CN111114535B - Intelligent driving vehicle transverse control method and control system - Google Patents

Intelligent driving vehicle transverse control method and control system Download PDF

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CN111114535B
CN111114535B CN202010001284.1A CN202010001284A CN111114535B CN 111114535 B CN111114535 B CN 111114535B CN 202010001284 A CN202010001284 A CN 202010001284A CN 111114535 B CN111114535 B CN 111114535B
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control system
simulation model
torque calibration
calibration simulation
torque
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CN111114535A (en
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陈英箭
栗工
郭嘉
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DIAS Automotive Electronic Systems Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/02—Control of vehicle driving stability
    • B60W30/025—Control of vehicle driving stability related to comfort of drivers or passengers
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/20—Conjoint control of vehicle sub-units of different type or different function including control of steering systems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0002—Automatic control, details of type of controller or control system architecture
    • B60W2050/0008—Feedback, closed loop systems or details of feedback error signal
    • B60W2050/0011—Proportional Integral Differential [PID] controller
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0022—Gains, weighting coefficients or weighting functions

Abstract

The invention discloses a transverse control method for an intelligent driving vehicle, which comprises the following steps: establishing a transverse control feedforward control system model, a feedback control system model, a feedforward control torque calibration simulation model and a feedback control torque calibration simulation model; carrying out first parameter adjustment on the feedforward control and feedback control torque calibration simulation models; carrying out real vehicle verification on the feedforward control and feedback control torque calibration simulation model parameters obtained by first parameter adjustment; changing different expected working conditions to repeatedly execute the verification step, calibrating the parameters of the feedforward control and feedback control torque calibration simulation models under different expected working conditions to obtain the optimal parameters of the feedforward control and feedback control torque calibration simulation models; and optimizing the parameters of the feedforward control torque calibration simulation model and the feedback control torque calibration simulation model by using the optimal parameters, and performing simulation debugging on the residual uncalibrated parameters to obtain all calibration parameters. The invention also discloses a transverse control system of the intelligent driving vehicle.

Description

Intelligent driving vehicle transverse control method and control system
Technical Field
The invention relates to the field of automobiles, in particular to a transverse control method for an intelligent driving vehicle. The invention also relates to a transverse control system of the intelligent driving vehicle.
Background
The intelligent driving technology is a branch of the artificial intelligence field which develops rapidly, and the intelligent networking has become the strategic highest point of various host factories at home and abroad. Accurate lateral control is the basis for realizing the functional algorithm of the intelligent driving vehicle. However, steer-by-wire technology is not mature, and there are obstacles in policy. Until now, the mass-produced steer-by-wire vehicle has only one model of Enfenidi Q50, and the vehicle can be recalled in a large amount due to the steer-by-wire problem within one year of the market.
Currently, there are two general categories of lateral control refitting of intelligent vehicles:
in the first category, an original power-assisted steering control system is used, the power-assisted steering system is a torque control system, and the relation between the steering wheel torque and the front wheel rotation angle can be established by using a formula (1) through testing and estimation of part parameters of a vehicle and combining inherent parameters of the power-assisted steering system.
Figure BDA0002353590980000011
Wherein the content of the first and second substances,
m is the mass of the whole vehicle;
lHdistance from the total center of gravity to the rear axle;
nvthe total drag distance of the front wheel;
ilis the steering wheel to tire gear ratio;
l is the wheelbase;
Vlis the steering assist coefficient;
v is the vehicle speed;
Vchis the vehicle characteristic speed.
The method can realize the transverse control of the intelligent driving vehicle by simple measurement and calibration without changing the structure of the original steering system. However, in the formula (1), the parameter VlFor the power-assisted steering coefficient, the parameter is the core technical parameter of a power-assisted steering supplier, and the parameter is difficult to obtain by a general host factory or an intelligent driving initial company, a college and the like, so the method is only suitable for manufacturers and mechanisms which comprise a power-assisted steering system in a product line or conditionally obtain the power-assisted steering core parameter.
