CN113547931A - Yaw stability control system and method for unmanned carrier - Google Patents

Yaw stability control system and method for unmanned carrier Download PDF

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CN113547931A
CN113547931A CN202110896484.2A CN202110896484A CN113547931A CN 113547931 A CN113547931 A CN 113547931A CN 202110896484 A CN202110896484 A CN 202110896484A CN 113547931 A CN113547931 A CN 113547931A
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yaw
vehicle
gamma
control
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刘玮
张庆杰
万益东
刘萍
俞跃
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Yancheng Institute of Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
    • B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2220/00—Electrical machine types; Structures or applications thereof
    • B60L2220/40—Electrical machine applications
    • B60L2220/42—Electrical machine applications with use of more than one motor
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2220/00—Electrical machine types; Structures or applications thereof
    • B60L2220/40—Electrical machine applications
    • B60L2220/44—Wheel Hub motors, i.e. integrated in the wheel hub
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/10—Vehicle control parameters
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/10—Vehicle control parameters
    • B60L2240/12—Speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/42—Drive Train control parameters related to electric machines
    • B60L2240/423—Torque
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/72—Electric energy management in electromobility

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  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

The invention discloses a yaw stability control system of an unmanned carrying vehicle, which comprises an upper layer controller and a lower layer controller electrically and mechanically connected with the upper layer controller; the upper layer controller comprises a yaw velocity controller, a mass center lateral deviation controller, a development joint controller and a sliding mode controller which are in electromechanical connection with the unmanned transport vehicle; the sliding mode controller is connected with the yaw angular speed controller, the mass center lateral deviation controller and the extension joint controller; the lower layer controller is a driving torque distribution controller and is in electromechanical connection with a hub motor of the unmanned transport vehicle. The invention greatly improves the yaw stability of the unmanned carrying vehicle under the working condition of the deterioration center.

Description

Yaw stability control system and method for unmanned carrier
Technical Field
The invention relates to a rollover prevention control system and method for an unmanned transport vehicle, and belongs to the technical field of control systems.
Background
The distributed driving electric automobile can effectively relieve the environmental protection and safety problems in the development of automobile industry, and becomes a popular carrier for a plurality of scholars at home and abroad to research the stability control of the automobile. The intelligent drive-by-wire and dynamic control device has the advantages of short drive chain, high drive efficiency and compact structure, and provides more possibilities for comprehensive intelligent drive-by-wire and dynamic control of the chassis.
Four-wheel independent drive independent steering is one of the main forms of distributed drive, compared with the traditional vehicle, the four-wheel independent drive independent steering optimally distributes the torque of each driving wheel, fully utilizes the road adhesion, changes the steering directions of the front wheel and the rear wheel at different speeds, and improves the safety and the operation stability of the vehicle. The stability of the vehicle steering driving has certain influence on the safety of the vehicle, and the control system of the four-wheel drive four-wheel steering electric vehicle has higher integration degree and stronger complexity than the traditional vehicle, so that domestic and foreign researchers carry out a great deal of research on the yaw stability of the four-wheel independent steering vehicle and obtain some achievements.
However, the main object of the above research is a traditional automobile with unchanged center of mass, and the application scene is a barrier-free and wide road. The problem that the yaw stability of the unmanned carrying vehicle is influenced by insufficient steering or excessive steering during steering due to the fact that the mass center of the unmanned carrying vehicle is changed due to irregular goods placing positions and unbalanced weight under the factory environment with limited motion space and strict vehicle speed requirement is not considered, and how to improve the yaw stability of the unmanned carrying vehicle under the working condition of high mass center is the problem needing to be considered and solved.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a yaw stability control system and method for an unmanned transport vehicle, so as to solve the technical problem that the yaw stability of the unmanned transport vehicle is poor under the working condition of a deterioration center in the prior art.
In order to solve the technical problem, the invention is realized by adopting the following scheme:
the invention provides a yaw stability control system of an unmanned carrying vehicle, which comprises an upper layer controller and a lower layer controller electrically and mechanically connected with the upper layer controller; the upper layer controller comprises a yaw velocity controller, a mass center lateral deviation controller, a development joint controller and a sliding mode controller which are in electromechanical connection with the unmanned transport vehicle; the sliding mode controller is connected with the yaw angular speed controller, the mass center lateral deviation controller and the extension joint controller; the lower layer controller is a driving torque distribution controller and is in electromechanical connection with a hub motor of the unmanned transport vehicle.
The invention also provides the unmanned carrier, and the yaw stability control system is adopted to control the yaw stability.
The invention also provides a control method of the unmanned carrier yaw stability control system, which comprises the following steps: establishing an ideal reference model and a dynamic model of the unmanned transport vehicle; inputting the target vehicle speed and the steering angle into an ideal reference model and a dynamic model, and calculating theoretical values gamma and beta and actual values gamma and beta of the yaw velocity and the centroid sideslip angle; the upper controller inputs gamma, beta, gamma and beta, a yaw rate controller, a mass center lateral deviation controller and an extensible combined controller to output calculated yaw moments delta M gamma, delta M beta, xi 1 delta M gamma and xi 2 delta M beta, wherein xi 1 and xi 2 are weights calculated in the extensible combined controller and occupied by the yaw rate controller and the mass center lateral deviation controller respectively; calculating by a sliding mode controller to obtain a final additional yaw moment delta Mz, and inputting the final additional yaw moment delta Mz to a lower layer controller; and a control domain module in the lower layer controller receives the delta Mz and gamma, beta, gamma and beta input by the upper layer controller to carry out control domain judgment according to the vehicle state, and distributes the driving torque to the hub motor of the unmanned transport vehicle through constraint conditions.
