CN109318905A - A kind of intelligent automobile path trace mixing control method - Google Patents
A kind of intelligent automobile path trace mixing control method Download PDFInfo
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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
- 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
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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
- 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
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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
- 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 kind of intelligent automobile path trace mixing control methods, belong to intelligent vehicle crosswise joint technical field.Path trace mixing control method of the present invention comprises the steps of: step 1, establishes vehicle lateral control preview kinematics model under low speed；Step 2 establishes the lower vehicle lateral control kinetic model of high speed；Step 3, design path tracks mixture control, including Lateral Controller, fuzzy controller is stablized in monitor and switching, wherein Lateral Controller is based on PID control and Model Predictive Control designs, monitor determines tracing mode based on longitudinal speed, switches and stablizes fuzzy controller based on fuzzy control theory design.Path trace mixing control method proposed by the present invention improves easy implementation, the Stability and veracity of intelligent automobile path trace under the effective coordination high speed operation of intelligent automobile the problem of crosswise joint performance requirement.
Description
Technical field
The invention belongs to intelligent vehicle motion control fields, are related to a kind of intelligent automobile crosswise joint method, more particularly to
A kind of intelligent automobile path trace mixing control method.
Background technique
Transverse movement control is one of the key technology realizing intelligent automobile and independently travelling, wherein path trace passes through
Self-steering control vehicle is travelled along expected path always, while guaranteeing the driving safety and riding comfort of vehicle, is
Towards unpiloted ultimate aim.Intelligent automobile requires transverse movement control system to have essence under a wide range of driving cycle
Really, efficient and reliable control performance, however traditional single control algolithm can not effective coordination self-steering control system
Demand for control of the system under different operating conditions.At the same time, intelligent automobile self-steering system is higher to the requirement of real-time of control,
While traditional controller design is difficult to ensure steering behaviour under different operating conditions, moreover it is possible to so that controller design is simple, it is real to be easy
It is existing.
Accuracy, stability and the easy implementation angularly controlled from intelligent automobile transverse movement, different operating conditions
Should have different control target and emphasis, so that whole synthesis performance is optimal.For example, when vehicle is transported in low speed
When row operating condition, the kinematics characteristic of vehicle is more prominent, and in high-speed cruising operating condition, the kinetic characteristics of vehicle are to its own
Operating status be affected.The crosswise joint problem for having studied intelligent automobile using PID/feedback control method is taken aim in advance, the control
Algorithm has the advantages that design is simple, real-time is good, easy realization under speed operation, but is unable to satisfy intelligence under high-speed working condition
The reliability requirement of automobile path trace.Using the crosswise joint problem of model predictive control method research intelligent automobile, the calculation
Method predicts the output state in object future first, then the control action at current time is determined with this, i.e., first predicts to control again；By
There is certain predictability in it, so that it is under high-speed working condition, hence it is evident that first export the PID for feeding back control again afterwards better than traditional
Control system, and the control algolithm can constrain dynamics of vehicle under high-speed working condition, it not only can be quickly quasi-
Really tracking destination path, while having ensured the safety and stability of vehicle driving, but controller design is complex, it is whole
It is computationally intensive, realize that difficulty is relatively large.
Summary of the invention
In order to solve the above technical problems, the present invention provides a kind of intelligent automobile path trace hybrid control strategy, devise
It is a kind of to restrain the intelligent automobile path trace mixing switching control plan formed by taking aim at PID/feedback control law and Model Predictive Control in advance
Slightly.When speed is lower, it is contemplated that vehicle is under comparatively safe operating condition, and using calculating, simple and easily realizing pre- to take aim at PID anti-
Control law is presented, to improve the rapidity and easy implementation of path following control；And when speed is higher, it is contemplated that vehicle has strong
Non-linear, time-varying and the features such as unstable, then use Model Predictive Control Algorithm, to improve the safety of vehicle route tracking
Property, stability and control precision.In addition, band has also been devised and stablizes while identifying high low speed control mode by car speed
The handover mechanism of supervision had not only been able to satisfy system Partial controll performance, but also energy by introducing control algolithm appropriate in each operating condition
Achieve the purpose that global optimization, improves easy implementation, the Stability and veracity of intelligent automobile path trace.
To realize above-mentioned target, the technical scheme is that
A kind of intelligent automobile path trace mixing control method, comprising the following steps:
S1 establishes vehicle lateral control preview kinematics model under low speed；
S2 establishes the lower vehicle lateral control kinetic model of high speed；
S3, design path track mixture control, including fuzzy controller is stablized in Lateral Controller, monitor and switching,
Wherein Lateral Controller includes PID controller and model predictive controller again.