In the second category, a power steering system is eliminated, and a third-party control mechanism is installed outside the system to control the steering wheel. Typically, a servo motor is mounted near the steering wheel and is used to respond to steering angle commands from the intelligent driving decision planning module. The method abandons the original power-assisted steering mechanism and can relatively easily realize the transverse control of the vehicle. However, the installation space required by the transverse control system reconstructed by the method is large, so that the reconstructed vehicle is not beautiful, the precision is low under all working conditions, and the cost for redeveloping a set of control system based on the servo motor is high, and the mass production is difficult to realize.
Disclosure of Invention
The invention aims to provide an intelligent driving vehicle transverse control method based on combined action of feedforward and feedback under the condition of not needing to obtain core parameters of a power steering system on the basis of not changing an original power steering control system of a vehicle.
The invention also provides an intelligent driving vehicle transverse control system based on the combined action of feedforward and feedback under the condition of not needing to obtain the core parameters of the power-assisted steering system on the basis of not changing the original power-assisted steering control system of the vehicle.
Feedforward, that is, through active disturbance rejection control, estimates, compensates and suppresses uncertainty of the power steering system through nonlinear feedback on the basis of not depending on a power steering system model, so as to prevent the steering wheel from shaking when passing through a special working condition, such as a pothole or a deceleration strip.
The feedback is to obtain a PID parameter table relative to the expected steering wheel angle and the vehicle speed by calibrating PID controller parameters of the ideal torque under different expected steering wheel angles and vehicle speeds, obtain the expected torque output in a certain period by a table look-up mode, and correspondingly output the expected steering wheel angle by an intelligent driving vehicle decision and planning module.
And (3) automobile transverse control, namely adjusting the pose (transverse deviation and heading deviation) of the vehicle relative to the driving route. In the driving process of the automobile, transverse control is of great importance, the intelligent automobile is enabled to track and set a route to drive by controlling the corner of the front wheel of the intelligent automobile, and the safety, the comfort and the stability of driving are guaranteed.
In order to solve the technical problem, the invention provides a transverse control method for a drivable vehicle, which comprises the following steps:
s1, establishing a torque calibration simulation model of the feedforward control system according to the vehicle power-assisted steering characteristic;
the vehicle power-assisted steering characteristic is the relationship among the speed, the steering wheel angle and the steering wheel power-assisted torque obtained by calibrating the steering wheel angles of different steering wheels under the condition of preset speed;
s2, establishing a torque calibration simulation model of the feedback control system according to the vehicle power-assisted steering characteristic;
s3, adjusting parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system according to parameter adjustment rules by setting an expected working condition;
s4, carrying out real vehicle verification on the parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system after parameter adjustment to obtain the optimal parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system, and using the optimal parameters as the parameters of the simulation model of the transverse control feedforward control system and the transverse control feedback control system;
s5, changing different expected working conditions, repeatedly executing the steps S3 and S4, and obtaining a feedforward control system torque calibration simulation model and a feedback control system torque calibration simulation model parameter forming model parameter set under different expected working conditions; with many parameters calibrated, the iterative execution of steps S3 and S4 makes the simulation model more accurate, with other parameters being calibrated with a relatively more accurate simulation model.
And S6, integrating the model parameter set into a torque calibration simulation model of the feedforward control system and a torque calibration simulation model of the feedback control system, and controlling the torques of the feedforward control system and the feedback control system.
Optionally, the method for controlling the transverse direction of the intelligent driving vehicle is further improved, and a torque calibration simulation model of the feedforward control system is as follows;
Figure BDA0002353590980000031
Figure BDA0002353590980000041
wherein z is1、z2、z3For different state observers,/1、l2、l3Is the observer gain coefficient; θ is the desired steering wheel angle; j. the design is a squaremEquivalent moment of inertia for the steering system; mrTo the steering wheel torque, δ is a set threshold and ε is the steering gear ratio.
A state observer is a type of dynamic system that derives state variable estimates from measured values of external variables (input variables and output variables) of the system, also known as a state reconstructor. In the early 60 s, in order to realize state feedback or other requirements on a control system, D.G. Luenberg, R.W. Pasteur, J.E. Berberland and the like propose the concept and the construction method of a state observer, and solve the problem that the state cannot be directly measured through a reconstruction way. The advent of the state observer not only offers practical possibilities for the technical implementation of state feedback, but also finds practical application in many aspects of control engineering, such as replicating disturbances to achieve complete compensation for disturbances, etc.
