CN111559389A - Control method of intelligent automobile under variable adhesion coefficient repeatability track - Google Patents

Control method of intelligent automobile under variable adhesion coefficient repeatability track Download PDF

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CN111559389A
CN111559389A CN202010397780.3A CN202010397780A CN111559389A CN 111559389 A CN111559389 A CN 111559389A CN 202010397780 A CN202010397780 A CN 202010397780A CN 111559389 A CN111559389 A CN 111559389A
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vehicle
control
intelligent automobile
control method
yaw rate
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汪伟
盛广庆
杨凤敏
罗金
姜苏杰
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Jiangsu University of Technology
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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
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • 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
    • B60W30/02—Control of vehicle driving stability
    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B60W40/064—Degree of grip
    • 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
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B60W60/0015—Planning or execution of driving tasks specially adapted for safety
    • B60W60/0016—Planning or execution of driving tasks specially adapted for safety of the vehicle or its occupants
    • 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
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/30—Road curve radius
    • 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
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • 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
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B60W2554/402—Type
    • B60W2554/4029—Pedestrians
    • 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
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B60W2554/404—Characteristics
    • B60W2554/4042—Longitudinal speed
    • 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
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B60W2554/404—Characteristics
    • B60W2554/4043—Lateral speed
    • 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
    • B60W2555/00—Input parameters relating to exterior conditions, not covered by groups B60W2552/00, B60W2554/00
    • B60W2555/20—Ambient conditions, e.g. wind or rain

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  • Mathematical Physics (AREA)
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Abstract

A control method of an intelligent automobile under a variable adhesion coefficient repetitive track changes a prediction range according to the change probability of an adhesion coefficient on the basis of a single prediction range of a traditional MPC (multi-media personal computer), so that the emergency capacity of the intelligent automobile is improved. The invention tracks the path by adopting an iterative learning control method, improves the tracking precision of the path, and improves the driving stability of the intelligent automobile by combining with a yaw stability controller. The MPC controller of the invention calls the corresponding control strategy of the possible occurrence situation in advance according to the information provided by the environment perception module, wherein the strategy comprises the corresponding prediction time domain and the constraint set; after that, iterative learning control is used as a method for determining the correct steering input of the transient driving, the tracking performance is improved through multiple iterations, and the driving stability of the intelligent automobile is improved by combining a yaw stability controller.

Description

Control method of intelligent automobile under variable adhesion coefficient repeatability track
Technical Field
The invention belongs to the field of intelligent automobile control, and particularly relates to a control method of an intelligent automobile under a variable-adhesion-coefficient repetitive track.
Background
With the rapid development of computer information processing technology, driving intelligence is used as the development direction of the automobile industry. The conventional MPC is mainly used for improving the accuracy of path tracking, but the accuracy is often reduced when the vehicle runs on a road surface with a variable adhesion coefficient, and a corresponding reaction is made only when the adhesion coefficient changes, so that the unmanned vehicle cannot accurately track a desired path. Therefore, the traditional single prediction range algorithm cannot solve the control requirements and control functions of the vehicle under different emergencies, and the traditional controller only controls the running track based on a general physical model and by using the current error, and does not fully utilize historical information to improve the running speed and the track tracking accuracy.
Disclosure of Invention
A control method of an intelligent automobile under a variable adhesion coefficient repetitive track changes a prediction range according to the change probability of an adhesion coefficient on the basis of a single prediction range of a traditional MPC (multi-media personal computer), so that the emergency capacity of the intelligent automobile is improved. The invention tracks the path by adopting an iterative learning control method, improves the tracking precision of the path, and improves the driving stability of the intelligent automobile by combining with a yaw stability controller.
A control method of an intelligent automobile under a variable adhesion coefficient repetitive track comprises the following steps:
step 1, acquiring front road information of an intelligent automobile in real time through an environment sensing module, collecting road condition emergencies in traffic road information in real time by combining a sensing technology, and transmitting the road condition emergencies to a control module for a controller to call in advance;
step 2, obtaining a vehicle balance equation on the basis of a three-degree-of-freedom vehicle motion model, obtaining a dynamic model through a vehicle body state quantity and a road state quantity, then controlling by using a variable prediction range model and a prediction control method, and converting the variable prediction range model into a state space form to obtain a state space equation; meanwhile, corresponding weights and constraints are calculated for different road adhesion coefficients, and performance indexes and system constraints are established;
step 3, an iterative learning controller is arranged, after the dynamic model predictive control of the step 2 finishes the reaction of the corresponding emergency, under the condition that one circle of error response is given to be finished, the learning steering input of the next circle is determined, and the steering dynamics change caused by the vehicle speed change is considered through an iterative learning algorithm;
and 4, setting a stability controller, combining stability control with dynamic model prediction and iterative learning algorithm, and controlling the vehicle yaw angular speed to improve the stability of the vehicle by optimizing and adjusting the output quantity of the transverse and longitudinal direction and the stability controller.