Further, the specific steps of the S1 are as follows:
S1.1 establishes vehicle lateral control preview kinematics model are as follows:
In formula, v is speed at vehicle centroid, and β is vehicle centroid side drift angle, and ω is yaw rate, Xc、YcRespectively
Cross, ordinate for position of the vehicle centroid in global earth coordinates,For the folder of longitudinal direction of car axis and axis of abscissas
Angle；
S1.2 calculates the lateral deviation at taking aim in advance and course deviation, table according to vehicle and the geometrical relationship of reference path
Up to formula are as follows:
In formula, xeIt takes aim at the distance between a little for vehicle under vehicle axis system and in advance, yeFor vehicle under vehicle axis system with take aim in advance
Lateral deviation between point,For vehicle under vehicle axis system and the pre- course deviation taken aim between a little, XfTo take aim in advance a little in the earth
Abscissa under coordinate system, YfTo take aim at the ordinate a little under earth coordinates in advance,To take aim in advance a little under earth coordinates
Course angle；
S1.3, the rule that velocity variations cause preview distance to be chosen are as follows:
xe=xe0+kv
In formula, xe0For vehicle under vehicle axis system and the pre- initial preview distance taken aim between a little, k is proportionality coefficient.
Further, vehicle lateral control kinetic model in the S2 are as follows:
In formula, m is vehicle mass, IzBe vehicle around the rotary inertia of z-axis, a, b be respectively mass center to axle away from
From δfFor vehicle front wheel angle,For Vehicular yaw angle, Ccf、CcrFor the cornering stiffness of tire before and after vehicle, Clf、ClrFor vehicle
The longitudinal rigidity of front and back tire, Sf、SrFor the slip rate of tire before and after vehicle, x, y are respectively the cross of the vehicle under vehicle axis system
Ordinate, X, Y are respectively the transverse and longitudinal coordinate of the vehicle under earth coordinates.
Further, the S3 specifically: Lateral Controller be by based on preview kinematics model foundation PID controller and
Based on the model predictive controller composition that kinetic model is established, monitor is to judge high low speed mould by identification car speed
Formula, switching and stablizing fuzzy controller is designed based on fuzzy control theory.
Further, the PID controller uses incremental timestamp algorithm, and the pid algorithm formula is as follows:
Δ u (k)=Kp[e(k)-e(k-1)]+Kie(k)+Kd[e(k)-2e(k-1)+e(k-2)]
U (k)=u (k-1)+Δ u (k)
In formula, it is assumed that sampling period T, then at the k moment, deviation is e (k)；KpFor proportionality coefficient, integral coefficientDifferential coefficientTiFor the time of integration, TdFor derivative time, u (k) is kth time sampling instant computer
Output, deviation e (k) be by lateral deviation yeAnd course deviationThe comprehensive deviation e obtained after nondimensionalization is handled, it is comprehensive
Input quantity of the deviation as PID controller is closed, output quantity u is front wheel angle δ.
Further, the specific establishment process of the model predictive controller are as follows:
A. in model predictive controller, quantity of state is chosenControl amount chooses u=[δ], establishes line
Property time-varying discrete model:
In formula, ξ (k) is the quantity of state after discretization, and y (k) is output quantity, and u (k) is control amount, and A (k), B (k) are discrete
Coefficient matrix after change, and A (k)=I+TA (t), B (k)=TB (t), T are the sampling period, I is unit matrix；
B. the predictive equation for deriving Model Predictive Control is as follows:
Predictive equation is a part important in Model Predictive Control, need to calculate the output of following a period of time system,
First above formula is converted into:
In formula, x (kt) is the matrix after conversion.
Obtain a new state-space expression:
Each matrix is defined as follows in formula:More than
Three is all the coefficient matrix predicted in time domain, and η (kt) is the system output predicted in time domain.
If the prediction time domain of system is Np, control time domain is Nc, wherein Nc≤Np, etching system exports when defining k are as follows:
Etching system inputs when defining k are as follows:
The output Y (k+1k) at system future k moment is expressed with a matrix type:
Y (k+1 | k)=ψkξ(k)+ΘkΔU(k)
ψ in formulakAnd ΘkIt is the coefficient matrix predicted in time domain, Δ U (k) is controlling increment matrix, and expression formula is as follows:
C. constraint condition is constructed, side slip angle constraint, slip angle of tire constraint and road surface attachment condition is joined and waits vehicles
Dynamic Constraints；
D. design object function are as follows:
In formula, and η (t+i | t)-ηr(t+i | t) is the difference of reality output and reference path, and ρ is weight coefficient, ε be it is loose because
Son, Q and R are weight matrix, and Δ u is controlling increment；
E. Optimization Solution, controller carry out the solution of Constrained Optimization in each control cycle:
After solving in each control cycle to above formula, the control sequence of the first front wheel angle of system is obtained, so
Afterwards again by first element interaction of the control sequence in actual system, until next sampling instant, and under
One sampling instant solves new control sequence according to new system measurement again.
Further, the course of work of the monitor are as follows: when speed is less than 50km/h, monitor recognizes vehicle driving
In speed operation, local-speed mode of operation is used at this time, and when speed is more than or equal to 50km/h, monitor recognizes vehicle driving and exists
High-speed working condition uses high-speed operation mode at this time.
Further, fuzzy controller is stablized in the switching specifically: switches and stablizes fuzzy controller to Model Predictive Control
The front wheel angle value δ of device and PID controller output1, δ2It is weighted processing, pressure limits its output amplitude, wherein λ1, λ2Point
The Lateral Controller output weighting coefficient for stablizing fuzzy controller output, the δ of mixture control final output Wei not switchedfAccording to
Formula δf=λ1δ1+λ2δ2It obtains, the acquisition of weighting coefficient is mainly reflected in the design of fuzzy rule.
