CN105676643B - A kind of intelligent automobile turns to and braking self-adaptive wavelet base method - Google Patents

A kind of intelligent automobile turns to and braking self-adaptive wavelet base method Download PDF

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CN105676643B
CN105676643B CN201610117821.2A CN201610117821A CN105676643B CN 105676643 B CN105676643 B CN 105676643B CN 201610117821 A CN201610117821 A CN 201610117821A CN 105676643 B CN105676643 B CN 105676643B
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intelligent automobile
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CN105676643A (en
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郭景华
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Xiamen University
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    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

Abstract

A kind of intelligent automobile turns to and braking self-adaptive wavelet base method, belongs to automatic Pilot and intelligent transportation field.1) recognition methods of expected path is designed, intelligent automobile is established and turns to and brake Coupling Dynamic Model;2) it is turned to and brake coordination control module using contragradience sliding formwork control Technology design intelligent automobile for target with lateral direction of car path trace and retro-speed control;3) design fuzzy system approximating step 2 in real time online) in approach control rule, automatic adjusument restrain to approach control is realized, so as to weaken steering and brake chattering phenomenon caused by contragradience sliding formwork in Dynamic coordinated control;4) analysis intelligent automobile turns to and brakes the stability of dynamic coordination controlling system.Effectively overcome the characteristics such as Vehicular turn, the close coupling of Brake Dynamics, non-linear and parameter uncertainty, ensure the real-time and stability of urgent avoidance, eliminate the dependence to Controlling model, enhance the robustness to parameter uncertainty, overall performance is improved, reduces cost.

Description

A kind of intelligent automobile turns to and braking self-adaptive wavelet base method
Technical field
The invention belongs to automatic Pilots and intelligent transportation field, turn to and brake adaptive more particularly to a kind of intelligent automobile Answer control method for coordinating.
Background technology
Intelligent automobile is dedicated to improving the comprehensive performances such as safe, comfortable and energy saving of vehicle, is intelligent control, modern passes The embodiment of high and new technologies integrated applications in Vehicle Engineering such as sense, information communication.Being automatically performed for intelligent automobile driving task can The traffic capacity of road, the safety of enhancing vehicle traveling and comfort are greatlyd improve, effectively lowers the fuel consumption of vehicle Amount realizes environmental protection and energy saving, has extensive practical social application value.
Navigation Control is to realize the key link of intelligent automobile automatic Pilot, mainly studies and how to control vehicle along in real time The path of planning, speed traveling, and ensure driving safety, stationarity and the riding comfort of vehicle, decide keeping away for vehicle Barrier ability is the deciding factor for influencing intelligent automobile automatic Pilot quality.
Existing navigation control system, mostly using individually designed orthogonal auto-steering, braking control system.Document 1(Perez Joshue,etc.Cascade Architecture for Lateral Control in Autonomous Vehicles[J].IEEE Transactions on Intelligent Transportation Systems,2011,12 (1):73-82.) report that a kind of intelligent automobile turns to hierarchical control method.Document 2 (Wang Jianqiang, etc.An Adaptive Longitudinal Driving Assistance System Based on Driver Characteristics[J].IEEE Transactions on Intelligent Transportation Systems, 2013,14(1):12.) a kind of longitudinal brake control method based on driver characteristics self study is reported.However, urgent avoidance work Under condition, there are close coupling relationship between the steering of vehicle, Brake Dynamics, and with strong nonlinearity, Parameter uncertainties characteristic will Course changing control and control for brake separately consider to will likely result in steering wheel, brake operating frequent, it is difficult to ensure the reality of urgent avoidance When property and stability reduce the overall performance of intelligent automobile automatic Pilot.
Invention content
The purpose of the present invention is to solve the above-mentioned technical problems in the prior art, and vehicle can effectively be overcome by providing The characteristics such as steering, the close coupling of Brake Dynamics, non-linear and parameter uncertainty ensure the real-time and stabilization of urgent avoidance Property, the dependence to Controlling model is eliminated, enhances the robustness to parameter uncertainty, improves overall performance, reduces the one of cost Kind intelligent automobile turns to and braking self-adaptive wavelet base method.