Preferably, the automated guided vehicle dynamics model includes:
longitudinal motion equation:
Figure RE-GDA0003261368570000021
Figure RE-GDA0003261368570000031
wherein m is the automated guided vehicle mass; v. ofxIs the longitudinal acceleration; v. ofyIs the transverse velocity; gamma is a yaw angular velocity; fxiAnd Fyi(i ═ fl, fr, rl, rr) are the tire longitudinal and lateral forces, respectively; deltafl、δfrRespectively the left and right steering angles of the front wheels; deltarl、δrrThe rear wheel left and right steering angle.
The transverse motion equation:
Figure RE-GDA0003261368570000032
in the formula, vyAcceleration in the lateral direction; v. ofxThe longitudinal speed of the mass center under the vehicle body coordinate system.
Yaw motion equation:
Figure RE-GDA0003261368570000033
in the formula IzMoment of inertia about the z-axis for the automated guided vehicle; gamma is yaw angular acceleration; l isfAnd LrDistances from the center of mass to the front and rear axes, respectively; d1、d2The distance from the left wheel and the right wheel to the equivalent wheel respectively.
Differential equation of wheel motion:
Figure RE-GDA0003261368570000034
in the formula, JωIs the rotational inertia of the wheel; omegaiIs the wheel rotational angular acceleration; t isdiIs the input torque of the wheel; fxiIs the tire lateral force; and R is the effective rolling radius of the wheel.
The method for establishing the tire model based on the magic formula comprises the following steps:
Figure RE-GDA0003261368570000035
wherein y (x) may be a lateral force or a longitudinal force; the independent variable x may represent the slip angle or the longitudinal slip ratio of the tire, respectively; the coefficient B, C, D, E in the formula is in turn determined by the vertical load and camber angle of the tire, where B is the stiffness factor; c is a curve shape factor; d is a crest factor; and E is a curve curvature factor.
Preferably, the ideal reference model of the automated guided vehicle selects a zero-centroid slip angle, and the ideal reference model is as follows:
Figure RE-GDA0003261368570000041
in the formula:
xd＝[βd ωr]T (12)
Figure RE-GDA0003261368570000042
Figure RE-GDA0003261368570000043
ud＝[δfl δfr δrl δrr]T (15)
wherein: kfFront wheel cornering stiffness; krIs rear wheel cornering stiffness; i iszIs horizontal swinging moment of inertia; and a and b are distances from the center of mass of the whole vehicle to the front and rear shafts respectively.
Namely, it is
Figure RE-GDA0003261368570000044
In the formula: tau isωIs the inertial link time constant; gωThe steady state gain of yaw rate versus turning angle is modeled for the ideal vehicle.
Figure RE-GDA0003261368570000045
In the formula: k is a steering characteristic stability factor of the vehicle.
Wherein, the yaw velocity of the vehicle needs to be ensured to satisfy:
|ω_rd|≤|0.85μg/v_x| (18)
in the formula: μ is a road surface adhesion coefficient.
Preferably, the yaw-rate controller inputs a difference Δ γ between the ideal yaw-rate and the predicted yaw-rate and a change rate of the difference using a combination of model predictive control and fuzzy control
Figure RE-GDA0003261368570000051
Outputting an additional yaw moment Δ Mγ。
Preferably, the centroid yaw angle controller combines model prediction control and fuzzy control, and inputs a difference value delta beta between an ideal yaw rate and a predicted yaw rate and a change rate of the difference value
Figure RE-GDA0003261368570000052
Outputting an additional yaw moment Δ Mβ。
Preferably, the control method of the scalable unified controller includes the following steps:
selecting a difference value delta gamma between gamma predicted by the MPC model and a theoretical value gamma calculated by an ideal reference model and a centroid slip angle actual value beta as control quantities, and dividing a vehicle driving state into a stable domain, a single control domain and a joint control domain; selecting a MPC model to take a numerical value beta predicted by an actual centroid sideslip angle as an abscissa, and taking a difference delta gamma between an ideal yaw velocity gamma obtained by the actual yaw velocity prediction numerical value gamma and a linear 2-DOF model as an ordinate to form a two-dimensional extension set; adopting a tolerance zone division method for the vertical coordinate yaw angular speed deviation to obtain the ranges of a stable domain, a single control domain and a joint control domain; converting the two-dimensional extension set into a one-dimensional extension set to calculate an extension distance and construct a correlation function; determining joint control weights, ξ, when in the stability domain1＝0,ξ 20; in the single control domain, xi1＝1,ξ20; xi when in the Joint control Domain1Absolute calculated value of the correlation function/100 ξ21-correlation function absolute calculation value/100.
Preferably, the control method of the sliding mode controller includes the following steps: constructing a switching surface; selecting an index approach rate; and calculating an additional yaw moment delta Mz by combining the exponential approach rate, and inputting the additional yaw moment delta Mz to a lower layer controller.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention adopts a control strategy combining model prediction control, sliding mode control and an extension theory, the upper layer is an additional yaw moment solving layer, and the compensation moment is calculated when the unmanned carrying vehicle turns; the lower layer is a driving force distribution layer, the compensation moment solved by the upper layer controller is reasonably distributed to the four wheels, and the yaw stability control of the unmanned carrying vehicle at low speed is guaranteed.