The invention has the benefit that
It is steady by taking aim at PID/feedback control law and Model Predictive Control rule and switching with supervision in advance that the present invention provides a kind of
The intelligent automobile path trace mixing switching control strategy of cover half fuzzy controllers composition, effective coordination intelligent automobile high low speed work
Under condition the problem of crosswise joint performance requirement, by introducing control algolithm appropriate in each operating condition, it was both able to satisfy system part
Control performance, and can achieve the purpose that global optimization, improve the easy implementation, accuracy and stabilization of intelligent automobile path trace
Property.
Detailed description of the invention
Fig. 1 is hybrid control system block diagram of the invention.
Fig. 2 is coordinate transition diagram.
Fig. 3 is vehicle single track model.
Fig. 4 is to switch to stablize fuzzy controller.
Fig. 5 is the corresponding subordinating degree function of weighting coefficient.
Fig. 6 is emulation longitudinal velocity variation diagram of the invention.
Fig. 7 is simulation result diagram of the invention, and Fig. 7 (a) is lane-change path trace Contrast on effect result figure, and Fig. 7 (b) is side
To acceleration comparing result figure, Fig. 7 (c) is yaw velocity comparing result figure.
Fig. 8 is real train test result figure of the invention, and Fig. 8 (a) is lane-change path trace Contrast on effect result figure, Fig. 8 (b)
For side acceleration comparing result figure, Fig. 8 (c) is yaw velocity comparing result figure.
Specific embodiment
Describe implementation process of the invention in detail below in conjunction with technical solution and attached drawing:
The system performance difference that present invention combination intelligent automobile shows under low speed and high speed steering operating condition, first builds respectively
Vehicle lateral control preview kinematics model and the lower vehicle lateral control kinetic model of high speed under low speed have been found, institute is then based on
The kinetic model of foundation designs control strategy, and uses PID control in the low-speed mode, and then uses in high speed mode
Model Predictive Control, monitor determines path following control mode by car speed, and then designs with the switching for stablizing supervision
Stablize fuzzy controller, realizes smoothly switching for crosswise joint system, it is final to realize intelligent automobile path trace mixing control,
Hybrid control system block diagram is as shown in Figure 1, include Lateral Controller in the block diagram, fuzzy controller is stablized in monitor and switching.
S1 establishes vehicle lateral control preview kinematics model under low speed
S1.1 establishes vehicle kinematics model
Good road surface run at a low speed operating condition under, generally without the concern for dynamics problems such as vehicle stabilization controls,
Path following control device based on kinematics model design has reliable control performance, therefore vehicle lateral control is taken aim in advance under low speed
Kinematics model is established as follows:
The kinematics model of vehicle is as shown in Fig. 2, position coordinates of the vehicle centroid in global earth coordinates are (Xc,
Yc), the angle of longitudinal direction of car axis and axis of abscissas isFollowing vehicle kinematics equation is established with geometry principle:
In formula, v is speed at vehicle centroid, and β is vehicle centroid side drift angle, and ω is yaw rate.
S1.2 calculates the lateral deviation at taking aim in advance and course deviation according to vehicle and the geometrical relationship of reference path
Vehicle axis system oxy and earth coordinates OXY transformational relation are as shown in Fig. 2, set in the road ahead that vehicle is taken aim in advance
Certain point OfCoordinate under earth coordinates is (Xf, Yf), this is pre- to take aim at point OfThe aircraft pursuit course tangential direction and the earth at place are sat
The angle of mark system axis of abscissas isAngle with vehicle axis system axis of abscissas isMiddle geometrical relationship can will be pre- according to fig. 2
Take aim at point OfPosition under earth coordinatesBe converted to the position under vehicle axis systemConversion
Relationship is as follows:
In formula, xeIt takes aim at the distance between a little for vehicle under vehicle axis system and in advance, yeFor vehicle under vehicle axis system with take aim in advance
Lateral deviation between point,For vehicle under vehicle axis system and the pre- course deviation taken aim between a little, XfTo take aim in advance a little in the earth
Abscissa under coordinate system, YfTo take aim at the ordinate a little under earth coordinates in advance,To take aim in advance a little under earth coordinates
Course angle.
S1.3, the rule that velocity variations cause preview distance to be chosen are as follows:
Speed is changeable when in view of vehicle driving, and the selection of preview distance is affected to taking aim at tracking effect in advance, works as vehicle
When speed is lower, the information that biggish preview distance will lead to vehicle front road does not utilize well；Work as car speed
When higher, the information that lesser preview distance will lead to part future trajectory is lost, so that path following control effect is made to be deteriorated,
Therefore the selection rule of preview distance is as follows:
xe=xe0+kv (3)
In formula, xe0For vehicle under vehicle axis system and the pre- initial preview distance taken aim between a little, k is proportionality coefficient.