Further, the emergency road conditions include, but are not limited to, severe weather, pedestrian crossing at the roadside, and sudden vehicle stop.
Further, in step 2, the three-degree-of-freedom vehicle dynamics model is adopted, and the three-degree-of-freedom vehicle dynamics model comprises longitudinal motion in the X-axis direction, transverse motion in the Y-axis direction and yaw motion in the rotation direction around the Z-axis, so that balance equations along the X-axis, the Y-axis and around the Z-axis are obtained:
Figure BDA0002488313850000031
wherein m is the vehicle mass; x is the longitudinal displacement; phi is a yaw angle;fis a front wheel corner; y is the lateral displacement; i iszIs the rotational inertia of the Z axis; fxIs the total longitudinal force experienced by the vehicle; fyIs the total lateral force experienced by the vehicle; mzThe total yaw moment borne by the vehicle; fcf，FcrThe lateral force borne by front and rear tires of the vehicle is related to the cornering stiffness and the cornering angle of the tires; flf，FlrLongitudinal force borne by front and rear tires of a vehicle is related to longitudinal rigidity and slip ratio of the tires; fxf，FxrThe stress of front and rear tires of the vehicle in the x direction is applied; fyf，FyrThe force is applied to the front and rear tires of the vehicle in the y direction; a is the distance from the front axis to the center of mass and b is the distance from the rear axis to the center of mass.
The dynamic model obtained by combining the three vehicle body state quantities of equations 2 to 4, and the three road state quantities of equations 5 to 7 is as follows:
Figure BDA0002488313850000032
Figure BDA0002488313850000033
Figure BDA0002488313850000034
Figure BDA0002488313850000035
Figure BDA0002488313850000036
Figure BDA0002488313850000037
wherein
Figure BDA0002488313850000038
Is the derivative of the longitudinal path of the car,
Figure BDA0002488313850000039
is the lateral error derivative of the distance from the center of mass of the automobile to the path, kappa is the road curvature, Delta psi is the course angle error, Vx、Vyβ and r are the longitudinal and transverse speeds, mass center slip angle and yaw rate of the automobile respectively.
Further, after a dynamic model is established, a variable prediction range model prediction control method is used for controlling; converting the dynamic model established in the formulas 2 to 7 into a state space form, and discretizing the model by using a first-order euler method to obtain state space equations shown in the formulas 8 to 9:
Figure BDA0002488313850000041
e＝Ccx (9)
in which the state quantity x is composed of (V)xVyr delta psi e) is formed by matrix splicing, the transverse and longitudinal dynamic characteristics are comprehensively considered, AcIs a state matrix, BcFor a matrix of control quantities, d is the disturbance quantity, CcIs an output quantity matrix; wherein e is the lateral error of the distance from the center of mass of the automobile to the driving track, and u is the control quantity.
Further, x isnSimilar to the single adhesion coefficient model predictive control, xd1、...xdnRepresenting various different road adhesion coefficients, corresponding to different constraints and cost functions, establishing a performance index J as follows:
Figure BDA0002488313850000042
wherein N isp、NcRespectively prediction and control time domain, Q, R, P respectively corresponding weight matrix set, rho respectively weight coefficient set and relaxation factor set, xkIs a state variable, Δ ukD is the disturbance quantity for control increment;
the system constraints are:
Figure BDA0002488313850000051
Figure BDA0002488313850000052
wherein
Figure BDA0002488313850000053
At the point where k is 0, the position of the first electrode,
Figure BDA0002488313850000054
wherein, each item U in the formula is a control quantity set calculated in the first step of the initial stage of the control program under different attachment coefficients, and k is the step length.
Further, constraint set H, G constrains the attachment coefficients; at U0The original expected path is tracked, and meanwhile, the emergency operation for the sudden change of the attachment coefficient is kept; hnAnd GnIs a constraint set with invariant attachment coefficients, where each term is a different constraint set corresponding to a different attachment coefficient, such as:
Figure BDA0002488313850000055
Figure BDA0002488313850000056
as HnIs set of constraints, adopts
Figure BDA0002488313850000061
Figure BDA0002488313850000062
In the formula
Figure BDA0002488313850000063
Expressed as a function of the width of the road surface, as GnA constraint set of (2); e.g. of the typek，
Figure BDA0002488313850000064
Is the deviation of the centroid from the reference path, and deals with different attachment coefficients, such as α, for values in the constraintsr，sat、
Figure BDA0002488313850000065
Making an adjustment; h and G respectively adjust the yaw rate, the centroid slip angle and the environmental constraint and continue to track the original expected path after the event is endedAnd (4) diameter.