2. The invention also effectively demonstrates the practical effectiveness and the obvious progress of the unmanned carrier yaw stability control system and the unmanned carrier yaw stability control method through simulation and experimental comparison.
Drawings
FIG. 1 is a flow chart of an automated guided vehicle yaw stability control system according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of a model predictive fuzzy extension controller control according to an embodiment of the present invention;
FIG. 3 is a control domain partition diagram of a method for controlling yaw stability of an automated guided vehicle according to an embodiment of the present invention;
FIG. 4 is a diagram of a dynamics model of an automated guided vehicle according to an embodiment of the present invention;
FIG. 5 is a membership function of a fuzzy subset of yaw-rate controller inputs provided by an embodiment of the present invention;
FIG. 6 is a membership function of a fuzzy subset of yaw-rate controller outputs provided by an embodiment of the present invention;
fig. 7 is a diagram of a fuzzy control law for a yaw-rate controller according to an embodiment of the present invention;
FIG. 8 is a membership function of an input fuzzy subset of a centroid cornering angle controller according to an embodiment of the present invention;
FIG. 9 is a graph of membership functions of the centroid sideslip angle controller output fuzzy subsets as provided by an embodiment of the present invention;
FIG. 10 is a diagram of fuzzy control rules for a centroid cornering angle controller according to an embodiment of the present invention;
FIG. 11 is a one-dimensional extension set according to an embodiment of the present invention;
FIG. 12 is a graph showing the response of yaw rate and centroid yaw angle in a low-speed state according to an embodiment of the present invention;
fig. 13 is a response diagram of yaw rate and centroid yaw angle under the low-speed steering experiment provided by the invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art through specific situations.
Example 1
As shown in fig. 1, an automated guided vehicle yaw stability control system employs hierarchical control, which is divided into upper and lower layers. The upper layer controller is used for solving an additional yaw moment required by yaw stability control and consists of a yaw velocity controller, a mass center lateral deviation controller, an extension joint controller and a sliding mode controller; the lower controller is a driving torque distribution controller which reasonably distributes the additional yaw moment obtained by the upper controller to four driving wheels after the additional yaw moment is restrained. The yaw stability control system of the automated guided vehicle provided by the embodiment of the invention improves the yaw stability of the automated guided vehicle at a low speed.
Example 2
The invention also provides the automatic guided vehicle, and the yaw stability control system of the automatic guided vehicle is adopted to control the yaw stability, so that the yaw stability control of the automatic guided vehicle at a low speed is ensured.
Example 3
The stability of the unmanned transport vehicle is mainly determined by transverse motion and yaw motion, so that the built vehicle model only needs to consider three degrees of freedom of longitudinal motion, transverse motion and yaw motion. The established dynamic model is used for model prediction control, and is subjected to specification on the basis of describing a vehicle dynamic process so as to reduce the calculation amount of a control algorithm. In modeling vehicle dynamics, the following idealized assumptions are made. Firstly, assuming that the unmanned transport vehicle runs on a flat road surface, and neglecting vertical movement; secondly, only considering the pure lateral deviation characteristic of the tire, neglecting the coupling of transverse and longitudinal tire forces; then the lateral load transfer of the tire is not considered; finally, the influence of the transverse and longitudinal aerodynamics on the yaw characteristic of the unmanned transport vehicle is neglected. Based on the above assumptions, a yaw dynamics model can be obtained, as shown in fig. 4. O is the instant center; CG is the centroid; l is the distance from the front axle to the rear axle; MZ is the yaw moment; β is the sideslip angle of the centroid.
Based on the assumption, a yaw dynamics model can be obtained, the stress analysis is carried out on the unmanned transport vehicle, and a three-degree-of-freedom motion equation is obtained according to the stress balance and the moment balance as follows:
longitudinal motion equation:
Figure RE-GDA0003261368570000081
wherein m is the automated guided vehicle mass; v. ofxIs the longitudinal acceleration; v. ofyIs the transverse velocity; gamma is a yaw angular velocity; fxiAnd Fyi(i ═ fl, fr, rl, rr) are the tire longitudinal and lateral forces, respectively; deltafl、δfrRespectively the left and right steering angles of the front wheels; deltarl、δrrThe rear wheel left and right steering angle.
The transverse motion equation:
Figure RE-GDA0003261368570000082
in the formula, vyAcceleration in the lateral direction; v. ofxThe longitudinal speed of the mass center under the vehicle body coordinate system.
Yaw motion equation:
Figure RE-GDA0003261368570000083
in the formula IzMoment of inertia about the z-axis for the automated guided vehicle; gamma is yaw angular acceleration; l isfAnd LrDistances from the center of mass to the front and rear axes, respectively; d1、d2The distance from the left wheel and the right wheel to the equivalent wheel respectively.
Considering the dynamic characteristics of the driving of the wheel, taking a single driving wheel as an example, neglecting the friction force, the differential equation of the wheel motion can be written as
Figure RE-GDA0003261368570000084
In the formula, JωIs the rotational inertia of the wheel; omegaiIs the wheel rotational angular acceleration; t isdiIs the input torque of the wheel; fxiIs the tire lateral force; and R is the effective rolling radius of the wheel.