S2 establishes vehicle dynamic model are as follows:
Intelligent vehicle more travels in complicated traffic environment at a relatively high speed, in order to improve intelligent vehicle in high speed row
Reliability when sailing, it is necessary to more accurate vehicle dynamic model is introduced in the controller, therefore lower vehicle lateral control at a high speed
Kinetic model is established as follows:
Intelligent vehicle is in the process of path trace, inherently along with the variation of longitudinal direction of car speed, lateral speed
Variation and the variation of yaw velocity establish that there are vertical, horizontal couplings for this purpose, when carrying out Full Vehicle Dynamics Modeling
Simple and effective vehicle single track model.Due to the present invention primarily to research vehicle tracking expected path has preferably tracking
Precision and riding stability, influence of the vehicle suspension characteristic for Vehicular system are ignored；And based on the mesh for reducing calculation amount
The Dynamic Constraints of vehicle are simplified.Therefore, the present invention first proposes following hypothesis when carrying out Dynamic Modeling:
(1) assume that vehicle is travelled always on flat road surface；
(2) vehicle and suspension system are rigid, and ignore the catenary motion of vehicle；
(3) vehicle movement is described with single track model, does not consider the left and right transfer of load；
(4) assume that tire working in linear region, ignores the longitudinal and lateral coupling relationship of tire force；
(5) ignore vertically and horizontally aerodynamics；
(6) ignore steering system, the input of course changing control is front wheel angle δf。
To sum up, the present invention has finally built the three degree of freedom including longitudinal movement, transverse shifting, horizontal swing
Vehicle single track model, schematic diagram are as shown in Figure 3:
According to Newton's second law, vehicle centroid is respectively obtained along x-axis, y-axis and around the stress balance equation of z-axis are as follows:
In formula, m is vehicle mass, IzBe vehicle around the rotary inertia of z-axis, a, b be respectively mass center to axle away from
From FcfAnd FcrLateral force suffered by tire, F respectively before and after vehiclelfAnd FlrLongitudinal force suffered by tire, δ respectively before and after vehiclef
For vehicle front wheel angle,For Vehicular yaw angle；F in Fig. 3xfAnd FxrRespectively tire power suffered by the direction x before and after vehicle, Fyf
And FyrIt is tire before and after vehicle in the direction y institute stress.
According to it is assumed that vehicle tyre work in linear region, at this time side drift angle and straight skidding rate it is smaller and lateral plus
Speed ay≤ 0.4g, the longitudinal force and lateral force of tire may be expressed as:
In formula, Ccf, CcrFor the cornering stiffness of tire before and after vehicle；Clf, ClrFor the longitudinal rigidity of tire before and after vehicle；Sf,
SrFor the slip rate of tire before and after vehicle.
There are more trigonometric function in the vehicle dynamic model established by formula (4), for model simplification have compared with
Big difficulty, it is assumed that vehicle front wheel angle and slip angle of tire are smaller, and following approximation relation can be used:
cosθ≈1,sinθ≈θ,tanθ≈θ (6)
Finally consider the transformational relation between vehicle body coordinate system and earth coordinates, and brings simplification above result into formula
(4) after, vehicle non-linear dynamic model is obtained:
In formula, m is vehicle mass, IzBe vehicle around the rotary inertia of z-axis, a, b be respectively mass center to axle away from
From δfFor vehicle front wheel angle,For Vehicular yaw angle, Ccf、CcrFor the cornering stiffness of tire before and after vehicle, Clf、ClrFor vehicle
The longitudinal rigidity of front and back tire, Sf、SrFor the slip rate of tire before and after vehicle, x, y are respectively the cross of the vehicle under vehicle axis system
Ordinate, X, Y are respectively the transverse and longitudinal coordinate of the vehicle under earth coordinates.
S3 designs Lateral Controller
S3.1 designs the PID controller based on preview kinematics model foundation
Based on the vehicle lateral control preview kinematics modelling PID controller that front is established, in PID controller
The proportional COEFFICIENT K of parameterP, integral coefficient KI, differential coefficient KD, K is found in real train testPAnd KDTwo parameters are to intelligent vehicle
Path trace has larger impact: biggish Proportional coefficient KPIntelligent automobile can be improved in the follow-up capability in bend path, still
Linear road is easy to appear reforming phenomena；Biggish differential coefficient KDIntelligent automobile can be allowed to enter bend in advance, followed out good
Good enters detour diameter, and linear road shows unstable or even easy drift off the runway.
Conventional pid algorithm formula is as follows:
In formula, u is control amount, KpFor proportionality coefficient, KiFor integral coefficient, KdFor differential coefficient, e (t) is deviation.
Because computer control system is time discrete control system, need to carry out sliding-model control: this hair to pid algorithm
Bright to use incremental timestamp algorithm, the pid algorithm formula is as follows:
Δ u (k)=Kp[e(k)-e(k-1)]+Kie(k)+Kd[e(k)-2e(k-1)+e(k-2)] (9)
U (k)=u (k-1)+Δ u (k) (10)
In formula, it is assumed that sampling period T, then at the k moment, deviation is e (k)；KpFor proportionality coefficient, integral coefficientDifferential coefficientTiFor the time of integration, TdFor derivative time, u (k) is kth time sampling instant computer
Output, deviation e (k) be by lateral deviation yeAnd course deviationThe comprehensive deviation e obtained after nondimensionalization is handled, it is comprehensive
Input quantity of the deviation as PID controller is closed, output quantity u is front wheel angle δ.