The present invention includes the following steps:
1) recognition methods of expected path is designed, intelligent automobile is established and turns to and brake Coupling Dynamic Model;
2) with lateral direction of car path trace and retro-speed control for target, using contragradience sliding formwork control Technology design intelligence Motor turning and brake coordination control module;
3) design fuzzy system approximating step 2 in real time online) in approach control rule, realize oneself that restrain to approach control It adapts to adjust, so as to weaken chattering phenomenon caused by contragradience sliding formwork in steering and braking Dynamic coordinated control;
4) analysis intelligent automobile turns to and brakes the stability of dynamic coordination controlling system.
In step 1), the recognition methods of the design expected path establishes intelligent automobile and turns to and brake coupling power Learning the specific method of model can be:
(1) vehicle-borne CCD acquisition image, designs the intelligent automobile expected path real-time identification method of view-based access control model, quickly carries Take out the expected travel path in vehicle traveling front;
(2) computational intelligence automobile relative to expected path lateral deviation yeAnd azimuth deviationCurrent vehicle speed v is by vehicle-mounted Speed measuring instrumentation measures, desired speed vpIt is provided by vehicle planning instrument, calculates present speed deviation ve, and by position deviation, speed The signals such as deviation are loaded into vehicle-mounted microprocessor;
(3) lateral deviation, azimuth deviation and velocity deviation are determined as state variable, front wheel angle and brake pressure are in order to control Input quantity turns to intelligent automobile and brakes Coupled Dynamics system modelling.
It is described to be controlled with lateral direction of car path trace and retro-speed as target in step 2), using contragradience sliding formwork control Technology design intelligent automobile processed turns to and the specific method of brake coordination control module can be:
(1) derive that intelligent automobile turns to and brake the Equivalent control law of dynamic coordinate using back-stepping, it is ensured that vehicle Lateral position deviation, longitudinal velocity deviation and the convergence of motion state uniform bound cause perfect condition;
(2) it is restrained using the reaching condition of contragradience sliding formwork design approach control, for effectively overcoming intelligent automobile kinetic simulation The parameter uncertainty and external disturbance of type;
(3) the approach control rule in the Equivalent control law and step (2) in combining step (1), obtains Vehicular turn and system It is dynamic to coordinate control law.
In step 3), design fuzzy system approximating step 2 in real time online) in approach control rule, realize pair The automatic adjusument of approach control rule, so as to weaken chattering phenomenon caused by contragradience sliding formwork in steering and braking Dynamic coordinated control Specific method can be:
(1) using fuzzy logic, real-time simulation approach control restrains switching function online, and mould is determined using method of expertise The control rule of fuzzy logic is realized and the accurate of approach control switching function in steering and brake coordination control is approached;
(2) the adaptive fuzzy adjuster of gain is controlled in design approach control rule, it is online to adjust approach control rule in real time In gain coefficient;
(3) system control signal front wheel angle and brake pressure input vehicle-mounted microprocessor, steerable system Navigation Control mould The Dynamic coordinated control device effect of block so that current lateral deviation and velocity deviation level off to zero, and motion state levels off to expectation Value.
In step 4), the analysis intelligent automobile turns to and the specific side of the stability of braking dynamic coordination controlling system Method can be:
(1) Lyapunov functions are established, seek its time-derivative;
(2) design meets the condition of Lyapunov stability, realizes the Dynamic coordinated control for turning to and braking.
The solution have the advantages that:Using turning to and braking adaptive dynamic coordinate control method, effectively overcome Strong coupling, the interference caused by the factors such as nonlinear characteristic, Unmarried pregnancy, hence it is evident that improve of Vehicular turn and Brake Dynamics Control system performance and jitter is eliminated, improve precision, the reliability and stability of control system, guarantee is promptly kept away The real-time and stability of barrier, eliminate the dependence to Controlling model, enhance the robustness to parameter uncertainty, improve Overall performance reduces cost.