Fitting the tire test data by using a magic formula, and completely expressing the longitudinal force and the lateral force of the tire and the combined working condition of the longitudinal force and the lateral force by using a set of formulas with the same form. Based on the magic formula, the expression formula of the tire model is established as
Figure RE-GDA0003261368570000085
Wherein y (x) may be a lateral force or a longitudinal force; the independent variable x may represent the slip angle or the longitudinal slip ratio of the tire, respectively; the coefficient B, C, D, E in the formula is in turn determined by the vertical load and camber angle of the tire, where B is the stiffness factor; c is a curve shape factor; d is a crest factor; and E is a curve curvature factor.
The hub motor adopts a brushless direct current motor, the research is mainly focused on a control strategy of the yaw stability of the four-wheel independent steering vehicle, and the following protocols are required to be carried out on a model of the hub motor. Firstly, the eddy current loss and the hysteresis loss of the motor are ignored, and the saturation of the iron core of the motor is ignored; secondly, the distribution of the air gap magnetic field is approximately trapezoidal wave with a flat top angle of 120 degrees; then, the cogging effect is neglected, and the armature conductors are uniformly distributed on the surface of the armature; and finally, regarding the freewheeling diode and the power tube as ideal elements.
The voltage equation of the hub motor is as follows:
Figure RE-GDA0003261368570000091
Pe＝eAiA+eBiB+eCic (7)
Pe＝Teomega (8) is obtained from the formulas (7) and (8)
Figure RE-GDA0003261368570000092
The motor has the equation of motion of
Figure RE-GDA0003261368570000093
In the formula, TeIs an electromagnetic torque; omega is the mechanical angular speed of the motor; t islIs the load torque; j is the rotor moment of inertia; b isvIs a viscous friction coefficient; u. ofa、ubAnd ucABC three-phase winding voltage respectively; i.e. ia、ibAnd icABC three-phase current respectively; e.g. of the typeA、eBAnd eCABC three-phase counter potentials are respectively; l is the self-inductance of the phase winding; m is the phase winding mutual inductance.
When the unmanned carrying vehicle turns, the barycenter slip angle is expected to be as small as possible, so that the barycenter slip angle is ideally zero. The linear two-degree-of-freedom model can effectively reflect the linear relation between the yaw angular velocity and the side slip angle of the mass center and the steering angle, and can be used as an ideal reference model. The ideal reference model of the automated guided vehicle can be expressed as
Figure RE-GDA0003261368570000101
In the formula:
xd＝[βd ωr]T (12)
Figure RE-GDA0003261368570000102
Figure RE-GDA0003261368570000103
ud＝[δfl δfr δrl δrr]T (15)
wherein: kfFront wheel cornering stiffness; krIs rear wheel cornering stiffness; i iszIs horizontal swinging moment of inertia; and a and b are distances from the center of mass of the whole vehicle to the front and rear shafts respectively.
Namely, it is
Figure RE-GDA0003261368570000104
In the formula: tau isωIs the inertial link time constant; gωThe steady state gain of yaw rate versus turning angle is modeled for the ideal vehicle.
Figure RE-GDA0003261368570000105
In the formula: k is a steering characteristic stability factor of the vehicle.
Considering that different adhesion coefficients of the road surface can limit the tire force, the yaw rate of the vehicle needs to be ensured to meet the requirement
Figure RE-GDA0003261368570000106
In the formula: μ is a road surface adhesion coefficient.
As shown in fig. 1, the method for controlling the yaw stability of the automated guided vehicle comprises the following specific steps: inputting the target vehicle speed and the steering angle into an ideal reference model and a dynamic model, and solving the theoretical values gamma of the yaw rate and the centroid slip angle*,β*And the actual values γ, β. Upper level controller input gamma*、β*Gamma, beta, yaw rate controller, barycenter offset controller and extension controller output respectively solved yaw moment delta Mγ，△Mβ，ξ1△Mγ+ξ2△MβIn which ξ1，ξ2The weights of the yaw angular velocity controller and the centroid yaw angle controller are solved from the extension joint controller respectively. The final additional yaw moment Delta M is obtained through sliding mode controlz. The control domain module in the lower layer controller receives the delta M input by the upper layer controllerzAnd gamma*、β*Gamma and beta, judging the control domain according to the vehicle state, and converting T into T according to the constraint conditiondi(i ═ fl, fr, rl, rr) to fourA hub motor.
The model prediction control is combined with a fuzzy control theory and an extension theory to form the fuzzy extension control based on the model prediction, and a control flow chart is shown in fig. 2.
Firstly, MPC predicts the values gamma, beta and the theoretical value gamma*、β*The error delta gamma and delta beta between the two are respectively used as the input quantities of the yaw angular velocity fuzzy controller and the centroid yaw angular velocity fuzzy controller, and the delta gamma and the beta are used as the input quantities of the extension joint controller. Secondly, outputting the lower layer controller through sliding mode control, and then adding an additional yaw moment delta M through the lower layer controllerzAnd finally, the MPC refreshes and optimizes the predicted value of the model state until the optimal control is realized.