Nondimensionalization handles and merge to deviation as follows:
In formula,Respectively nondimensionalization treated lateral deviation and course deviation；yemax、yeminIt is respectively horizontal
To the maximum value and minimum value of deviation；The respectively maximum value and minimum value of course deviation；E is composition error；n
For weight coefficient.
S3.2 designs the model predictive controller established based on kinetic model
It is as follows to establish linear time-varying model by S3.2.1:
Model Predictive Control Algorithm is used under the higher speed of vehicle, introduces vehicle dynamic model in the controller, with
Accurate kinetic model can be improved controller and estimate ability to vehicle future behaviour as prediction model, so as to
The control precision of vehicle route tracking is improved, but traditional model predictive controller solves control amount presence under higher speed
Slow-footed problem, therefore the present invention uses explicit model PREDICTIVE CONTROL is existed Optimization Solution by the thought of parametric programming
Line computation is put into offline progress, to improve the speed in line computation so as to guaranteeing the rapidity controlled under higher speed
And real-time, while intelligent vehicle is stringenter to the requirement of real-time of controller when running at high speed, non-linear mould predictive
Control is difficult to meet.Compared to Nonlinear Model Predictive Control algorithm, the line using linear time-varying model as prediction model is used
Property time-varying model predictive control algorithm, calculate it is relatively easy, real-time is preferable, so as to improve control rapidity and in real time
Property.
In model predictive controller, quantity of state is chosenControl amount chooses u=[δ], below to S2
The non-linear vehicle dynamic model established uses the linearization technique for state trajectory to carry out linearization process, obtains line
Property time-varying variance are as follows:
WhereinC=(0,0,0,0,1,0)T, the above three is all coefficient matrix；Y is output
Amount.
Sliding-model control is carried out using the method for single order difference coefficient to formula (14), obtains discrete state control expression formula:
In formula, ξ (k) is the quantity of state after discretization, and y (k) is output quantity, and A (k), B (k) are the coefficient square after discretization
Battle array, and A (k)=I+TA (t), B (k)=TB (t), T are the sampling period, I is unit matrix.
After introducing incremental model, state control table reaches formula are as follows:
Δ ξ (k) is the increment of quantity of state in formula, and Δ u (k) is the increment of control amount.
It is as follows to derive Model Predictive Control predictive equation by S3.2.2:
Based on linear state space model, the predictive equation of Model Predictive Control is derived, it can by predictive equation
To calculate quantity of state and output quantity of the system in prediction time domain.
Predictive equation is a part important in Model Predictive Control, need to calculate the output of following a period of time system.
First formula (15) is converted into:
In formula, x (k | t) is the matrix after conversion.
An available new state-space expression:
Each matrix is defined as follows in formula:More than
Three are all the coefficient matrix in predicting time domain, and η (k | t) is the system output in prediction time domain.
If the prediction time domain of system is Np, control time domain is Nc, wherein Nc≤Np, etching system exports when defining k are as follows:
Etching system inputs when defining k are as follows:
The output Y at system future k moment (k+1 | k) is expressed with a matrix type:
Y (k+1 | k)=ψkξ(k)+ΘkΔU(k) (21)
ψ in formulakAnd ΘkIt is the coefficient matrix predicted in time domain, Δ U (k) is controlling increment matrix, and expression formula is as follows:
S3.2.3, building constraint condition are as follows
The present invention not only allows for the constraint of control amount and controlling increment when designing a model predictive controller, it is also contemplated that
To vehicle under higher speed, dynamics constraint condition is more stringenter than under low speed, and the present invention is added to some dynamics of vehicle
Constraint, the Dynamic Constraints including vehicles such as side slip angle constraint, slip angle of tire constraint and road surface attachment conditions, passes through this
A little constraints can further support vehicles traveling safety, stability and comfort.
A. side slip angle constrains
Side slip angle is larger to the stability influence of vehicle, it is therefore necessary to increase side slip angle constraint.According to grinding
Study carefully display, attachment the good dry bituminous pavement of condition on, vehicle stabilization traveling the side slip angle limit can achieve ±
12 °, and on the poor ice and snow road of attachment condition, limiting value is approximately ± 2 °.Therefore the present invention to normal vehicle operation when, matter
Heart side drift angle needs to do following constraint:
- 12 ° 12 ° of < β < (good road surface) (22)
- 2 ° 2 ° of < β < (ice and snow road) (23)
B. slip angle of tire constrains
If slip angle of tire is excessive, tire adhesion force is easy to reach limit of adhesion, so that vehicle is easy sliding, can lose
Stability.According to the cornering behavior of tire it is found that side drift angle and lateral deviation power are at approximately linear when slip angle of tire is no more than 5 °
Relationship.The low-angle constraint proposed when according to front construction force model, sets front-wheel side drift angle constraint condition are as follows:
- 3 ° of < αf3 ° of < (24)
C. adhere to constraint
The power performance of automobile is also influenced by coefficient of road adhesion, and when road surface attachment condition is preferable, the factor is to vehicle
Traveling influences little；When condition is more severe, then can the comfort of dynamic property and passenger to vehicle have an impact.Work as vehicle
On road surface when driving, the longitudinal acceleration a of vehicley, side acceleration ax, there are following relationships by coefficient of road adhesion μ:
So far, all constraints are included in the solution procedure of quadratic programming.