Description of the drawings
Fig. 1 is steering/braking self-adaptive wavelet base organization plan schematic diagram of the present invention.
Fig. 2 is the expected path identification process figure of the present invention.
Fig. 3 is path deviation information schematic diagram in acquisition image of the invention.
In figure:XOY is the coordinate system of longitudinal direction of car center line OY and lateral center of car line OX compositions;O is longitudinal direction of car The intersection point of center line OY and lateral center of car line OX;O1It is pre- take aim at a little;O1X1It was to take aim at a little and be parallel to lateral center of car in advance The straight line of line OX.
Fig. 4 is the vehicle preview kinematics schematic diagram of the present invention.
Fig. 5 be fuzzy logic approach in switching function input variable S (t) andMembership function schematic diagram.
Fig. 6 is that fuzzy logic approaches output variable u in switching functionFMSCMembership function schematic diagram.
Fig. 7 be in self adaptive control gain input variable e (t) andMembership function schematic diagram.
Fig. 8 is output variable λ in self adaptive control gainrMembership function schematic diagram.
Specific embodiment
It is derived as shown in Figure 1, the present invention is primarily based on contragradience sliding formwork control technology by Equivalent control law and approach control The steering of composition and brake coordination control law are restrained, the switching function of approach control rule is approached secondly by fuzzy logic, and is designed Self-adaptive regulator real-time adjusting control gain coefficient online.
The intelligent automobile that the present invention includes view-based access control model turns to and brakes Coupling Dynamic Model modeling process, coordinates control Design process, four part of control gain-adaptive design of Regulator process and stability analysis process.
Step 1:Using visual identity expected path, the intelligent automobile for establishing view-based access control model turns to and brakes Coupled Dynamics Model, specific steps include as follows
Step 1.1:Vehicle-borne CCD video camera is demarcated, acquires the environmental information in front in real time by CCD camera, As shown in Fig. 2, image filtering, edge detection, contours extract, Morphological scale-space, last march first are carried out to the image of acquisition Line fitting accurately identifies and extracts vehicle front expected travel path in real time, for point coordinates (xi, the y in imagei),i =0,1 ..., m, the calculation formula of expected path matched curve are:
In formula,For the basic function of linear independence, y (x) is matched curve, cj(j=0,1 ..., n) matched curve system Number.
Fitting coefficient c is determined using least square method0,c1,…,cnSo that following error sum of squares z is minimum:
Step 1.2:Make at the straight line intersection of image cross central line with taking aim at a little and being parallel in advance excessively in reference path curve Tangent line knows desired position information of the intelligent automobile relative to driving path, as shown in figure 3, yeVehicle at being taken aim in advance for vision Center line and the lateral deviation in path,Vehicle centre-line and the angle of path tangent line, fitting routines at being taken aim in advance for vision Curve and straight line O1X1Point of intersection P (x0,y0) lateral deviation and azimuth deviation calculation formula be:
Wherein, w1For the width of image, γ is pixel and actual range proportionality coefficient, KdFor tangent slope.
Step 1.3:The preview kinematics model of design description intelligent automobile relative position deviation change rate, as shown in figure 4, Using lateral deviation, azimuth deviation, velocity deviation, longitudinal velocity, lateral velocity and yaw velocity as state variable, establish containing not Determine that the intelligent automobile of factor and external disturbance turns to and brake Coupling Dynamic Model, it is as follows:
In formula,
Wherein, KLRepresent curvature, DLRepresent preview distance, veFor velocity deviation, vx、vpRespectively the actual speed of vehicle and Desired speed,Represent the yaw velocity of vehicle, ax、apThe respectively actual acceleration of vehicle and expectation acceleration, m is vehicle Quality, lfAnd lrWheelbase and barycenter are to the distance of front and back wheel, c respectively between front and back wheelxAnd cyRespectively vertical, horizontal air hinders Force coefficient, fRRepresent coefficient of rolling resistance, IzFor vehicle rotary inertia, CfAnd CrRespectively front and rear tire cornering stiffness, g are represented Acceleration of gravity, δfRepresent front wheel angle, PbRepresent brake pressure, KbRepresent brake pressure coefficient, rwFor radius of wheel, τ (Δx)、τ(Δy) andThe indeterminate respectively as caused by Unmarried pregnancy and time-varying parameter.