Optionally, the method for controlling the transverse direction of the intelligent driving vehicle is further improved, and a torque calibration simulation model of the feedback control system is as follows;
Figure BDA0002353590980000042
wherein u (x) is an output torque function, e (t) is a steering wheel angle error function, Kp, Ki and Kd are respectively proportional, integral and differential coefficients (Kp, Ki and Kd are obtained by calibration),
Figure BDA0002353590980000043
is the derivative of the error function with respect to time and dt is the system change period.
Optionally, the method for controlling the lateral direction of the intelligent driving vehicle is further improved, and the parameter adjusting rule comprises that the state of the observer and the error of the first derivative and the second derivative relative to the expected steering wheel angle are smaller than the PID value of a preset order of magnitude;
wherein the predetermined order of magnitude is 10-4。
Optionally, the method for controlling the transverse direction of the intelligent driving vehicle is further improved, and when step S4 is implemented, the minimum tracking error of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system is used as the optimal parameter of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system
Optionally, the method for controlling the transverse direction of the intelligent driving vehicle is further improved, when step S4 is implemented, the actual vehicle verification of the optimal parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system is performed in a cross manner with the model simulation process, and the simulation model parameters under different set expected conditions are calibrated while the simulation model is optimized. Wherein the optimal parameters can be directly substituted into the simulation model.
Optionally, the method for controlling the lateral direction of the intelligent driving vehicle is further improved, and when the step S4 is implemented to perform vehicle verification, the method includes the following steps:
1) inputting the adjustment coefficient obtained by simulation and the set expected working condition into a real vehicle online calibration algorithm;
2) and controlling the running of the whole vehicle through CAN communication of the whole vehicle to obtain real vehicle verification parameters.
Optionally, the method for controlling the lateral direction of the intelligent driving vehicle is further improved, and the adjusting coefficient comprises a proportional coefficient, an integral coefficient and a differential coefficient.
Alternatively, the method for controlling the lateral direction of the intelligent driving vehicle is further improved, and in step S3, the expected working condition comprises the expected steering wheel angle and the expected vehicle speed.
The invention provides a transverse control system of an intelligent driving vehicle, which comprises:
the modeling module is used for establishing a torque calibration simulation model of a feedforward control system and a torque calibration simulation model of a feedback control system according to the vehicle power-assisted steering characteristic, wherein the vehicle power-assisted steering characteristic is the relationship among the speed, the steering wheel angle and the steering wheel power-assisted torque obtained by calibrating different steering wheel angles under the condition of preset speed;
the parameter adjusting module adjusts parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system according to parameter adjusting rules by setting an expected working condition;
the verification module is used for guiding the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system after parameter adjustment into a real vehicle, performing real vehicle verification to obtain optimal parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system, and using the optimal parameters as parameters of the simulation model of the transverse control feedforward control system and the transverse control feedback control system;
the control module is used for converting different expected working conditions, guiding the different expected working conditions into the control parameter adjusting module and the verification module, obtaining parameters of a feedforward control system torque calibration simulation model and a feedback control system torque calibration simulation model under different expected working conditions to form model parameter sets, and integrating the model parameter sets into the feedforward control system torque calibration simulation model and the feedback control system torque calibration simulation model for torque control of the feedforward control system and the feedback control system.
Optionally, the intelligent driving vehicle transverse control system is further improved, and the modeling module establishes a feedforward control system torque calibration simulation model as follows;
Figure BDA0002353590980000061
Figure BDA0002353590980000062
wherein z is1、z2、z3Different state observers; l1、l2、l3Is the observer gain coefficient; θ is the desired steering wheel angle; j. the design is a squaremEquivalent moment of inertia for the steering system; mrTo the steering wheel torque, δ is a set threshold and ε is the steering gear ratio.