As shown in fig. 2, the automated guided vehicle can be divided into three states by the theory of extension when turning at a low speed, which are: a stability domain, a single control domain and a joint control domain. In the stable region, the unmanned carrier operates stably without control; in the single control domain, the unmanned transport vehicle gradually becomes unstable or has a tendency of instability, and the yaw rate controller starts to work; when the unmanned transport vehicles are in the joint control domain, the unmanned transport vehicles are unstable, and the yaw rate controller and the mass center lateral deviation angle controller work simultaneously, wherein the control weight is determined by an extension theory.
When the unmanned transport vehicle transports goods in a factory building, the randomness of the placing positions and the weights of the goods causes that the yaw angular speed of the vehicle is larger or smaller, so that the vehicle body is unstable when the vehicle turns. The controller corrects the yaw rate by combining model predictive control and fuzzy control. Fuzzy subset membership functions of the input and the output are shown in fig. 5 and fig. 6. The yaw rate controller fuzzy control rule is as shown in fig. 7.
The fuzzy subset membership functions input and output by the yaw rate controller adopt triangular functions, and the input quantity is the difference value delta gamma between the ideal yaw rate and the predicted yaw rate and the change rate of the difference value
Figure RE-GDA0003261368570000121
Output added yaw moment△Mγ. Wherein the abscissa is the domain of discourse and the ordinate is the degree of membership. Determining the input discourse domain of [ -66 ] through multiple times of simulation experiment data according to the system model]The output universe of discourse is [ -33 [ ]]The fuzzy sets are [ NB, NM, NS, ZO, PS, PM, PB]。
When the unmanned transport vehicle transports goods between two stations of a factory, due to the randomness of the placing positions of the goods, the unmanned transport vehicle generates understeer and oversteer in the low-speed steering process and deviates from an expected path. The controller corrects the centroid sideslip angle by combining model prediction control and fuzzy control. Fuzzy subset membership functions of input and output are shown in fig. 8 and 9. The fuzzy control rule of the centroid cornering angle controller is shown in figure 10.
The fuzzy subset membership functions input and output by the yaw angular velocity controller adopt double S-shaped functions, and the input quantity is the difference value delta beta of the ideal centroid slip angle and the predicted centroid slip angle and the change rate of the difference value
Figure RE-GDA0003261368570000122
Output as additional yaw moment DeltaMβ. Wherein the abscissa is the domain of discourse and the ordinate is the degree of membership. Determining the input discourse domain of [ -66 ] through multiple times of simulation experiment data according to the system model]The output universe of discourse is [ -33 [ ]]The fuzzy sets are [ NB, NM, NS, ZO, PS, PM, PB]。
The implementation of the extensible combined control is to determine the size of the extensible set, i.e. the stability boundary when the automated guided vehicle is traveling. And secondly, realizing the distribution of weights of the yaw rate and the centroid slip angle control in the joint control domain. The method comprises the following steps of establishing the extensible joint controller.
First, the control amount is selected. The yaw rate and the centroid slip angle are main performance indicators for evaluating the running stability of the vehicle. The yaw rate represents the stability of the vehicle during steering, and the centroid slip angle shows the deviation degree of the actual running track of the vehicle from the expected path. The difference value delta gamma between gamma predicted by the MPC model and a theoretical value gamma calculated by an ideal reference model and the actual value beta of the centroid slip angle are selected as control quantities, and the vehicle running state is divided into a stable domain, a single control domain and a joint control domain.
And secondly, set partitioning. And selecting the MPC model to take the numerical value beta predicted by the actual mass center slip angle as an abscissa, and taking the difference delta gamma between the numerical value gamma predicted by the actual yaw rate and the ideal yaw rate gamma calculated by the linear 2-DOF model as an ordinate to form an extension set. Wherein, a tolerance zone division method is adopted for the vertical coordinate and horizontal swing angular speed deviation: (ii) a stable region | [ Delta ] gamma1|<|ξ1γ*L, |; ② single control field | xi1γ*|≤|△γ|≤|ξ2γ*L, |; ③ associated control field Deltagamma ray emitting>|ξ2γ*L. In the formula, xi1、ξ2Constant, take 0.05 and 0.15, respectively. And determining the centroid side slip angle stable region according to whether the yaw velocity gain is in a linear region, gradually increasing the steering angle corner input when the control is not applied, fitting the relation between the extreme value of the linear region corner and the vehicle speed, and inputting the relation into a two-free model to obtain a stable boundary beta 1. A large number of simulations show that when the centroid slip angle is 1.45 beta 1, the vehicle stability can be obviously improved by controlling the yaw angular speed, and the corresponding centroid slip angle value is beta 2. And determining that the limit value of the centroid slip angle is | beta | ═ arctan (0.02 μ g) |, when the vehicle is in the instability state, wherein μ is the road adhesion coefficient, and setting the value of the centroid slip angle at the moment to be β 3.
In summary, the stable region range is
Figure RE-GDA0003261368570000131
A single control domain range of
Figure RE-GDA0003261368570000132
The range of the joint control domain is
Figure RE-GDA0003261368570000133
Then a correlation function is constructed. The origin O (0, 0) is the optimal point in the extension set, a point Q of the single control domain is taken, two points Q, O are connected and extended, and the point Q is intersected with the boundary of the stable domain and the joint control domain1、Q2、Q3、 Q4As in fig. 5. The line segment OQ is the shortest distance of Q close to the optimal point, and the two-dimensional extension set is converted into the one-dimensional extension set to calculate the extension distance, where the one-dimensional extension set is shown in fig. 11.
