CN206537164U - A kind of differential steering system - Google Patents
A kind of differential steering system Download PDFInfo
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- CN206537164U CN206537164U CN201720045778.3U CN201720045778U CN206537164U CN 206537164 U CN206537164 U CN 206537164U CN 201720045778 U CN201720045778 U CN 201720045778U CN 206537164 U CN206537164 U CN 206537164U
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- 230000008859 change Effects 0.000 description 8
- 210000002569 neuron Anatomy 0.000 description 8
- 230000007935 neutral effect Effects 0.000 description 4
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- 239000011159 matrix material Substances 0.000 description 3
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- 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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Abstract
The utility model discloses a kind of differential steering system, the system includes steering wheel angle sensor, steering wheel, steering column, rack and pinion steering gear, four wheels and wheel hub motor, front axle, finished vehicle electronic control unit, batteries, yaw-rate sensor, vehicle speed sensor, rear axle and motor control unit.In the process of moving, finished vehicle electronic control unit gathers steering wheel angle, yaw velocity and GES in real time, calculate the difference of preferable yaw velocity and actual yaw velocity, wheel hub motor output torque is recalculated by the neural network control device of design, and this dtc signal is delivered to electric machine controller, and current signal is sent from electric machine controller to four wheel hub motors, complete wheel hub motor and normally controlled with the steering stability under disabled status.The utility model can improve the stability and security when reliability and the running car of differential steering system.
Description
Technical field
The utility model is related to four-wheel steering technical field, more particularly to a kind of differential steering system.
Background technology
For conventional truck, clutch, speed changer, power transmission shaft, differential mechanism or even transfer gear be all it is essential,
And not only weight weight, vehicle structure are complicated for these parts, while the problem of there is also periodic maintenance and fault rate is needed.But wheel
Hub motor just solves this problem well.In addition to structure is more simple, use the vehicle of In-wheel motor driving can be with
More preferable space availability ratio is obtained, while transmission efficiency.
Because wheel hub motor possesses the characteristic that single wheel independently drives, therefore it is easily achieved forerunner, rear-guard or four
Drive drive form.Wheel hub motor can adjust left and right wheelses torque simultaneously or rotating speed realizes differential steering, greatly reduce vehicle
Radius of turn, pivot stud can be almost realized under special circumstances.
But wheel hub motor there may be failure conditions, there is problem in reliability.How under wheel hub motor failure conditions,
The problem of ensureing stability of automobile is badly in need of solving.
Utility model content
Technical problem to be solved in the utility model is that there is provided one kind is poor for defect involved in background technology
Fast steering.
The utility model uses following technical scheme to solve above-mentioned technical problem：
A kind of differential steering system, including steering wheel angle sensor, steering wheel, steering column, rack and pinion steering
Device, first to fourth wheel, first to fourth wheel hub motor, front axle, finished vehicle electronic control unit, batteries, speed sensing
Device, yaw-rate sensor, rear axle and motor control unit；
Described steering column one end and steering wheel are fixedly linked, and the other end passes through rack and pinion steering gear and the front axle
It is connected；
The steering wheel angle sensor is arranged on steering column, for obtaining steering wheel angle；
The vehicle speed sensor and yaw-rate sensor are arranged on automobile, are respectively used to obtain the speed of automobile
And yaw velocity；
First wheel, the second wheel are separately positioned on the two ends of the front axle, the 3rd wheel, the 4th wheel point
The two ends of the rear axle are not arranged on；
First to fourth wheel hub motor is correspondingly arranged on first to fourth wheel respectively, for driving first
To the 4th wheel；
The batteries are set on automobile, for powering；
The finished vehicle electronic control unit is sensed with steering wheel angle sensor, vehicle speed sensor, yaw velocity respectively
Device, electric machine controller, batteries are electrically connected, for according to steering wheel angle sensor, vehicle speed sensor and yaw angle speed
The data that degree sensor is measured, which calculate the torque of four wheel hub motors and produce corresponding current signal, passes to the motor control
Device processed；
The electric machine controller is electrically connected with four wheel hub motors, batteries respectively, for according to the electric current received
Signal controls four wheel hub motor work.