Further, if multiple adhesion coefficients are detected simultaneously, output results when the adhesion coefficients on the left side and the right side change are calculated simultaneously, and the final results are obtained through final weighted summation; the expected path is always followed if no abrupt adhesion coefficients occur.
Further, in step 3, after the model predictive control completes the reaction of the corresponding emergency, an error response e is given to be completed by one circlejIn the case of (2), the learning steering input of the next round is determined
Figure BDA0002488313850000066
The iterative learning algorithm framework is as follows:
Figure BDA0002488313850000067
where Q is an NxN filter matrix and L is an NxN learning matrix, the matrices Q and L will be obtained by a quadratic optimal Q-ILC learning controller;
learning steering input
Figure BDA0002488313850000068
Determined by minimizing the quadratic cost function in the next iteration:
Figure BDA0002488313850000071
wherein
Figure BDA0002488313850000072
T, R, S is the weight matrix of N × N, the filter matrix and the iteration matrix are as follows:
Q＝(PTTP+R+S)-1(PTTP+S) (19)
L＝(PTTP+S)-1PTTP[T1/2P)-1T1/2(20)
further, in step 4, a desired yaw rate is calculated, and compared with an actual yaw rate, an additional yaw moment is controlled according to the deviation value, and the driving torque of the outer side wheel or the inner side wheel is reduced, so that the driving stability of the vehicle is improved;
the desired yaw rate is calculated as:
Figure BDA0002488313850000073
wherein L is the wheelbase, C2，C1The cornering stiffness of the front and rear wheels, respectively, K is the stability factor:
Figure BDA0002488313850000074
while in conjunction with road adhesion conditions, the desired yaw rate can be expressed as:
Figure BDA0002488313850000075
wherein mu is the road surface friction coefficient g and is the gravity acceleration.
The ideal value for yaw rate is expressed as:
Figure BDA0002488313850000081
the vehicle stability control is performed by controlling the yaw moment of the vehicle. The difference value between the ideal yaw rate and the actual yaw rate is obtained by measuring the actual yaw rate of the vehicle, and is as follows: e.g. of the typer＝rd-r; obtaining the value DeltaM of the additional yaw moment by a PID control strategyz(ii) a After the desired additional yaw moment is obtained by the above controller, a control strategy of proportional braking of the front and rear wheels is adopted from the viewpoint of vehicle braking, and the braking force required for the obtained additional yaw moment is proportionally distributed to the front and rear wheels.
The invention achieves the following beneficial effects:
(1) according to the idea of model prediction control on the variable adhesion coefficient, an operation mode adopted after the original adhesion coefficient is mutated is replaced by a mode which is used for judging a possible event through environmental perception in advance, and a prediction time domain, a corresponding constraint set and a cost function which correspond to the possible event are called for a prediction range model prediction controller, so that the intelligent automobile changes a tracking path in advance, the emergency caused by the mutation of the adhesion coefficient is avoided, and the running stability of the automobile is improved.
(2) The iterative trajectory tracking control method is a method for determining correct vehicle steering input parameters under a transient driving working condition through multiple iterative computations, so that the reference tracking performance is improved.
(3) The proposed method of additional stability control improves the stability of the vehicle by controlling the yaw rate of the vehicle. After the controller obtains the expected additional yaw moment, a control strategy of proportionally distributing and braking the front wheels and the rear wheels is adopted, and the obtained additional yaw moment is proportionally distributed to the front wheels and the rear wheels, so that the reliability and the safety of the vehicle are ensured.
Drawings
FIG. 1 is a logic block diagram of a control module in an embodiment of the invention.
FIG. 2 is a block diagram of multiple prediction ranges in an embodiment of the invention.
FIG. 3 is a block diagram of a cost function for multiple prediction ranges in an embodiment of the invention.
FIG. 4 is a schematic diagram of a three-degree-of-freedom vehicle dynamics model according to an embodiment of the present invention.
Fig. 5 is an XYZ directional diagram of a vehicle in an embodiment of the present invention.
FIG. 6 is a schematic representation of a vehicle under-steer and over-steer in an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
A control method of an intelligent automobile under a variable adhesion coefficient repetitive track comprises the following steps:
step 1, designing an environment perception module.