S3.2.4, design object function are as follows:
It since the complexity of vehicle dynamic model is higher, while also joined many Dynamic Constraints, therefore controlling
In device practical implementation processed, it is more likely that the case where appearance can not calculate optimal solution at the appointed time.Therefore, this hair
Bright to joined relaxation factor ε in design object function, the expression formula for obtaining objective function is as follows:
In formula, and η (t+i | t)-ηr(t+i | t) is the difference of reality output and reference path.ρ is weight coefficient, ε be relaxation because
Son, Q and R are weight matrix, and Δ u is controlling increment.Expression formula first item reflect system to the follow-up capability of desired trajectory, second
Requirement of the item reflection system to control amount smooth change, expression formula generally function are to enable a system to before the deadline fastly
Speed smoothly tracks upper desired trajectory.
S3.2.5, Optimization Solution:
The constraint condition and objective function established according to front, controller need to carry out belt restraining in each control cycle
The solution of optimization problem:
After being solved in each control cycle to formula (27), the first control sequence of system is obtained, then again should
First element interaction of control sequence is in actual system, until next sampling instant, and in next sampling
Moment solves new control sequence according to new system measurement again.
S4, designing supervision device:
Switch longitudinal speed that index is vehicle in the present invention, since the switching point of high low speed generally sets 45-55km/h, because
This setting switching speed is 50km/h, and when speed is less than 50km/h, monitor recognizes vehicle driving in speed operation, at this time
Using local-speed mode of operation, when speed is more than or equal to 50km/h, monitor recognizes vehicle driving in high-speed working condition, adopts at this time
Use high-speed operation mode.
S5, design switches stable fuzzy controller, and detailed process is as follows:
S5.1 switches and stablizes the front wheel angle value δ that fuzzy controller exports model predictive controller and PID controller1,
δ2It is weighted processing, pressure limits its output amplitude, wherein λ1, λ2Respectively switch the transverse direction for stablizing fuzzy controller output
Controller exports weighting coefficient, mixture control final output front wheel angle δfIt is obtained according to formula (28).In handoff procedure, two
A weighting coefficient works at the same time, and after finishing switching, one of weighting coefficient is 1, another is 0, to prevent from controlling
The large jump exported when pattern switching along with controller, causes system disturbance and transient response, to realize crosswise joint
System smoothly switches and stablizes supervision.
δf=λ1δ1+λ2δ2 (28)
S5.2, switching and stablizing fuzzy controller output is that Lateral Controller exports weighting coefficient, switches and stablizes fuzzy control
The input of device: desired output corresponding to the value of feedback and destination path exported as carsim auto model in Fig. 1 makes the difference, and adopts
It is current defeated for high low speed switching control process with current output and the difference of target output and the change rate of difference
The output of previous controller is indicated out, and target exports the output of controller after then indicating handoff procedure, controller is defeated
Weighting coefficient 1 refers to that the output weighting coefficient of previous controller, controller output weighting coefficient 2 refer to the control that will be worked out
Device processed exports weighting coefficient, establishes switching shown in Fig. 4 and stablizes fuzzy controller structure chart.
The basic domain of S5.3, controller input quantity output bias e be [- 40,40], obscure domain be -2, -1,0,1,
2 }, corresponding fuzzy subset is { NB, NS, ZO, PS, PB }, output bias change rate deBasic domain be [- 28,28], obscure
Domain is { -1,0,1 }, and corresponding fuzzy subset is { N, ZO, P }, and input quantity is all made of Gaussian subordinating degree function:
In formula, σ indicates that the width of subordinating degree function, c indicate the center of subordinating degree function.
There are two the output quantities of controller, is Lateral Controller output weighting coefficient, therefore the basic domain of the two is equal
For [0,1], obscuring domain is { 0,1,2,3 }, and corresponding fuzzy subset is { ZO, PS, PM, PB }, wherein NB, NM, NS, ZO, PS,
PM, PB, N, P are referred to as negative big, bear, bear it is small, zero, just small, center is honest, bear, just；Output quantity is all made of Fig. 5 discrete type
Triangleshape grade of membership function.
Rule is controlled using method of expertise ambiguity in definition, control rule is as shown in table 1,2:
Table 1, which switches, stablizes fuzzy controller output 1 control rule table of weighting coefficient
Table 2, which switches, stablizes fuzzy controller output 2 control rule table of weighting coefficient
Representative situation is lifted to be illustrated fuzzy control rule:
1) when output difference is positive greatly, difference change rate is timing, and current output is larger with target output bias at this time, and
And difference has increase tendency, in order to guarantee the continuity of system switching, controller output weighting coefficient 1 is answered larger, and controller is defeated
Weighting coefficient 2 should be smaller out；
2) when output difference is negative greatly, difference change rate is timing, and current output is larger with target output bias at this time, and
And difference has increase tendency, for the purposes of guaranteeing the continuity of system switching, controller output weighting coefficient 1 also answers larger, control
Device output weighting coefficient 2 processed should be smaller；
3) when output difference is zero, when difference change rate is also zero, current output at this time and target output are very close to and poor
Value variation is stablized, and illustrates that handoff procedure is near completion, and the weighting coefficient 1 of controller output at this time is zero, controller output weighting system
Number 2 is because maximum；
4) when output difference is negative small, when difference change rate is negative, current output is smaller with target output bias at this time, and
Gap is constantly reducing, and in order to guarantee the continuity of system switching, controller output weighting coefficient 1 answers moderate, controller output
Weighting coefficient 2 is also answered moderate.