Optionally, the transverse control system of the intelligent driving vehicle is further improved, and a modeling module establishes a torque calibration simulation model of the feedback control system as follows;
Figure BDA0002353590980000063
wherein u (x) is an output torque function, e (t) is a steering wheel angle error function, Kp, Ki and Kd are respectively proportional, integral and differential coefficients (Kp, Ki and Kd are obtained by calibration),
Figure BDA0002353590980000064
is the derivative of the error function with respect to time and dt is the system change period.
Optionally, the intelligent driving vehicle lateral control system is further improved, and the parameter adjusting rule of the parameter adjusting module comprises a PID value which enables the state of the observer and the error of the first derivative and the second derivative relative to the expected steering wheel angle to be smaller than a preset order of magnitude;
optionally, the intelligent driving vehicle transverse control system is further improved, and the preset order of magnitude is 10-4。
Optionally, the intelligent driving vehicle transverse control system is further improved, and the verification module takes the minimum tracking error of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system as the optimal parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system.
Optionally, the intelligent driving vehicle transverse control system is further improved, the control module enables the actual vehicle verification of the optimal parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system to be performed in a crossed manner with the model simulation process, and the simulation model parameters under different set expected working conditions are calibrated while the simulation model is optimized.
Optionally, the intelligent driving vehicle transverse control system is further improved, when the verification module performs real vehicle verification, the adjustment coefficient obtained by the simulation model and the set expected working condition are input into a real vehicle online calibration algorithm, and the whole vehicle is controlled to run through whole vehicle CAN communication to obtain real vehicle verification parameters.
Optionally, the intelligent driving vehicle lateral control system is further improved, and the adjusting coefficient comprises a proportional coefficient, an integral coefficient and a differential coefficient.
Optionally, the intelligent driving vehicle lateral control system is further improved, and the expected working condition comprises expected steering wheel rotation angle and expected vehicle speed.
The invention adopts feedforward and feedback combined control, and reduces the requirement on the precision of a feedforward control model due to the addition of feedback control (PID control related to an actual vehicle) from the perspective of feedforward control, and can correct the disturbance of an interference signal which is not measured. From the perspective of feedback, the feedforward control is added, and the accuracy of the whole control system can be improved. Therefore, the method can get rid of the constraint of unknown core parameters of the power-assisted steering system on the intelligent driving control system, and plays a positive role in promoting the development of the intelligent driving industry. The invention can improve the transverse control stability of the intelligent vehicle, obtain good driving experience and provide a good verification platform for the decision and planning algorithm of the intelligent vehicle. In addition, the invention can continue to use the power-assisted steering system of the whole vehicle, reduce the refitting cost of the intelligent driving vehicle, and can carry out parameter calibration by combining a real vehicle and a simulation parallel mode, thereby shortening at least 50% of the calibration period.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention, are incorporated in and constitute a part of this specification. The drawings are not necessarily to scale, however, and may not be intended to accurately reflect the precise structural or performance characteristics of any given embodiment, and should not be construed as limiting or restricting the scope of values or properties encompassed by exemplary embodiments in accordance with the invention. The invention will be described in further detail with reference to the following detailed description and accompanying drawings:
FIG. 1 is a schematic diagram of the feed forward and feed back control flow of the power steering system of the present invention.
Fig. 2 is a schematic diagram of a first diagram of an actual vehicle verifying an expected steering wheel angle and an actual steering wheel angle change, which shows a tracking curve of a PID parameter calibration process for left-turning of a steering wheel.
Fig. 3 is a schematic diagram ii showing changes in the actual steering wheel angle and the expected steering wheel angle through vehicle verification, which shows a tracking curve of the PID parameter calibration process for right-turn steering wheel, and can be obtained by combining fig. 2, and the tracking effect by using feedforward and feedback control is better under left-turn and right-turn conditions.
Fig. 4 is a schematic view of a steering angle control curve without using feed forward control.
FIG. 5 is a schematic diagram of a corner control curve by adopting feedforward control, and after the feedforward control is added, the curve vibration is small, the stability is improved, and the control precision is greatly improved.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and technical effects of the present invention will be fully apparent to those skilled in the art from the disclosure in the specification. The invention is capable of other embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the general spirit of the invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. The following exemplary embodiments of the present invention may be embodied in many different forms and should not be construed as limited to the specific embodiments set forth herein. It is to be understood that these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the technical solutions of these exemplary embodiments to those skilled in the art.