The intelligent vehicle front road information is collected in real time, and road condition events such as ice and snow weather, roadside pedestrians or front vehicles stopping and the like in the traffic road information are collected in real time and transmitted to the control module to be called by the controller in advance by combining the sensing technology.
And 2, designing a variable attachment coefficient Model Prediction (MPC) module.
The invention adopts a three-degree-of-freedom vehicle dynamic model, which comprises a longitudinal motion (X-axis direction), a transverse motion (Y-axis direction) and a yaw motion (rotating direction around a Z axis) as shown in figure 5, and as shown in figure 4, a three-degree-of-freedom single-track dynamic model schematic diagram of the vehicle is shown. The meaning of each character represented in fig. 4 below is introduced. The Z-axis passes through the centroid O and is perpendicular to the plane of the x-axis and the y-axis. The equilibrium equations along the x-axis, y-axis and around the z-axis can be obtained according to newton's second law theorem:
Figure BDA0002488313850000101
wherein m is the vehicle mass; x is the longitudinal displacement; phi is a yaw angle;fis a front wheel corner; y is the lateral displacement; i iszIs the rotational inertia of the Z axis; fxIs the total longitudinal force experienced by the vehicle; fyIs the total lateral force experienced by the vehicle; mzThe total yaw moment borne by the vehicle; fcf，FcrThe lateral force borne by front and rear tires of the vehicle is related to the cornering stiffness and the cornering angle of the tires; flf，FlrLongitudinal force borne by front and rear tires of a vehicle is related to longitudinal rigidity and slip ratio of the tires; fxf，FxrThe stress of front and rear tires of the vehicle in the x direction is applied; fyf，FyrThe force is applied to the front and rear tires of the vehicle in the y direction; a is the distance from the front axis to the center of mass and b is the distance from the rear axis to the center of mass.
The dynamic model obtained by combining the three vehicle body state quantities (variables on the left side of the equal sign in the equations 2-4) and the three road state quantities (variables on the left side of the equal sign in the equations 5-7) is as follows:
Figure BDA0002488313850000102
Figure BDA0002488313850000103
Figure BDA0002488313850000104
Figure BDA0002488313850000105
Figure BDA0002488313850000106
Figure BDA0002488313850000107
wherein
Figure BDA0002488313850000111
Is the derivative of the longitudinal path of the car,
Figure BDA0002488313850000112
is the lateral error derivative of the distance from the center of mass of the automobile to the path, k is the road curvature, delta psi is the course angle error, Vx、Vyβ and r are the longitudinal and transverse speeds, mass center slip angle and yaw rate of the automobile respectively.
After the dynamic model is established, a variable prediction range model prediction control method is used for control. Converting the dynamics models established in the formulas (2) to (7) into a state space form, and discretizing the models by using a first-order Euler method to obtain a state space equation shown in the formulas 8 to 9:
Figure BDA0002488313850000113
e＝Ccx (9)
in which the state quantity x is composed of (V)xVyr delta psi e) is formed by matrix splicing, the transverse and longitudinal dynamic characteristics are comprehensively considered, AcIs in a stateMatrix, BcFor a matrix of control quantities, d is the disturbance quantity, CcIs an output quantity matrix; wherein e is the lateral error of the distance from the center of mass of the automobile to the driving track, and u is the control quantity.
As shown in FIG. 2, where xnSimilar to single adhesion coefficient model predictive control, but otherwise xd1、...xdnVarious road adhesion coefficients are represented, corresponding to different constraints and cost functions. The advantage of this separation is that only x is calculated without a sudden change in the attachment coefficientnAnd calculating and outputting the corresponding cost function, and only calculating corresponding weight and constraint after detecting the change of the adhesion coefficient.
The performance index J is established as follows
Figure BDA0002488313850000121
Wherein N isp、NcRespectively prediction and control time domain, Q, R, P respectively corresponding weight matrix set, rho respectively weight coefficient set and relaxation factor set, xkIs a state variable, Δ ukTo control the increment, d is the disturbance quantity.
The system constraints are:
Figure BDA0002488313850000122
Figure BDA0002488313850000123
wherein
Figure BDA0002488313850000124
At the point where k is 0, the position of the first electrode,
Figure BDA0002488313850000125
wherein, each item U in the formula is a control quantity set calculated in the first step of the initial stage of the control program under different attachment coefficients, and k is the step length.