Step 2:With lateral direction of car path trace and retro-speed control for target, using contragradience sliding formwork control Technology design Dynamic coordinated control module is turned to and braked, determines that bounded control inputs δfAnd PbSo that lateral position deviation and velocity deviation have The accurate tracing control to expected path and retro-speed is realized in boundary's stable convergence, mainly includes following four step:
Step 2.1:The Equivalent control law of intelligent automobile steering and braking dynamic coordinate is derived using back-stepping, really It protects lateral direction of car position deviation, longitudinal velocity deviation and motion state uniform bound and converges to perfect condition, including:
Step 2.2.1:Define course changing control error signal s1:
s1=ye
Select Lyapunov functionsTime-derivative asked for along s1 to the function, and by lateral position deviation Variation rule substitutes into derived function
DefinitionFor virtual controlling input signal, to ensure derived functionIt can obtain virtually The expectation input quantity α of control1For:
a1=-k1s1+vy
Wherein k1For arithmetic number.
Define virtual error variance s2For:
Above formula is substituted into derived functionHave:
As steering and braking pressure control input quantity Pb、δfCause s2It converges on 0 or converges on a certain smaller value, can protect CardSo as to ensure s1Converge on 0 or uniform bound convergence.
Step 2.1.2:Select Lyapunov functions Vlat1
To above formula seeking time derivative, obtain:
It is assumed that:
Wherein k2For arithmetic number, to virtual error variance derivation, bring intoIt obtains:
It willAbove formula is substituted into, acquires steering and braking coupling control input quantity:
It can ensure that Lyapunov stability conditions are set up:
Wherein η1For s2Bounded nondeterministic function caused by derivation.
Step 2.1.3:Define control for brake error signal p1
p1=ve
Determine that Lyapunov functions are:
To above formula seeking time derivative:
Steering is obtained and braking coupling control input quantity is:
It can ensure Lyapunov stability conditionsIt sets up, that is, ensures p1Level off to zero.
Wherein l1For an arithmetic number, ξ1It is rightBounded nondeterministic function caused by derivation.
Step 2.1.4:Coordination that combining step 2.1.2 and step 2.1.3 are obtained control input, can obtain front wheel angle and The equivalent control of brake pressure:
In formula:PbeqIt is inputted for brake pressure equivalent control, δbeqIt is inputted for front wheel angle equivalent control.
To be used to compensate nondeterministic function ξ1、η1Nonlinear Damping Term, ζ1And ε1It is arithmetic number.
Step 2.2:Approach control rule is designed for overcoming intelligent automobile model parameter uncertainty and external disturbance, such as Under:
In formula, PbsControl input, δ are compensated for brake pressurefsControl input is compensated for front wheel angle, sign () is represented Switching function, λ1, λ2For arithmetic number.
Step 2.3:The approach control rule that the equivalent control and step 2.2 that combining step 2.1 is obtained are obtained, obtains steering and system Moving coordination control law is:
Wherein, TedAnd δfdRespectively desired braking pressure and expectation front wheel angle.
Step 3:The fuzzy system approach control rule in approximation step 2 in real time online is designed, realizes and approach control is restrained Self-adaptive regulator, so as to weaken steering and braking dynamic cooperation in chattering phenomenon caused by contragradience sliding formwork control.Adaptively Adjustment module design process is:
Step 3.1:With the handover module sign (p in fuzzy logic fitting approach control rule1) and sign (s2), approach control System rule can be written as:
Wherein, uFMSC() is the control output of fuzzy logic, and value is by normalized s2Withp1WithIt determines.