Referring to fig. 1, a first embodiment of a method for lateral control of a drivable vehicle according to the invention comprises the following steps:
s1, establishing a torque calibration simulation model of the feedforward control system according to the vehicle power-assisted steering characteristic;
the vehicle power-assisted steering characteristic is the relationship among the speed, the steering wheel angle and the steering wheel power-assisted torque obtained by calibrating the steering wheel angles of different steering wheels under the condition of preset speed;
s2, establishing a torque calibration simulation model of the feedback control system according to the vehicle power-assisted steering characteristic;
s3, adjusting parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system according to parameter adjustment rules by setting an expected working condition;
s4, carrying out real vehicle verification on the parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system after parameter adjustment to obtain the optimal parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system, and using the optimal parameters as the parameters of the simulation model of the transverse control feedforward control system and the transverse control feedback control system;
s5, changing different expected working conditions, repeatedly executing the steps S3 and S4, and obtaining a feedforward control system torque calibration simulation model and a feedback control system torque calibration simulation model parameter forming model parameter set under different expected working conditions; with many parameters calibrated, the iterative execution of steps S3 and S4 makes the simulation model more accurate, with other parameters being calibrated with a relatively more accurate simulation model.
And S6, integrating the model parameter set into a torque calibration simulation model of the feedforward control system and a torque calibration simulation model of the feedback control system, and controlling the torques of the feedforward control system and the feedback control system.
According to the first embodiment of the intelligent driving vehicle transverse control method, on the premise that core parameters of the power-assisted steering system are not needed, the transverse control stability of the intelligent vehicle can be improved through repeated iteration of a system model, a simulation model and real vehicle verification. In addition, the first embodiment of the intelligent driving vehicle transverse control method can completely use the power-assisted steering system of the whole vehicle, reduces the refitting cost of the intelligent driving vehicle, and can greatly shorten the calibration period.
The invention provides a second embodiment of a transverse control method of a drivable vehicle, which comprises the following steps:
s1, establishing a torque calibration simulation model of the feedforward control system according to the vehicle power-assisted steering characteristic;
the vehicle power-assisted steering characteristic is the relationship among the speed, the steering wheel angle and the steering wheel power-assisted torque obtained by calibrating the steering wheel angles of different steering wheels under the condition of preset speed;
Figure BDA0002353590980000091
Figure BDA0002353590980000092
wherein z is1、z2、z3Different state observers; l1、l2、l3Is the observer gain coefficient; θ is the desired steering wheel angle; j. the design is a squaremEquivalent moment of inertia for the steering system; mrTo the steering wheel torque, δ is a set threshold and ε is the steering gear ratio.
S2, establishing a torque calibration simulation model of the feedback control system according to the vehicle power-assisted steering characteristic;
Figure BDA0002353590980000093
wherein u (x) is an output torque function, e (t) is a steering wheel angle error function, Kp, Ki and Kd are respectively proportional, integral and differential coefficients obtained by calibration,
Figure BDA0002353590980000094
is the derivative of the error function with respect to time and dt is the system change period.
S3, adjusting parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system according to parameter adjustment rules by setting an expected working condition; the parameter adjustment rule includes a PID value for which the state of the observer and the first and second derivative errors with respect to the desired steering wheel angle are smaller than a preset order of magnitude, which is 10-4；
S4, the actual vehicle verification of the optimal parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system is performed in a crossed manner with the model simulation process, the optimal parameters (minimum tracking error) of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system are obtained, the parameters of the simulation models under different set expected working conditions are calibrated while the simulation models are optimized, and the optimal parameters are used as the parameters of the simulation models of the transverse control feedforward control system and the transverse control feedback control system;
and when the real vehicle is verified, inputting the adjustment coefficient obtained by simulation and the set expected working condition into a real vehicle online calibration algorithm, and controlling the running of the whole vehicle through the CAN communication of the whole vehicle to obtain real vehicle verification parameters.