Constraint set H, G is used to constrain the adhesion coefficients. At U0It remains to track the originally desired path while maintaining emergency operation for abrupt adhesion coefficients. HnAnd GnIs a constraint set with constant adhesion coefficient, Hn1、Gn1；Hn2、Gn2Etc. are different sets of constraints corresponding to different attachment coefficients. Such as
Figure BDA0002488313850000131
Vy，max＝Vxαr，sat+br (14)
As HnIs set of constraints, adopts
Figure BDA0002488313850000132
Figure BDA0002488313850000133
In the formula
Figure BDA0002488313850000134
Expressed as a function of the width of the road surface, as GnIs determined. e.g. of the typek，
Figure BDA0002488313850000135
Is the deviation of the centroid from the reference path, for different attachment coefficients, for values in the constraint such as αr，sat、
Figure BDA0002488313850000136
Adjustments are made. H and G respectively adjust the yaw rate, the centroid slip angle and the environmental constraint and continue to track the original expected path after the event is finished. If multiple adhesion coefficients are detected simultaneously, the output results of the left and right adhesion coefficients when changed are calculated simultaneously, and the final results are obtained by weighting and summing finally, like the difference of the adhesion coefficients of the left and right sides at a moment. Tracking period if no abrupt adhesion coefficient occursThe approach is followed.
And 3, designing an iterative learning controller.
After the model predictive control has finished reacting to the corresponding emergency, an error response e is given to have finished one turnjIn the case of (2), the learning steering input of the next round is determined
Figure BDA0002488313850000137
The iterative learning algorithm framework is as follows:
Figure BDA0002488313850000138
where Q is an NxN filter matrix and L is an NxN learning matrix, the matrices Q and L will be obtained by a quadratic optimal (Q-ILC) learning controller.
Learning steering input
Figure BDA0002488313850000141
Is determined by minimizing the quadratic cost function in the next iteration:
Figure BDA0002488313850000142
wherein
Figure BDA0002488313850000143
T, R, S is a weight matrix of N × N that allows weighting competing targets to minimize tracking error and control amount variation.
The filter matrix and the iteration matrix are as follows:
Q＝(PTTP+R+S)-1(PTTP+S) (19)
L＝(PTTP+S)-1PTTP(T1/2P)-1T1/2(20)
one advantage of quadratic optimal control design is that the Q and L of the controller matrix take into account the dynamics P matrix. This enables the iterative learning algorithm to account for changes in steering dynamics due to changes in vehicle speed.
And 4, designing a stability controller.
This step improves the stability of the vehicle by controlling the yaw rate of the vehicle. Stability control is combined with model prediction and iterative learning, and the output quantity of the transverse and longitudinal stability controllers is optimized and adjusted, so that the trajectory tracking precision and stability of the vehicle under the variable attachment coefficient are improved.
Since the yaw rate has a large influence on the stability of the vehicle, a desired yaw rate is calculated, and an additional yaw moment is controlled based on a deviation value of the calculated yaw rate from an actual yaw rate, in such a manner that the driving torque of the outer or inner wheel is reduced, thereby improving the driving stability of the vehicle.
The desired yaw rate is calculated as:
Figure BDA0002488313850000151
wherein L is the wheelbase, C2，C1The cornering stiffness of the front and rear wheels, respectively, K is the stability factor:
Figure BDA0002488313850000152
while in conjunction with road adhesion conditions, the desired yaw rate can be expressed as:
Figure BDA0002488313850000153
the ideal value for yaw rate is expressed as:
Figure BDA0002488313850000154
the vehicle stability control is performed by controlling the yaw moment of the vehicle. The difference value between the ideal yaw rate and the actual yaw rate is obtained by measuring the actual yaw rate of the vehicle, and is as follows: e.g. of the typer＝rd-r. Obtaining the value DeltaM of the additional yaw moment by a PID control strategyz. After the desired additional yaw moment is derived by the above controller, it needs to be addressed how to apply the additional yaw moment to the car. From the angle of vehicle braking, the four wheels of the vehicle are respectively braked to generate additional yaw moments with different magnitudes and different directions. Taking the left turn of the vehicle as an example, the steering angle is a positive value, and the yaw moment applied to the vehicle body is also a positive value. When the vehicle is in excessive steering, the actual yaw moment value is larger than the steady-state yaw moment value, and the negative yaw moment in the clockwise direction needs to be added to offset the excessive steering of the vehicle. Similarly, adding a counter-clockwise positive yaw moment when understeer occurs counteracts the understeer of the vehicle as shown in fig. 5. The braking force required for the additional yaw moment obtained above is proportionally distributed to the front and rear wheels, taking a control strategy for proportional braking of the front and rear wheels. The purpose is as follows: since the braking force is distributed to the two wheels, the brake pressure load on a single wheel is reduced as compared to acting on a single wheel, thereby protecting the brakes on a single wheel. Meanwhile, the existing brake system adopts a four-channel brake pipeline distribution mode, so that when one pipeline fails to brake, the normal work of the rest three brake pipelines can be still kept, and the reliability and the safety of the control process are improved.