Anti fuzzy method algorithm of the present invention uses common gravity model appoach, and gravity model appoach is to take fuzzy membership function curve and horizontal seat
The center of gravity of the area surrounded is marked as controller final output numerical value.
The path trace curve that the present invention designs is a lane-change path, expression formula are as follows:
In formula, d is the lateral displacement that lane-change completes rear vehicle, and l is the length travel that lane-change completes rear vehicle, and the present invention takes
D is 4m, and l is 100 meters.
Fig. 6 is a kind of emulation longitudinal velocity variation diagram that the present invention designs, and vehicular longitudinal velocity is with abscissa X variation relation
As shown in fig. 6, vehicle first slows down during vehicle lane-changing, further accelerate, finally drives at a constant speed.
Fig. 7 is a kind of intelligent automobile path trace hybrid control strategy simulation result diagram of the present invention, and Fig. 7 (a) is lane-change road
Diameter tracking effect comparing result figure, Fig. 7 (b) are side acceleration comparing result figure, and Fig. 7 (c) is yaw velocity comparing result
Figure.The path trace mixing control that the present invention that Fig. 7 (a) shows designs has better path trace than single PID controller
Effect, wherein maximum deviation is 0.043m, it can be seen that the path of vehicle actual travel can track destination path, vehicle well
It is also easy to produce little deviation in bend, but can eliminated quickly.Fig. 7 (b)~Fig. 7 (c) shows vehicle during lane-change, without steady
Surely the path trace mixing control for switching monitoring controller is easy to happen unexpected shake, side acceleration and yaw velocity variation
It is precipitous, it is unstable.And what the present invention designed has with the path trace mixing control for stablizing supervision switch controller than single
PID control have more minor swing, and side acceleration and yaw velocity variation it is relatively steady, be both at safe range it
It is interior, illustrate that the hybrid control strategy that the present invention designs can control vehicle in path tracking procedure in good stable shape
State.Lane-change operating condition simulation result shows that the hybrid control strategy can control vehicle under longitudinal high speed and low speed not only
Can be with the tracking destination path of accurate stable, and the switching of the smooth steady between two kinds of control algolithms may be implemented, reach good
Good control effect.
Fig. 8 is a kind of intelligent automobile path trace hybrid control strategy real train test result figure of the present invention, and Fig. 8 (a) is to change
Path tracking effect comparing result figure, Fig. 8 (b) are side acceleration comparing result figure, and Fig. 8 (c) is yaw velocity comparison
Result figure.Lane-change operating condition real train test the result shows that, which can control vehicle route tracing deviation in ± 0.15m
In range, side acceleration size is controlled within the scope of ± 0.28g/s, and yaw velocity size is controlled in ± 2.5 °/s.This hair
The intelligent automobile path trace hybrid control strategy of bright design can control the tracking destination path of vehicle fast and stable, and have
There is preferable tracking accuracy, obtains better control effect relative to single PID control.
A kind of intelligent automobile path trace hybrid control strategy provided by the present invention is described in detail above, with
The upper only present pre-ferred embodiments, are merely to illustrate design philosophy and feature of the invention, are not limited to this hair
Bright, all any modification, equivalent replacement, improvement and so under technical thought of the invention should be included in protection of the invention
Within the scope of.
Claims (8)
1. a kind of intelligent automobile path trace mixing control method, which comprises the following steps:
S1 establishes vehicle lateral control preview kinematics model under low speed；
S2 establishes the lower vehicle lateral control kinetic model of high speed；
S3, design path track mixture control, including fuzzy controller is stablized in Lateral Controller, monitor and switching, wherein
Lateral Controller includes PID controller and model predictive controller again.
2. a kind of intelligent automobile path trace mixing control method according to claim 1, which is characterized in that the S1's
Specific steps are as follows:
S1.1 establishes vehicle lateral control preview kinematics model are as follows:
In formula, v is speed at vehicle centroid, and β is vehicle centroid side drift angle, and ω is yaw rate, Xc、YcRespectively vehicle
Cross, the ordinate of position of the mass center in global earth coordinates,For the angle of longitudinal direction of car axis and axis of abscissas；
S1.2 calculates the lateral deviation at taking aim in advance and course deviation, expression formula according to vehicle and the geometrical relationship of reference path
Are as follows:
In formula, xeIt takes aim at the distance between a little for vehicle under vehicle axis system and in advance, yePoint is taken aim at pre- for vehicle under vehicle axis system
Between lateral deviation,For vehicle under vehicle axis system and the pre- course deviation taken aim between a little, XfTo take aim in advance a little in geodetic coordinates
Abscissa under system, YfTo take aim at the ordinate a little under earth coordinates in advance,To take aim at the course a little under earth coordinates in advance
Angle；
S1.3, the rule that velocity variations cause preview distance to be chosen are as follows:
xe=xe0+kv
In formula, xe0For vehicle under vehicle axis system and the pre- initial preview distance taken aim between a little, k is proportionality coefficient.