S5, changing different expected working conditions, repeatedly executing the steps S3 and S4, and obtaining a feedforward control system torque calibration simulation model and a feedback control system torque calibration simulation model parameter forming model parameter set under different expected working conditions; with many parameters calibrated, the iterative execution of steps S3 and S4 makes the simulation model more accurate, with other parameters being calibrated with a relatively more accurate simulation model.
And S6, integrating the model parameter set into a torque calibration simulation model of the feedforward control system and a torque calibration simulation model of the feedback control system, and controlling the torques of the feedforward control system and the feedback control system.
Wherein the adjustment coefficients include a proportional coefficient, an integral coefficient and a differential coefficient, and the desired operating condition includes a desired steering wheel angle and a desired vehicle speed.
The invention provides a first implementation of a lateral control system of an intelligent driving vehicle, which comprises the following components:
the modeling module is used for establishing a torque calibration simulation model of a feedforward control system and a torque calibration simulation model of a feedback control system according to the vehicle power-assisted steering characteristic, wherein the vehicle power-assisted steering characteristic is the relationship among the speed, the steering wheel angle and the steering wheel power-assisted torque obtained by calibrating different steering wheel angles under the condition of preset speed;
the parameter adjusting module adjusts parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system according to parameter adjusting rules by setting an expected working condition;
the verification module is used for guiding the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system after parameter adjustment into a real vehicle, performing real vehicle verification to obtain optimal parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system, and using the optimal parameters as parameters of the simulation model of the transverse control feedforward control system and the transverse control feedback control system;
the control module is used for converting different expected working conditions, guiding the different expected working conditions into the control parameter adjusting module and the verification module, obtaining parameters of a feedforward control system torque calibration simulation model and a feedback control system torque calibration simulation model under different expected working conditions to form model parameter sets, and integrating the model parameter sets into the feedforward control system torque calibration simulation model and the feedback control system torque calibration simulation model for torque control of the feedforward control system and the feedback control system.
The invention provides a second implementation of a lateral control system of an intelligent driving vehicle, which comprises the following components:
the modeling module is used for establishing a torque calibration simulation model of a feedforward control system and a torque calibration simulation model of a feedback control system according to the vehicle power-assisted steering characteristic, wherein the vehicle power-assisted steering characteristic is the relationship among the speed, the steering wheel angle and the steering wheel power-assisted torque obtained by calibrating different steering wheel angles under the condition of preset speed;
the torque calibration simulation model of the feedforward control system is as follows;
Figure BDA0002353590980000111
Figure BDA0002353590980000112
wherein z is1、z2、z3Different state observers; l1、l2、l3Is the observer gain coefficient; θ is the desired steering wheel angle; j. the design is a squaremEquivalent moment of inertia for the steering system; mrTo the steering wheel torque, δ is a set threshold and ε is the steering gear ratio.
The torque calibration simulation model of the feedback control system is as follows;
Figure BDA0002353590980000113
wherein u (x) is an output torque function, e (t) is a steering wheel angle error function, Kp, Ki and Kd are respectively proportional, integral and differential coefficients obtained by calibration,
Figure BDA0002353590980000114
is the derivative of the error function with respect to time, dt is the system change period;
the parameter adjusting module adjusts parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system according to parameter adjusting rules by setting an expected working condition; the parameter adjustment rule comprises the state of the observer and the first and second derivative errors with respect to the desired steering wheel angle being smaller than PID values of a preset order of magnitude, said preset order of magnitude being 10-4；
The verification module is used for guiding the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system after parameter adjustment into a real vehicle, performing real vehicle verification to obtain optimal parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system, and using the optimal parameters as parameters of the simulation model of the transverse control feedforward control system and the transverse control feedback control system;
and when the verification module performs real vehicle verification, inputting the adjustment coefficient obtained by the simulation model and the set expected working condition into a real vehicle online calibration algorithm, and controlling the operation of the whole vehicle through the CAN communication of the whole vehicle to obtain real vehicle verification parameters.