The above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above embodiment, but equivalent modifications or changes made by those skilled in the art according to the present disclosure should be included in the scope of the present invention as set forth in the appended claims.

Claims (9)

1. A control method of an intelligent automobile under a variable adhesion coefficient repeatability track is characterized by comprising the following steps: the method comprises the following steps:
step 1, acquiring front road information of an intelligent automobile in real time through an environment sensing module, collecting road condition emergencies in traffic road information in real time by combining a sensing technology, and transmitting the road condition emergencies to a control module for a controller to call in advance;
step 2, obtaining a vehicle balance equation on the basis of a three-degree-of-freedom vehicle motion model, obtaining a dynamic model through a vehicle body state quantity and a road state quantity, then controlling by using a variable prediction range model and a prediction control method, and converting the variable prediction range model into a state space form to obtain a state space equation; meanwhile, corresponding weights and constraints are calculated for different road adhesion coefficients, and performance indexes and system constraints are established;
step 3, an iterative learning controller is arranged, after the dynamic model predictive control of the step 2 finishes the reaction of the corresponding emergency, under the condition that one circle of error response is given to be finished, the learning steering input of the next circle is determined, and the steering dynamics change caused by the vehicle speed change is considered through an iterative learning algorithm;
and 4, setting a stability controller, combining stability control with dynamic model prediction and iterative learning algorithm, and controlling the vehicle yaw angular speed to improve the stability of the vehicle by optimizing and adjusting the output quantity of the transverse and longitudinal direction and the stability controller.
2. The control method of the intelligent automobile under the variable adhesion coefficient repeatability track according to claim 1, characterized in that: the road emergency events include, but are not limited to, severe weather, roadside pedestrian crossing and front vehicle sudden stop.
3. The control method of the intelligent automobile under the variable adhesion coefficient repeatability track according to claim 1, characterized in that: in step 2, the three-degree-of-freedom vehicle dynamics model is adopted, and the three-degree-of-freedom vehicle dynamics model comprises longitudinal motion in the X-axis direction, transverse motion in the Y-axis direction and yaw motion in the rotating direction around the Z-axis, so that balance equations along the X-axis, the Y-axis and around the Z-axis are obtained:
Figure FDA0002488313840000021
wherein m is the vehicle mass; x is the longitudinal displacement; phi is a yaw angle;fis the front wheel turning angle(ii) a y is the lateral displacement; i iszIs the rotational inertia of the Z axis; fxIs the total longitudinal force experienced by the vehicle; fyIs the total lateral force experienced by the vehicle; mzThe total yaw moment borne by the vehicle; fcf，FcrThe lateral force borne by front and rear tires of the vehicle is related to the cornering stiffness and the cornering angle of the tires; flf，FlrLongitudinal force borne by front and rear tires of a vehicle is related to longitudinal rigidity and slip ratio of the tires; fxf，FxrThe stress of front and rear tires of the vehicle in the x direction is applied; fyf，FyrThe force is applied to the front and rear tires of the vehicle in the y direction; a is the distance from the front axis to the center of mass and b is the distance from the rear axis to the center of mass.
The dynamic model obtained by combining the three vehicle body state quantities of equations 2 to 4, and the three road state quantities of equations 5 to 7 is as follows:
Figure FDA0002488313840000022
Figure FDA0002488313840000023
Figure FDA0002488313840000024
Figure FDA0002488313840000025
Figure FDA0002488313840000026
Figure FDA0002488313840000031
wherein
Figure FDA0002488313840000032
Is steamThe derivative of the longitudinal path of the vehicle,
Figure FDA0002488313840000033
is the lateral error derivative of the distance from the center of mass of the automobile to the path, kappa is the road curvature, Delta psi is the course angle error, Vx、Vyβ and r are the longitudinal and transverse speeds, mass center slip angle and yaw rate of the automobile respectively.