3. a kind of intelligent automobile path trace mixing control method according to claim 1, which is characterized in that in the S2
Vehicle lateral control kinetic model are as follows:
In formula, m is vehicle mass, IzIt is vehicle around the rotary inertia of z-axis, a, b are respectively distance of the mass center to axle, δfFor
Vehicle front wheel angle,For Vehicular yaw angle, Ccf、CcrFor the cornering stiffness of tire before and after vehicle, Clf、ClrFor vehicle front and back wheel
The longitudinal rigidity of tire, Sf、SrFor the slip rate of tire before and after vehicle, x, y are respectively that the transverse and longitudinal of the vehicle under vehicle axis system is sat
Mark, X, Y are respectively the transverse and longitudinal coordinate of the vehicle under earth coordinates.
4. a kind of intelligent automobile path trace mixing control method according to claim 1, which is characterized in that the S3 tool
Body are as follows: Lateral Controller is by the PID controller based on preview kinematics model foundation and the mould established based on kinetic model
Type predictive controller composition, monitor are that high low-speed mode is judged by identification car speed, switch and stablize fuzzy controller
It is to be designed based on fuzzy control theory.
5. a kind of intelligent automobile path trace mixing control method according to claim 4, which is characterized in that the PID
Controller uses incremental timestamp algorithm, and the pid algorithm formula is as follows:
Δ u (k)=Kp[e(k)-e(k-1)]+Kie(k)+Kd[e(k)-2e(k-1)+e(k-2)]
U (k)=u (k-1)+Δ u (k)
In formula, it is assumed that sampling period T, then at the k moment, deviation is e (k)；KpFor proportionality coefficient, integral coefficientIt is micro-
Divide coefficientTiFor the time of integration, TdFor derivative time, u (k) is the output of kth time sampling instant computer, deviation
Value e (k) is by lateral deviation yeAnd course deviationThe comprehensive deviation e obtained after nondimensionalization is handled, comprehensive deviation conduct
The input quantity of PID controller, output quantity u are front wheel angle δ.
6. a kind of intelligent automobile path trace mixing control method according to claim 4, which is characterized in that the model
The specific establishment process of predictive controller are as follows:
A. in model predictive controller, quantity of state is chosenControl amount chooses u=[δ], when establishing linear
Become discrete model:
In formula, ξ (k) is the quantity of state after discretization, and y (k) is output quantity, and u (k) is control amount, and A (k), B (k) is after discretizations
Coefficient matrix, and A (k)=I+TA (t), B (k)=TB (t), T are the sampling period, and I is unit matrix；
B. the predictive equation for deriving Model Predictive Control is as follows:
Predictive equation is a part important in Model Predictive Control, need to calculate the output of following a period of time system, first will
Above formula is converted into:
In formula, x (kt) is the matrix after conversion；
Obtain a new state-space expression:
Each matrix is defined as follows in formula:The above three
It is all the coefficient matrix predicted in time domain, η (k | t) is the system output in prediction time domain；
If the prediction time domain of system is Np, control time domain is Nc, wherein Nc≤Np, etching system exports when defining k are as follows:
Etching system inputs when defining k are as follows:
The output Y (k+1k) at system future k moment is expressed with a matrix type:
Y (k+1 | k)=ψkξ(k)+ΘkΔU(k)
ψ in formulakAnd ΘkIt is the coefficient matrix predicted in time domain, Δ U (k) is controlling increment matrix, and expression formula is as follows:
C. constraint condition is constructed, joined the vehicles such as side slip angle constraint, slip angle of tire constraint and road surface attachment condition
Dynamic Constraints；
D. design object function are as follows:
In formula, and η (t+i | t)-ηr(t+i | t) is the difference of reality output and reference path, and ρ is weight coefficient, and ε is relaxation factor, Q
It is weight matrix with R, Δ u is controlling increment；
E. Optimization Solution, controller carry out the solution of Constrained Optimization in each control cycle:
After solving in each control cycle to above formula, the control sequence of the first front wheel angle of system is obtained, then again
By first element interaction of the control sequence in actual system, until next sampling instant, and next
Sampling instant solves new control sequence according to new system measurement again.
7. a kind of intelligent automobile path trace mixing control method according to claim 1, which is characterized in that the supervision
The course of work of device are as follows: when speed is less than 50km/h, monitor recognizes vehicle driving in speed operation, uses low speed at this time
Operating mode, when speed is more than or equal to 50km/h, monitor recognizes vehicle driving in high-speed working condition, uses high speed work at this time
Operation mode.
8. a kind of intelligent automobile path trace hybrid control strategy according to claim 1, which is characterized in that the switching
Stablize fuzzy controller specifically: switch and stablize the front-wheel that fuzzy controller exports model predictive controller and PID controller
Corner value δ1, δ2It is weighted processing, pressure limits its output amplitude, wherein λ1, λ2It is defeated respectively to switch stable fuzzy controller
Lateral Controller out exports weighting coefficient, the δ of mixture control final outputfAccording to formula δf=λ1δ1+λ2δ2It obtains, weighting system
Several acquisitions are mainly reflected in the design of fuzzy rule.
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