Make the stable domain set as XwThe set of single control domains is XdThe extension distance from the point Q to the stable domain is rho (Q, X)w) And the extension distance from the point Q to the single control domain is rho (Q, X)d) The extension distance from the point to the control domain is different along with the position of the Q point. Take the extension distance from point Q to the single control domain as an example
Figure RE-GDA0003261368570000134
Correlation function
Figure RE-GDA0003261368570000141
In the formula, D (Q, X)d,Xw)＝ρ(Q,Xd)-ρ(Q,Xw)
And finally determining the joint control weight. When the trolley is in a stable region, the trolley runs stably without additional control, and xi is1＝0,ξ 20; when the trolley is in the single control domain, the trolley is gradually unstable, the trolley can be recovered to stably run only by independently applying yaw angular speed control, and xi is realized at the moment1＝1,ξ 20; when the trolley is in the joint control domain, the trolley is unstable, the yaw angular speed control and the mass center slip angle control need to be applied simultaneously, and then xi is1＝|K(S)|/100,ξ2＝1-|K(S)|/100。
The sliding mode control firstly selects sliding mode surfaces, different switching surface control effects are different, the research in the text is to simultaneously control yaw angular velocity and mass center slip angle, so the following switching surfaces are constructed:
s＝ξ1(γ-γd)+ξ2(β-βd) (21)
the control mode can not only track the ideal yaw velocity quickly, but also ensure that the centroid side deviation angle is not far away from the theoretical value, and ensure the operation stability when the vehicle is turned to run.
Sliding mode control is theoretically a perfect control method, but the generated buffeting phenomenon makes it difficult to apply in practice. The sliding mode control of the exponential approaching law is adopted, the sliding mode switching surface is gradually converged from the starting point, and the buffeting phenomenon of the sliding mode control is effectively reduced. The constructed approach law is as follows.
Selecting an index approach rate:
Figure RE-GDA0003261368570000142
in the formula, the value of k1 represents the approaching speed of the system state moving to the sliding mode surface s being 0 under the sliding mode control; the value of k2 represents the convergence speed of the system state after reaching the sliding mode surface and moving to the balance point; sgn (.) is a sign function.
Derivation of formula (21) to obtain
Figure RE-GDA0003261368570000143
Additional moment of body stability is
ΔMZ＝lf(Fxflsinδfl+Fxfrsinδfr)+lr[Fxrlsinδrl+Fxrrsinδrr] -d1[Fxflcosδfl+Fxrlcos(δrl)]+d2[Fxfrcos(δfr)+Fxrrcos(δrr)]
(24)
Can be converted into
Figure RE-GDA0003261368570000151
By substituting formula (25) for formula (23)
Figure RE-GDA0003261368570000152
Additional torque can be obtained by combining the exponential approach rate
Figure RE-GDA0003261368570000153
The obtained additional yaw moment is reasonably distributed to four driving wheels, and the balance between the driving force of each wheel and the additional yaw moment is
Figure RE-GDA0003261368570000161
Mf＝iMZ；Mr＝(1-i)MZ (29)
Figure RE-GDA0003261368570000162
In the formula, Mf，MrAdditional moments for the front and rear wheels, respectively; i is an additional moment adjusting coefficient; j is a power regulation coefficient; t isdThe driving torque of the whole vehicle is obtained; r is the tire radius.
The longitudinal forces of the 4 wheels can be determined from the above constraints and the determined additional yaw moment:
Figure RE-GDA0003261368570000163
converting the longitudinal force into a driving torque:
Figure RE-GDA0003261368570000164
in the formula: i is the rotational inertia of the wheel; fxiIs four-wheel driving force.
In addition, in order to verify the effectiveness of the control method, combined simulation is carried out on the basis of Carsim and Matlab/Simulink, the whole vehicle dynamic model and the ideal reference model which are established in the Carsim environment are established, the hub motor and controller model are established in the Simulink, and the control effect of the control strategy on the yaw stability of the intelligent vehicle under the low-speed steering working condition is verified. And (3) selecting a step working condition which is easy to have instability under the low-speed steering working condition for simulation analysis, wherein the parameters of the whole vehicle are shown in the table 1.
The method comprises the steps that a low-speed steering driving working condition is established in a complete vehicle dynamic model, the longitudinal speed is 25Km/h, the ground adhesion coefficient is 0.3, after an intelligent vehicle normally drives for 3s at a constant speed, the intelligent vehicle is influenced by a step signal of 20 degrees (about 0.349rad), the body of the unmanned transport vehicle is unstable, the controller corrects the yaw angular speed and the centroid sideslip angle, the body is enabled to recover stably, and the whole working condition lasts for 20 s. The low speed step simulation results are shown in fig. 12.
TABLE 1
Figure RE-GDA0003261368570000171
As can be seen from fig. 12: in the first 3s of normal running, the unmanned carrier is in a stable region, the change of the theoretical values of gamma and beta can be well tracked, the vehicle starts to turn after 3s and is influenced by a step signal, the gamma and beta start to deviate from the theoretical values and gradually enlarge, the maximum value is reached near 5s, the maximum value of the yaw angular speed is 0.58rad/s, the maximum value exceeds the theoretical value by 16%, and the lag delay of 0.4s is arranged at the maximum value; the centroid slip angle maximum is-0.84 rad, 12% above theoretical, with a 0.3s hysteresis delay at the maximum and a ripple after the maximum. The stability of the unmanned carrier is poor due to the fact that the unmanned carrier has a instability tendency or already is unstable. After the layered control is added, the gamma and the beta can closely track theoretical values, errors of the gamma, the beta and the theoretical values are respectively kept at 4% and 3%, the lag delay is small and negligible, and the yaw stability of the unmanned carrier under the low-speed working condition is improved.