Define switching manifold S (t)=[p1 s2]T, select switching manifold S (t) and switching manifold derivativeFor fuzzy control Input variable,Output variable for fuzzy control.
Design input variable S andFuzzy subset's membership function for trapezoidal function and trigonometric function, as shown in Figure 5. The fuzzy subset's membership function for designing fuzzy control output variable uFMSC is monotropic function, as shown in Figure 6.
It is { NB, NM, NS, NZ, PS, PM, PB } that setting input variable and output variable, which correspond to fuzzy subset's linguistic variable, Wherein NB, NM, NS, NZ, PS, PM, PB are referred to as " negative big ", " in negative ", " negative small ", " zero ", " just small ", " center ", " just Greatly ".
The control rule of fuzzy sliding mode is determined using method of expertise, fuzzy logic is approached fuzzy control in switching function and advised Then table is as shown in table 1, and each fuzzy control rule obscures sentence by " IF-THEN " of following form and forms:
Wherein,WithFor the linguistic variable of input variable fuzzy subset, BiIt is the language change of output variable fuzzy subset Amount.I=1,2 ..., 49 represent the number of fuzzy control rule.For example, wherein a fuzzy control rule is represented by:
The control law represents system mode above switching manifold and far from switching manifold, therefore, controlling behavior to be negative big, Controlling behavior can make system mode rapidly return back to switching manifold.
Table 1
Fuzzy reasoning uses the max-min synthetic methods of Mamdani, carries out ambiguity solution operation using gravity model appoach, determines Nearly control law
Step 3.2:Design the control fader based on self-adapting fuzzy logic.Define bias vector e (t)=[ye ve]T, choose deviation e (t) and change of error restrainedFor the input variable of fuzzy logic, control gain λ is chosenr=diag { λ1, λ2Be fuzzy logic output variable.
Design input variable e (t) andFuzzy subset's membership function for trigonometric function, as shown in Figure 7.It designs defeated Go out variable λr=diag { λ12Fuzzy subset's membership function for trigonometric function, as shown in Figure 8.
The linguistic variable of input variable fuzzy subset is set for { NB, NM, NS, ZE, PS, PM, PB }, wherein NB, NM, NS, NZ, PS, PM, PB are referred to as " negative big ", " in negative ", " negative small ", " zero ", " just small ", " center ", " honest ".Setting output becomes The linguistic variable for measuring fuzzy subset is { VVS, VS, S, M, B, VB, VVB }, and wherein VVS, VS, S, M, B, VB, VVB are represented respectively " very small ", " smaller ", " small ", " in ", " big ", " larger ", " very big ".
Determine that the control of Self-adaptive fuzzy control gain is regular, fuzzy control rule in self adaptive control fader Then table is as shown in table 2, and each fuzzy control rule table is shown as following form:
Wherein,WithThe respectively linguistic variable of input variable fuzzy subset, FiLanguage for output variable fuzzy subset Say variable.I=1,2 ..., 49 represent the number of fuzzy control rule.
Fuzzy logic inference is carried out using the max-min methods of Mandani, carries out ambiguity solution operation with gravity model appoach, in real time Control gain λ is obtainedr
Table 2
Step 3.3:System control signal front wheel angle and brake pressure input vehicle-mounted microprocessor, and steerable system is controlled automatically The Dynamic coordinated control device effect of molding block so that current deviation levels off to zero, and motion state levels off to desired value.
Step 4:Based on Lyapunov Theory of Stability conditions, the validity and stability of authentication control method, analysis turns To the stability with brake coordination closed-loop control system:
Step 4.1:Select Lyapunov equations V=Vlat1+Vlong, derivation obtains:
Step 4.2:It will coordinate control law to substitute intoWithIt is obtainedTo the convergence of control system It is analyzed, whether system is stablized according to Lyapunov stability criterias:
If control system is stablized, the Dynamic coordinated control of steering and braking is realized.