The control module is used for transforming different expected working conditions, guiding the transformed expected working conditions into the control parameter adjusting module and the verification module, obtaining parameters of a feedforward control system torque calibration simulation model and a feedback control system torque calibration simulation model under different expected working conditions to form model parameter sets, and integrating the model parameter sets into the feedforward control system torque calibration simulation model and the feedback control system torque calibration simulation model for torque control of the feedforward control system and the feedback control system; the feedforward module also enables the actual vehicle verification of the optimal parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system to be performed in a crossed manner with the model simulation process, and the parameters of the simulation models under different set expected working conditions are calibrated while the simulation models are optimized.
Wherein the adjustment coefficients include a proportional coefficient, an integral coefficient and a differential coefficient, and the desired operating condition includes a desired steering wheel angle and a desired vehicle speed.
The present invention has been described in detail with reference to the specific embodiments and examples, but these are not intended to limit the present invention. Many variations and modifications may be made by one of ordinary skill in the art without departing from the principles of the present invention, which should also be considered as within the scope of the present invention.

Claims (20)

1. A method for controlling the transverse direction of an intelligent driving vehicle is characterized by comprising the following steps:
s1, establishing a torque calibration simulation model of the feedforward control system according to the vehicle power-assisted steering characteristic;
the vehicle power-assisted steering characteristic is the relationship among the speed, the steering wheel angle and the steering wheel power-assisted torque obtained by calibrating the steering wheel angles of different steering wheels under the condition of preset speed;
s2, establishing a torque calibration simulation model of the feedback control system according to the vehicle power-assisted steering characteristic;
s3, adjusting parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system according to parameter adjustment rules by setting an expected working condition;
s4, carrying out real vehicle verification on the parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system after parameter adjustment to obtain the optimal parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system, and using the optimal parameters as the parameters of the simulation model of the transverse control feedforward control system and the transverse control feedback control system;
s5, changing different expected working conditions, repeatedly executing the steps S3 and S4, and obtaining a feedforward control system torque calibration simulation model and a feedback control system torque calibration simulation model parameter forming model parameter set under different expected working conditions;
and S6, integrating the model parameter set into a torque calibration simulation model of the feedforward control system and a torque calibration simulation model of the feedback control system, and controlling the torques of the feedforward control system and the feedback control system.
2. The intelligent-drive vehicle lateral control method of claim 1, wherein:
the torque calibration simulation model of the feedforward control system is as follows;
Figure FDA0003022026750000011
Figure FDA0003022026750000012
wherein z is1、z2、z3Different state observers; l1、l2、l3Is the observer gain coefficient; θ is the desired steering wheel angle; j. the design is a squaremEquivalent moment of inertia for the steering system; mrTo the steering wheel torque, δ is a set threshold and ε is the steering gear ratio.
3. The intelligent-drive vehicle lateral control method of claim 1, wherein:
the torque calibration simulation model of the feedback control system is as follows;
Figure FDA0003022026750000021
wherein u (x) is an output torque function, e (t) is a steering wheel angle error function, Kp, Ki, Kd are proportional, integral and differential coefficients, respectively,
Figure FDA0003022026750000022
is the derivative of the error function with respect to time and dt is the system change period.
4. The intelligent-drive vehicle lateral control method of claim 1, wherein:
the parameter adjustment rule includes PID values that cause the states of the observer and the first and second derivative errors with respect to the desired steering wheel angle to be less than a preset order of magnitude.
5. The intelligent-drive vehicle lateral control method of claim 4, wherein: the predetermined order of magnitude is 10-4。
6. The intelligent-drive vehicle lateral control method of claim 1, wherein: when the step S4 is implemented, the minimum tracking error of the feedforward control system torque calibration simulation model and the feedback control system torque calibration simulation model is used as the optimal parameter of the feedforward control torque calibration simulation model and the feedback control torque calibration simulation model.
7. The intelligent-drive vehicle lateral control method of claim 1, wherein: when the step S4 is implemented, the actual vehicle verification of the optimal parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system is performed in a crossed manner with the model simulation process, and the simulation model parameters under different set expected working conditions are calibrated while the simulation model is optimized.