In addition, a sample vehicle experiment is carried out on the unmanned transport vehicle yaw stability control system and the method, the sample vehicle in the experiment is a four-wheel drive four-wheel steering unmanned transport vehicle which is designed and manufactured autonomously, and the parameters of the whole vehicle and the parameters of a motor are shown in a table 2. The unmanned carrying vehicle is provided with a DSP with the model number of F28335 as a lower computer, the embedded mini industrial personal computer is an upper computer, a hub motor is used as a driving source, a stepping motor is used as a steering source, and four-wheel independent driving, braking and steering are realized through a wire control technology; the load platform uses the push rod motor as the power supply, realizes reciprocating, conveniently loads and unloads goods.
During the experiment, the position and attitude information of the vehicle such as the two-dimensional position of the vehicle, the vehicle speed, the yaw velocity, the mass center slip angle and the like are determined by a high-precision INS inertial navigation system which is differentiated from a base station, real-time parameters of the in-wheel motor such as the motor rotating speed and the torque are respectively measured by a motor encoder and a torque sensor, signals are collected in real time by a DSP singlechip and are fed back to an upper computer, the upper computer sends control instructions to the singlechip through serial port communication, and the singlechip directly controls corresponding equipment according to the commands.
TABLE 2
Figure RE-GDA0003261368570000181
Figure RE-GDA0003261368570000191
The operating environment of the unmanned carrier researched by the invention is the research on the low-speed yaw stability between two stations in a factory, and for the safety, the road surface with the adhesion coefficient of 0.7 is selected in the test, the average speed is kept at 25Km/h, the unmanned carrier loads from the loading area, then moves out of the loading area, turns to enter the unloading area to unload, sprays water on the road surface to enable the adhesion coefficient of the road surface to be approximately 0.4, and the test is repeated. The automatic guided vehicle enters an experimental site at a constant speed and arrives at a feeding area to load goods, the automatic guided vehicle starts to turn after 3 seconds, and the automatic guided vehicle finishes turning after 20 seconds and arrives at a discharging area to unload the goods. The results of the experiment are shown in FIG. 13.
As can be seen from the figure: unmanned transport vehicle is loading the goods after, and the goods is put irregularly and is leaded to whole car barycenter to change, and when 3s began to turn to, the stability of yawing began to produce undulant, and the controller began to work. 4s to 5s, the unmanned transport vehicle is in a single control domain state, and the yaw rate controller works independently; 5s to 12s, starting the unmanned transport vehicle to be in a joint control domain state, and enabling the yaw rate controller and the mass center lateral deviation controller to work jointly; the unmanned transport vehicle recovers the single control domain state from 12s to 17s, and the mass center side deflection angle controller stops working; the automated guided vehicle 17s to 20s gradually and smoothly enters the stable region, and the yaw-rate controller stops operating. Experiments show that the input of the controller can closely track theoretical values of the actual values of the yaw rate and the centroid slip angle under different road adhesion coefficients. The deviation between the actual value peak value and the theoretical value is kept within 5%, so that the yaw stability of the unmanned carrier during low-speed steering is improved, and the effectiveness of a control strategy is proved.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (9)

1. The yaw stability control system of the unmanned carrying vehicle is characterized by comprising an upper layer controller and a lower layer controller which is electrically and mechanically connected with the upper layer controller;
the upper layer controller comprises a yaw velocity controller, a mass center lateral deviation controller, a development joint controller and a sliding mode controller which are in electromechanical connection with the unmanned transport vehicle; the sliding mode controller is connected with the yaw angular speed controller, the mass center lateral deviation controller and the extension joint controller;
the lower layer controller is a driving torque distribution controller and is in electromechanical connection with a hub motor of the unmanned transport vehicle.
2. An automated guided vehicle yaw stability control system according to claim 1, wherein the yaw stability control system is used for yaw stability control.
3. The control method of the automated guided vehicle yaw stability control system of claim 1, comprising the steps of:
establishing an ideal reference model and a dynamic model of the unmanned transport vehicle;
inputting the target vehicle speed and the steering angle into an ideal reference model and a dynamic model, and calculating theoretical values gamma and beta and actual values gamma and beta of the yaw velocity and the centroid sideslip angle;
the upper controller inputs gamma, beta, gamma and beta, a yaw rate controller, a mass center lateral deviation controller and an extensible combined controller to output calculated yaw moments delta M gamma, delta M beta, xi 1 delta M gamma and xi 2 delta M beta, wherein xi 1 and xi 2 are weights calculated in the extensible combined controller and occupied by the yaw rate controller and the mass center lateral deviation controller respectively;
calculating by a sliding mode controller to obtain a final additional yaw moment delta Mz, and inputting the final additional yaw moment delta Mz to a lower layer controller;
and a control domain module in the lower layer controller receives the delta Mz and gamma, beta, gamma and beta input by the upper layer controller to carry out control domain judgment according to the vehicle state, and distributes the driving torque to the hub motor of the unmanned transport vehicle through constraint conditions.
