | 2022-04-19 | 上海宏景智驾信息科技有限公司 | Automatic driving semi-trailer truck transverse control method suitable for different loads |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107561942A (en) * | 2017-09-12 | 2018-01-09 | 重庆邮电大学 | Intelligent vehicle track following model predictive control method based on model compensation |
| CN108427417A (en) * | 2018-03-30 | 2018-08-21 | 北京图森未来科技有限公司 | Automatic driving control system and method, computer server and automatic driving vehicle |
| CN108845568A (en) * | 2018-04-27 | 2018-11-20 | 榛硕（武汉）智能科技有限公司 | Trajectory Tracking Control System and its control method for Vehicular automatic driving |
| CN108981684A (en) * | 2018-06-06 | 2018-12-11 | 苏州智加科技有限公司 | Container truck positioning system and method |
| CN109407677A (en) * | 2018-12-24 | 2019-03-01 | 清华大学 | The trace tracking method of automatic driving vehicle |
| CN110271534A (en) * | 2019-06-14 | 2019-09-24 | 百度在线网络技术（北京）有限公司 | Control method, device, computer equipment and the storage medium of automatic driving vehicle |
| CN110414082A (en) * | 2019-07-09 | 2019-11-05 | 武汉乐庭软件技术有限公司 | A kind of construction method of automatic Pilot decision and control associative simulation model |
| CN110471428A (en) * | 2019-09-18 | 2019-11-19 | 吉林大学 | A kind of path following method of change preview distance and constraint of velocity based on model |
-
2019
- 2019-12-06 CN CN201911240558.6A patent/CN110908284A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107561942A (en) * | 2017-09-12 | 2018-01-09 | 重庆邮电大学 | Intelligent vehicle track following model predictive control method based on model compensation |
| CN108427417A (en) * | 2018-03-30 | 2018-08-21 | 北京图森未来科技有限公司 | Automatic driving control system and method, computer server and automatic driving vehicle |
| CN108845568A (en) * | 2018-04-27 | 2018-11-20 | 榛硕（武汉）智能科技有限公司 | Trajectory Tracking Control System and its control method for Vehicular automatic driving |
| CN108981684A (en) * | 2018-06-06 | 2018-12-11 | 苏州智加科技有限公司 | Container truck positioning system and method |
| CN109407677A (en) * | 2018-12-24 | 2019-03-01 | 清华大学 | The trace tracking method of automatic driving vehicle |
| CN110271534A (en) * | 2019-06-14 | 2019-09-24 | 百度在线网络技术（北京）有限公司 | Control method, device, computer equipment and the storage medium of automatic driving vehicle |
| CN110414082A (en) * | 2019-07-09 | 2019-11-05 | 武汉乐庭软件技术有限公司 | A kind of construction method of automatic Pilot decision and control associative simulation model |
| CN110471428A (en) * | 2019-09-18 | 2019-11-19 | 吉林大学 | A kind of path following method of change preview distance and constraint of velocity based on model |
Non-Patent Citations (1)
| Title |
|---|
| 陶冰冰: "自动驾驶车辆LQR轨迹跟踪控制器设计", 《湖北汽车工业学院学报》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021109554A1 (en) * | 2019-12-04 | 2021-06-10 | Suzhou Zhijia Science & Technologies Co., Ltd. | Longitudinal control system and method for autonomous vehicle based on feed forward control |
| CN111638712A (en) * | 2020-05-26 | 2020-09-08 | 三一专用汽车有限责任公司 | Transverse motion control method and device for automatic driving vehicle and automatic driving vehicle |
| CN111731382A (en) * | 2020-06-30 | 2020-10-02 | 中国第一汽车股份有限公司 | Vehicle lateral control method, system, vehicle and storage medium |
| CN111731382B (en) * | 2020-06-30 | 2021-07-27 | 中国第一汽车股份有限公司 | Vehicle lateral control method, system, vehicle and storage medium |
| CN112622895A (en) * | 2020-12-30 | 2021-04-09 | 威伯科汽车控制系统（中国）有限公司 | Prediction control method applied to trajectory control of automatic driving |
| CN112947474A (en) * | 2021-03-22 | 2021-06-11 | 中国第一汽车股份有限公司 | Method and device for adjusting transverse control parameters of automatic driving vehicle |
| CN113183957A (en) * | 2021-05-24 | 2021-07-30 | 前海七剑科技(深圳)有限公司 | Vehicle control method, device and equipment and automatic driving vehicle |
| CN114368380A (en) * | 2022-01-06 | 2022-04-19 | 上海宏景智驾信息科技有限公司 | Automatic driving semi-trailer truck transverse control method suitable for different loads |
| CN114368380B (en) * | 2022-01-06 | 2023-02-17 | 上海宏景智驾信息科技有限公司 | Transverse control method for automatic driving semi-trailer truck adapting to different loads |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110908284A (en) | Transverse control method and system for automatically driving truck | |
| CN107992681B (en) | Composite control method for active front wheel steering system of electric automobile | |
| CN112519882B (en) | Vehicle reference track tracking method and system | |
| CN111688704B (en) | Man-machine torque cooperative control method based on driving state prediction | |
| CN111025907A (en) | Transverse control method and system for automatic driving truck and storage medium | |
| CN109050658B (en) | Model predictive control-based automobile active front wheel steering self-adaptive adjustment method | |
| CN110588633B (en) | Path tracking and stability control method for intelligent automobile under limit working condition | |
| CN108001447A (en) | A kind of intelligent vehicle path trace front wheel angle compensating control method | |
| CN111923908A (en) | Stability-fused intelligent automobile path tracking control method | |
| CN111679575B (en) | Intelligent automobile trajectory tracking controller based on robust model predictive control and construction method thereof | |
| JP7118517B2 (en) | Methods and devices for implementing closed-loop advanced driver assistance devices | |
| CN113183957A (en) | Vehicle control method, device and equipment and automatic driving vehicle | |
| CN112622895B (en) | Prediction control method applied to trajectory control of automatic driving | |
| CN110008600A (en) | The design method of vehicle stability controller performance conservative | |
| CN111142534B (en) | Intelligent vehicle transverse and longitudinal comprehensive track tracking method and control system | |
| CN113978450A (en) | Anti-heeling path tracking game control method for commercial vehicle | |
| CN110046417A (en) | Improve the controller compensation method of driver's response delay | |
| WO2023241050A1 (en) | Vehicle lateral control method and system, and vehicle | |
| CN111580513A (en) | Deviation correction control method for heavy-load AGV | |
| CN114137825B (en) | Motion control method, system and device for heavy-load double-steering-wheel AGV body | |
| CN115257808A (en) | Vehicle longitudinal control method and device, electronic equipment and storage medium | |
| CN115167135A (en) | Feedback and model feedforward cascade unmanned vehicle self-tendency optimal position and posture control system | |
| CN114802202A (en) | Vehicle stability control method based on Lyapunov stability theory | |
| Chokor et al. | Design of a new gain-scheduled LPV/ℋ∞ controller for vehicle’s global chassis control | |
| Lu et al. | Adaptive terminal sliding mode trajectory tracking control with fixed‐time prescribed performance considering rollover stability of autonomous vehicles |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200324 |