The invention also discloses a kind of adaptive neural network fault tolerant control method based on the differential steering system,
Comprise the following steps：
Step 1), calculate the relation of preferable yaw velocity and steering wheel angle；
Step 2), set up the state-space model of differential steering system；
Step 3), the state-space model based on differential steering system sets up its adaptive neural network fault-tolerant control system
State-space model, and the state-space model of the adaptive neural network fault-tolerant control system based on differential steering system,
The state for setting up adaptive neural network fault-tolerant control system of the differential steering system in the case of wheel hub motor breaks down is empty
Between model；
Step 4), set up the reference model and inversion model of adaptive neural network fault-tolerant control system；
Step 5), reference model, inversion model and preferable yaw angle based on adaptive neural network fault-tolerant control system
Speed and the relation of steering wheel angle set up the Network Compensator of adaptive neural network fault-tolerant control system；
Step 6), based on the Network Compensator of adaptive neural network fault-tolerant control system, set up adaptive neural network
The neural network control device of network fault tolerance control system；
Step 7), it is adaptive under being broken down based on neural network control device to reference model and wheel hub motor
Error between the output of neutral net fault-tolerant control system is adaptively adjusted.
Adaptive neural network fault tolerant control method as a kind of differential steering system of the utility model is further excellent
Change scheme, step 1) described in preferable yaw velocity ωr *With steering wheel angle θswRelation is：
In formula：a0=kfkr(a+b)2+(krb-kfa)mu2；b0=kfkr(a+b)u；L is antero posterior axis axle
Away from；U is car speed；KsFor default yaw velocity adjusting parameter；kf、krRespectively front and back wheel cornering stiffness；A is barycenter
To front shaft away from；B is barycenter to rear axle wheelbase；M is complete vehicle quality.
Adaptive neural network fault tolerant control method as a kind of differential steering system of the utility model is further excellent
Change scheme, step 2) described in the state-space model of differential steering system be：
In formula,
C=[0 00 1]；
U (t)=[Tfl Tfr Trl Trr]T；W (t)=[Tsw]T；Y (t)=[ωr]T；
δfFor front wheel angle；β is side slip angle；ωrFor yaw velocity；D is half wheelbase；JsFor equivalent turn of steering wheel
Dynamic inertia；G is rack and pinion steering gear gearratio；I is vehicle around z-axis rotary inertia；BsFor steering wheel equivalent damping, R is wheel
Tire radius；d2Square is dragged for tire；d1For stub lateral shift square；TswThe torque of steering wheel is acted on for driver；Tfl、Tfr、
Trl、TrrBefore respectively left front, right, left back, off hind wheel hub motor output torque.
Adaptive neural network fault tolerant control method as a kind of differential steering system of the utility model is further excellent
Change scheme, step 3) described in differential steering system adaptive neural network fault-tolerant control system state-space model
For：
In formula, f (x (t))=Ax (t)；G (x (t))=λ B；H (x (t))=Cx (t)；T is the time；λ1、λ2、λ3、λ4Before respectively left front, right, the probability that left back, off hind wheel hub motor breaks down.
Adaptive neural network fault tolerant control method as a kind of differential steering system of the utility model is further excellent
Change scheme, step 3) described in adaptive neural network of the differential steering system in the case of wheel hub motor breaks down it is fault-tolerant
The state-space model of control system is：
In formula, σ (x (t), u (t), w (t)) is the disturbance input function under wheel hub motor failure.
Adaptive neural network fault tolerant control method as a kind of differential steering system of the utility model is further excellent
Change scheme, step 4) described in the reference model of adaptive neural network fault-tolerant control system be：
In formula：xm(t) it is the state vector of reference model；um(t) it is the input dominant vector of reference model, ym(t) it is ginseng
Examine the output vector of model；Am=A；Bm=λ B；Cm=C.
Adaptive neural network fault tolerant control method as a kind of differential steering system of the utility model is further excellent
Change scheme, step 4) described in the inversion model of adaptive neural network fault-tolerant control system be：
U (t)=g-1(t)[v(t)-f(x)]
In formula：V (t) is given tracking response.
Adaptive neural network fault tolerant control method as a kind of differential steering system of the utility model is further excellent
Change scheme, step 5) described in Network Compensator be：
In formula：Δ is inversion model error；ysFor the output of s layers of neutral net；wisFor i-th of neuron to s layers of god
Weight through member；gi(x) it is i-th of neuron output value；I is is less than or equal to n natural number more than or equal to 1, and n is neuron
Number, s is the Current Situation of Neural Network number of plies.
Adaptive neural network fault tolerant control method as a kind of differential steering system of the utility model is further excellent
Change scheme, step 6) described in neural network control device be：
In formula：ueer(t) it is the compensation error of inner ring system；KpFor parameter matrix；uNNFor neural network control device
Output.