4. The method of controlling the automated guided vehicle yaw stability control system of claim 3, wherein the automated guided vehicle dynamics model comprises:
longitudinal motion equation:
Figure FDA0003198063110000021
wherein m is the automated guided vehicle mass; v. ofxIs the longitudinal acceleration; v. ofyIs the transverse velocity; gamma is a yaw angular velocity; fxiAnd Fyi(i ═ fl, fr, rl, rr) are the tire longitudinal and lateral forces, respectively; deltafl、δfrRespectively the left and right steering angles of the front wheels; deltarl、δrrThe rear wheel left and right steering angle.
The transverse motion equation:
Figure FDA0003198063110000022
in the formula, vyAcceleration in the lateral direction; v. ofxThe longitudinal speed of the mass center under the vehicle body coordinate system.
Yaw motion equation:
Figure FDA0003198063110000023
in the formula IzMoment of inertia about the z-axis for the automated guided vehicle; gamma is yaw angular acceleration; l isfAnd LrDistances from the center of mass to the front and rear axes, respectively; d1、d2The distance from the left wheel and the right wheel to the equivalent wheel respectively.
Differential equation of wheel motion:
Figure FDA0003198063110000024
in the formula, JωIs the rotational inertia of the wheel; omegaiIs the wheel rotational angular acceleration; t isdiIs the input torque of the wheel; fxiIs the tire lateral force; and R is the effective rolling radius of the wheel.
The method for establishing the tire model based on the magic formula comprises the following steps:
Figure FDA0003198063110000031
wherein y (x) may be a lateral force or a longitudinal force; the independent variable x may represent the slip angle or the longitudinal slip ratio of the tire, respectively; the coefficient B, C, D, E in the formula is in turn determined by the vertical load and camber angle of the tire, where B is the stiffness factor; c is a curve shape factor; d is a crest factor; and E is a curve curvature factor.
5. The method of controlling an automated guided vehicle yaw stability control system of claim 3, wherein the automated guided vehicle ideal reference model selects a zero-centroid slip angle, the ideal reference model being:
Figure FDA0003198063110000032
in the formula:
xd＝[βd ωr]T (12)
Figure FDA0003198063110000033
Figure FDA0003198063110000034
ud＝[δfl δfr δrl δrr]T (15)
wherein: kfFront wheel cornering stiffness; krIs rear wheel cornering stiffness; i iszIs horizontal swinging moment of inertia; and a and b are distances from the center of mass of the whole vehicle to the front and rear shafts respectively.
Namely, it is
Figure FDA0003198063110000041
In the formula: tau isωIs the inertial link time constant; gωThe steady state gain of yaw rate versus turning angle is modeled for the ideal vehicle.
Figure FDA0003198063110000042
In the formula: k is a steering characteristic stability factor of the vehicle.
Wherein, the yaw velocity of the vehicle needs to be ensured to satisfy:
|ω_rd|≤|0.85μg/v_x| (18)
in the formula: μ is a road surface adhesion coefficient.
6. The control method of the automated guided vehicle yaw stability control system according to claim 3, wherein the yaw rate controller inputs a difference Δ γ between the desired yaw rate and the predicted yaw rate and a change rate of the difference using a combination of model predictive control and fuzzy control
Figure FDA0003198063110000043
Outputting an additional yaw moment Δ Mγ。
7. The method of controlling an automated guided vehicle yaw stability control system of claim 3, wherein the centroid yaw angle controller inputs a difference Δ β between the desired yaw rate and the predicted yaw rate and a change rate of the difference using a combination of model predictive control and fuzzy control
Figure FDA0003198063110000044
Outputting an additional yaw moment Δ Mβ。
8. The control method of the automated guided vehicle yaw stability control system of claim 3, wherein the control method of the scalable unified controller comprises the steps of:
selecting a difference value delta gamma between gamma predicted by the MPC model and a theoretical value gamma calculated by an ideal reference model and a centroid slip angle actual value beta as control quantities, and dividing a vehicle driving state into a stable domain, a single control domain and a joint control domain;
selecting a MPC model to take a numerical value beta predicted by an actual centroid sideslip angle as an abscissa, and taking a difference delta gamma between an ideal yaw velocity gamma obtained by the actual yaw velocity prediction numerical value gamma and a linear 2-DOF model as an ordinate to form a two-dimensional extension set; adopting a tolerance zone division method for the vertical coordinate yaw angular speed deviation to obtain the ranges of a stable domain, a single control domain and a joint control domain;
converting the two-dimensional extension set into a one-dimensional extension set to calculate an extension distance and construct a correlation function;
determining joint control weights, ξ, when in the stability domain1＝0,ξ20; in the single control domain, xi1＝1,ξ20; xi when in the Joint control Domain1Absolute calculated value of the correlation function/100 ξ21-correlation function absolute calculation value/100.
9. The control method of an automated guided vehicle yaw stability control system of claim 3, wherein the control method of the sliding mode controller comprises the steps of:
constructing a switching surface;
selecting an index approach rate;
and calculating an additional yaw moment delta Mz by combining the exponential approach rate, and inputting the additional yaw moment delta Mz to a lower layer controller.
CN202110896484.2A 2021-08-05 2021-08-05 Yaw stability control system and method for unmanned carrier Withdrawn CN113547931A (en)

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