4. The control method of the intelligent automobile under the variable adhesion coefficient repeatability track according to claim 3, characterized in that: after a dynamic model is established, a variable prediction range model prediction control method is used for controlling; converting the dynamic model established in the formulas 2 to 7 into a state space form, and discretizing the model by using a first-order euler method to obtain state space equations shown in the formulas 8 to 9:
Figure FDA0002488313840000034
e＝Ccx (9)
in which the state quantity x is composed of (V)xVyr delta psi e) is formed by matrix splicing, the transverse and longitudinal dynamic characteristics are comprehensively considered, AcIs a state matrix, BcFor a matrix of control quantities, d is the disturbance quantity, CcIs an output quantity matrix; wherein e is the lateral error of the distance from the center of mass of the automobile to the driving track, and u is the control quantity.
5. The control method of the intelligent automobile under the variable adhesion coefficient repeatability track according to claim 1, characterized in that: x is to benSimilar to the single adhesion coefficient model predictive control, xd1、...xdnRepresenting various different road adhesion coefficients, corresponding to different constraints and cost functions, establishing a performance index J as follows:
Figure FDA0002488313840000041
wherein N isp、NcRespectively prediction and control time domain, Q, R, P respectively corresponding weight matrix set, rho respectively weight coefficient set and relaxation factor set, xkIs a state variable, Δ ukD is the disturbance quantity for control increment;
the system constraints are:
Figure FDA0002488313840000042
Figure FDA0002488313840000043
wherein
Figure FDA0002488313840000044
At the point where k is 0, the position of the first electrode,
Figure FDA0002488313840000045
wherein, each item U in the formula is a control quantity set calculated in the first step of the initial stage of the control program under different attachment coefficients, and k is the step length.
6. The control method of the intelligent automobile under the variable adhesion coefficient repeatability track according to claim 5, characterized in that: constraint set H, G constrains the attachment coefficients; at U0The original expected path is tracked, and meanwhile, the emergency operation for the sudden change of the attachment coefficient is kept; hnAnd GnIs a constraint set with invariant attachment coefficients, where each term is a different constraint set corresponding to a different attachment coefficient, such as:
Figure FDA0002488313840000051
Vy，max＝Vxαr，sat+br (14)
as HnIs set of constraints, adopts
Figure FDA0002488313840000052
Figure FDA0002488313840000053
In the formula
Figure FDA0002488313840000054
Expressed as a function of the width of the road surface, as GnA constraint set of (2); e.g. of the typek，
Figure FDA0002488313840000055
Is the deviation of the centroid from the reference path, and deals with different attachment coefficients, such as α, for values in the constraintsr，sat、
Figure FDA0002488313840000056
Making an adjustment; h and G respectively adjust the yaw rate, the centroid slip angle and the environmental constraint and continue to track the original expected path after the event is finished.
7. The control method of the intelligent automobile under the variable adhesion coefficient repeatability track according to claim 6, characterized in that: if multiple adhesion coefficients are detected simultaneously, output results when the adhesion coefficients on the left side and the right side change are calculated simultaneously, and final results are obtained through final weighted summation; the expected path is always followed if no abrupt adhesion coefficients occur.
8. The control method of the intelligent automobile under the variable adhesion coefficient repeatability track according to claim 1, characterized in that: in step 3, after the model predictive control finishes the reaction of the corresponding emergency, an error response e is given to finish one circlejIn the case of (2), the learning steering input of the next round is determined
Figure FDA0002488313840000061
The iterative learning algorithm framework is as follows:
Figure FDA0002488313840000062
where Q is an NxN filter matrix and L is an NxN learning matrix, the matrices Q and L will be obtained by a quadratic optimal Q-ILC learning controller;
learning steering input
Figure FDA0002488313840000063
Determined by minimizing the quadratic cost function in the next iteration:
Figure FDA0002488313840000064
wherein
Figure FDA0002488313840000065
T, R, S is the weight matrix of N × N, the filter matrix and the iteration matrix are as follows:
Q＝(PTTP+R+S)-1(PTTP+S) (19)
L＝(PTTP+S)-1PTTP(T1/2P)-1T1/2(20)
9. the control method of the intelligent automobile under the variable adhesion coefficient repeatability track according to claim 1, characterized in that: in step 4, calculating a desired yaw rate, comparing the desired yaw rate with an actual yaw rate, and controlling an additional yaw moment according to a deviation value of the desired yaw rate, wherein the control mode is to reduce the driving torque of the outer side wheel or the inner side wheel so as to improve the driving stability of the vehicle;
the desired yaw rate is calculated as:
Figure FDA0002488313840000066
wherein L is the wheelbase, C2，C1The cornering stiffness of the front and rear wheels, respectively, K is the stability factor:
Figure FDA0002488313840000071
while in conjunction with road adhesion conditions, the desired yaw rate can be expressed as:
Figure FDA0002488313840000072
wherein mu is the road surface friction coefficient g and is the gravity acceleration.