If control system is unstable, return to step 2 redesigns the controller of system.
The above content is combine optimal technical scheme to the further description of the invention done.

Claims (4)

1. a kind of intelligent automobile turns to and braking self-adaptive wavelet base method, it is characterised in that includes the following steps:
1) recognition methods of expected path is designed, intelligent automobile is established and turns to and brake Coupling Dynamic Model, specific method For:
(1) vehicle-borne CCD acquisition image, designs the intelligent automobile expected path real-time identification method of view-based access control model, and rapid extraction goes out The expected travel path in vehicle traveling front;
(2) computational intelligence automobile relative to expected path lateral deviation yeAnd azimuth deviationCurrent vehicle speed v is by vehicular speeds Measuring instrument measures, desired speed vpIt is provided by vehicle planning instrument, calculates present speed deviation ve, and by position deviation, velocity deviation Signals is waited to be loaded into vehicle-mounted microprocessor;
(3) lateral deviation, azimuth deviation and velocity deviation are determined as state variable, front wheel angle and brake pressure input in order to control Amount turns to intelligent automobile and brakes Coupled Dynamics system modelling;
2) with lateral direction of car path trace and retro-speed control for target, using contragradience sliding formwork control Technology design intelligent automobile It turns to and brake coordination control module;
3) design fuzzy system approximating step 2 in real time online) in approach control rule, realize approach control is restrained it is adaptive It adjusts, so as to weaken chattering phenomenon caused by contragradience sliding formwork in steering and braking Dynamic coordinated control;
4) analysis intelligent automobile turns to and brakes the stability of dynamic coordination controlling system.
2. a kind of intelligent automobile turns to and brakes self-adaptive wavelet base method as described in claim 1, it is characterised in that in step It is rapid 2) in, it is described with lateral direction of car path trace and retro-speed control for target, using contragradience sliding formwork control Technology design intelligence Can the specific method of motor turning and brake coordination control module be:
(1) derive that intelligent automobile turns to and brake the Equivalent control law of dynamic coordinate using back-stepping, it is ensured that vehicle is horizontal It is restrained to position deviation, longitudinal velocity deviation and motion state uniform bound and causes perfect condition;
(2) it is restrained using the reaching condition of contragradience sliding formwork design approach control, for effectively overcoming intelligent automobile kinetic model Parameter uncertainty and external disturbance;
(3) the approach control rule in the Equivalent control law and step (2) in combining step (1), obtains Vehicular turn and braking is assisted Adjust control law.
3. a kind of intelligent automobile turns to and brakes self-adaptive wavelet base method as described in claim 1, it is characterised in that in step It is rapid 3) in, design fuzzy system approximating step 2 in real time online) in approach control rule, realize what approach control was restrained Automatic adjusument, so as to weaken steering and brake the specific method of chattering phenomenon caused by contragradience sliding formwork in Dynamic coordinated control For:
(1) using fuzzy logic, real-time simulation approach control restrains switching function online, and fuzzy patrol is determined using method of expertise The control rule collected is realized and the accurate of approach control switching function in steering and brake coordination control is approached;
(2) the adaptive fuzzy adjuster of gain is controlled in design approach control rule, it is online to adjust in real time in approach control rule Gain coefficient;
(3) system control signal front wheel angle and brake pressure input vehicle-mounted microprocessor, steerable system navigation control module Dynamic coordinated control device acts on so that current lateral deviation and velocity deviation level off to zero, and motion state levels off to desired value.
4. a kind of intelligent automobile turns to and brakes self-adaptive wavelet base method as described in claim 1, it is characterised in that in step It is rapid 4) in, the analysis intelligent automobile turns to and the specific method of stability of braking dynamic coordination controlling system is:
(1) Lyapunov functions are established, seek its time-derivative;
(2) design meets the condition of Lyapunov stability, realizes the Dynamic coordinated control for turning to and braking.
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