8. The intelligent-drive vehicle lateral control method of claim 1, wherein: when the step S4 is executed to perform the real vehicle verification, the method includes the following steps:
1) inputting the adjustment coefficient obtained by simulation and the set expected working condition into a real vehicle online calibration algorithm;
2) and controlling the running of the whole vehicle through CAN communication of the whole vehicle to obtain real vehicle verification parameters.
9. The intelligent-drive vehicle lateral control method of claim 8, wherein: the adjustment coefficients include a proportionality coefficient, an integral coefficient, and a differential coefficient.
10. The intelligent-drive vehicle lateral control method of claim 1, wherein: in step S3, the desired operating conditions include a desired steering wheel angle and a desired vehicle speed.
11. A smart-driven vehicle lateral control system, comprising:
the modeling module is used for establishing a torque calibration simulation model of a feedforward control system and a torque calibration simulation model of a feedback control system according to the vehicle power-assisted steering characteristic, wherein the vehicle power-assisted steering characteristic is the relationship among the speed, the steering wheel angle and the steering wheel power-assisted torque obtained by calibrating different steering wheel angles under the condition of preset speed;
the parameter adjusting module adjusts parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system according to parameter adjusting rules by setting an expected working condition;
the verification module is used for guiding the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system after parameter adjustment into a real vehicle, performing real vehicle verification to obtain optimal parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system, and using the optimal parameters as parameters of the simulation model of the transverse control feedforward control system and the transverse control feedback control system;
the control module is used for converting different expected working conditions, guiding the different expected working conditions into the control parameter adjusting module and the verification module, obtaining parameters of a feedforward control system torque calibration simulation model and a feedback control system torque calibration simulation model under different expected working conditions to form model parameter sets, and integrating the model parameter sets into the feedforward control system torque calibration simulation model and the feedback control system torque calibration simulation model for torque control of the feedforward control system and the feedback control system.
12. The smart driving vehicle lateral control system of claim 11, wherein:
the modeling module establishes a torque calibration simulation model of the feedforward control system as follows;
Figure FDA0003022026750000031
Figure FDA0003022026750000032
wherein z is1、z2、z3Different state observers; l1、l2、l3Is the observer gain coefficient; θ is the desired steering wheel angle; j. the design is a squaremEquivalent moment of inertia for the steering system; mrTo the steering wheel torque, δ is a set threshold and ε is the steering gear ratio.
13. The smart driving vehicle lateral control system of claim 11, wherein:
the modeling module establishes a torque calibration simulation model of the feedback control system as follows;
Figure FDA0003022026750000033
wherein u (x) is an output torque function, e (t) is a steering wheel angle error function, Kp, Ki, Kd are proportional, integral and differential coefficients, respectively,
Figure FDA0003022026750000041
is the derivative of the error function with respect to time and dt is the system change period.
14. The smart driving vehicle lateral control system of claim 11, wherein:
the parameter adjustment rules of the parameter adjustment module include PID values that cause the state of the observer and the first and second derivative errors with respect to the desired steering wheel angle to be less than a predetermined order of magnitude.
15. The smart driving vehicle lateral control system of claim 14, wherein: the predetermined order of magnitude is 10-4。
16. The smart driving vehicle lateral control system of claim 11, wherein:
the verification module takes the minimum tracking error of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system as the optimal parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system.
17. The smart driving vehicle lateral control system of claim 11, wherein:
the control module enables the actual vehicle verification of the optimal parameters of the torque calibration simulation model of the feedforward control system and the torque calibration simulation model of the feedback control system to be performed in a crossed manner with the model simulation process, and the simulation model parameters under different set expected working conditions are calibrated while the simulation model is optimized.
18. The smart driving vehicle lateral control system of claim 11, wherein: and when the verification module performs real vehicle verification, inputting the adjustment coefficient obtained by the simulation model and the set expected working condition into a real vehicle online calibration algorithm, and controlling the operation of the whole vehicle through the CAN communication of the whole vehicle to obtain real vehicle verification parameters.
19. The smart driving vehicle lateral control system of claim 18, wherein: the adjustment coefficients include a proportionality coefficient, an integral coefficient, and a differential coefficient.
20. The smart driving vehicle lateral control system of claim 11, wherein: the desired operating conditions include a desired steering wheel angle and a desired vehicle speed.
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