The ideal value for yaw rate is expressed as:
Figure FDA0002488313840000073
the vehicle stability control is performed by controlling the yaw moment of the vehicle. The difference value between the ideal yaw rate and the actual yaw rate is obtained by measuring the actual yaw rate of the vehicle, and is as follows: e.g. of the typer＝rd-r; obtaining the value DeltaM of the additional yaw moment by a PID control strategyz(ii) a After the desired additional yaw moment is obtained by the above controller, a control strategy of proportional braking of the front and rear wheels is adopted from the viewpoint of vehicle braking, and the braking force required for the obtained additional yaw moment is proportionally distributed to the front and rear wheels.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112180731A (en) * 2020-10-13 2021-01-05 天津大学 Energy equipment operation control method and system
CN113093708A (en) * 2021-04-06 2021-07-09 哈尔滨理工大学 Multi-signal fusion hub motor automobile torque distribution system and prospective control method
CN113705865A (en) * 2021-08-16 2021-11-26 东南大学 Automobile stability factor prediction method based on deep neural network
CN114179818A (en) * 2021-12-31 2022-03-15 江苏理工学院 Intelligent automobile transverse control method based on adaptive preview time and sliding mode control

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104773170A (en) * 2015-04-28 2015-07-15 吉林大学 Stability integrated control method of vehicle
CN105172790A (en) * 2015-10-30 2015-12-23 吉林大学 Vehicle yaw stability control method based on three-step method
US20170139423A1 (en) * 2015-11-12 2017-05-18 King Fahd University Of Petroleum And Minerals Control system and method for multi-vehicle systems
CN106828464A (en) * 2017-01-06 2017-06-13 合肥工业大学 A kind of vehicle body stable control method and system based on coefficient of road adhesion estimation
CN109131325A (en) * 2018-08-15 2019-01-04 江苏大学 The three-dimensional of intelligent driving automobile can open up the pre- lane for taking aim at switching and keep control method
CN109808508A (en) * 2019-02-20 2019-05-28 武汉理工大学 A kind of drive system faults-tolerant control strategy of distributed-driving electric automobile
CN110539752A (en) * 2019-06-26 2019-12-06 江苏大学 Intelligent automobile multi-prediction-range model prediction trajectory tracking control method and system
CN111240327A (en) * 2020-01-16 2020-06-05 江苏大学 Intelligent vehicle iteration track tracking control method based on variable rate model

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104773170A (en) * 2015-04-28 2015-07-15 吉林大学 Stability integrated control method of vehicle
CN105172790A (en) * 2015-10-30 2015-12-23 吉林大学 Vehicle yaw stability control method based on three-step method
US20170139423A1 (en) * 2015-11-12 2017-05-18 King Fahd University Of Petroleum And Minerals Control system and method for multi-vehicle systems
CN106828464A (en) * 2017-01-06 2017-06-13 合肥工业大学 A kind of vehicle body stable control method and system based on coefficient of road adhesion estimation
CN109131325A (en) * 2018-08-15 2019-01-04 江苏大学 The three-dimensional of intelligent driving automobile can open up the pre- lane for taking aim at switching and keep control method
CN109808508A (en) * 2019-02-20 2019-05-28 武汉理工大学 A kind of drive system faults-tolerant control strategy of distributed-driving electric automobile
CN110539752A (en) * 2019-06-26 2019-12-06 江苏大学 Intelligent automobile multi-prediction-range model prediction trajectory tracking control method and system
CN111240327A (en) * 2020-01-16 2020-06-05 江苏大学 Intelligent vehicle iteration track tracking control method based on variable rate model

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112180731A (en) * 2020-10-13 2021-01-05 天津大学 Energy equipment operation control method and system
CN112180731B (en) * 2020-10-13 2024-05-31 天津大学 Energy equipment operation control method and system
CN113093708A (en) * 2021-04-06 2021-07-09 哈尔滨理工大学 Multi-signal fusion hub motor automobile torque distribution system and prospective control method
CN113093708B (en) * 2021-04-06 2023-03-21 哈尔滨理工大学 Multi-signal fusion hub motor automobile torque distribution test system and prospective control method
CN113705865A (en) * 2021-08-16 2021-11-26 东南大学 Automobile stability factor prediction method based on deep neural network
CN113705865B (en) * 2021-08-16 2024-05-03 东南大学 Automobile stability factor prediction method based on deep neural network
CN114179818A (en) * 2021-12-31 2022-03-15 江苏理工学院 Intelligent automobile transverse control method based on adaptive preview time and sliding mode control

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