The utility model uses above technical scheme compared with prior art, with following technique effect：
1. gathering steering wheel angle, yaw velocity and GES in real time according to electronic control unit, calculate preferable
The difference of yaw velocity and actual yaw velocity, wheel hub electricity is recalculated by the neural network control device of design
Machine output torque, and current signal is sent from ECU to wheel hub motor, complete wheel hub motor normally steady with steering under disabled status
Qualitative contrlol；
2. it is easy to be reliable to put forward control method, while effectively overcoming inverse mould caused by steering failure using neutral net
The influence of type error and non-linear factor, so as to realize the real-time model- following control to differential steering model；
3. this method need not be known a priori by position and the size of failure generation, need not also parameter identification be carried out to system, just
The accurate trace model output of differential steering system in case of a fault can be ensured, so as to reach preferable dynamic property.
Brief description of the drawings
Fig. 1 is the structural representation of differential steering system in the utility model；
Fig. 2 is the schematic flow sheet of adaptive neural network fault tolerant control method in the utility model.
In figure, 1- steering wheel angle sensors, 2- steering wheels, 3- steering columns, 4- the near front wheels and wheel hub motor, 5- gears
Rack steering gear, 6- off-front wheels and wheel hub motor, 7- front axles, 8- finished vehicle electronic control units, 9- batteries, 10- left rear wheels
And wheel hub motor, 11- off hind wheels and wheel hub motor, 12- vehicle speed sensor, 13- yaw-rate sensors, 14- rear axles, 15-
Electric machine controller.
Embodiment
The technical solution of the utility model is described in further detail below in conjunction with the accompanying drawings：
As shown in figure 1, the utility model develops a kind of differential steering system, including steering wheel angle sensor 1, direction
Disk 2, steering column 3, rack and pinion steering gear 5, first to fourth wheel, first to fourth wheel hub motor, front axle 7, vehicle
Electronic control unit 8, batteries 9, vehicle speed sensor 12, yaw-rate sensor 13, rear axle 14 and motor control unit
15；
Described one end of steering column 3 and steering wheel 2 are fixedly linked, and the other end passes through rack and pinion steering gear 5 and described
Front axle 7 is connected；
The steering wheel angle sensor 1 is arranged on steering column 3, for obtaining steering wheel angle；
The vehicle speed sensor 12 and yaw-rate sensor 13 are arranged on automobile, are respectively used to obtain automobile
Speed and yaw velocity；
First wheel, the second wheel are separately positioned on the two ends of the front axle 7, the 3rd wheel, the 4th wheel
It is separately positioned on the two ends of the rear axle 14；
First to fourth wheel hub motor is correspondingly arranged on first to fourth wheel respectively, for driving first
To the 4th wheel；
The batteries 9 are arranged on automobile, for powering；
The finished vehicle electronic control unit 8 respectively with steering wheel angle sensor 1, vehicle speed sensor 12, yaw velocity
Sensor 13, electric machine controller 15, batteries 9 are electrically connected, for according to steering wheel angle sensor 1, vehicle speed sensor
12 and the data that measure of yaw-rate sensor 13 calculate the torque of four wheel hub motors and produce corresponding current signal and pass
Pass the electric machine controller 15；
The electric machine controller 15 is electrically connected with four wheel hub motors, battery 9 respectively, for according to the electricity received
Flow signal and control four wheel hub motor work.
Hold as shown in Fig. 2 the utility model also disclosed a kind of adaptive neural network based on the differential steering system
Wrong control method, it is characterised in that comprise the following steps：
Step 1), calculate preferable yaw velocity ωr *With steering wheel angle θswRelation：
In formula：a0=k1k2(a+b)2+(k2b-k1a)mu2；b0=k1k2(a+b)u；L is antero posterior axis
Wheelbase；U is car speed；KsFor default yaw velocity adjusting parameter, its scope can like according to driver to be chosen, preferentially
For 0.12-0.37；k1、k2Respectively front and back wheel cornering stiffness；A be barycenter to front shaft away from；B is barycenter to rear axle wheelbase；M is
Complete vehicle quality.
Step 2), set up differential steering system state-space model：
Differential steering system state-space model is：
In formula,
C=[0 00 1]；
U (t)=[Tfl Tfr Trl Trr]T；W (t)=[Tsw]T；Y (t)=[ωr]T；
δfFor front wheel angle；β is side slip angle；ωrFor yaw velocity；D is half wheelbase；JsFor equivalent turn of steering wheel
Dynamic inertia；G is rack and pinion steering gear gearratio；I is vehicle around z-axis rotary inertia；BsFor steering wheel equivalent damping, R is wheel
Tire radius；d2Square is dragged for tire；d1For stub lateral shift square；TswThe torque of steering wheel is acted on for driver；Tfl、Tfr、
Trl、TrrBefore respectively left front, right, left back, off hind wheel hub motor output torque.
Step 3), the state-space model based on differential steering system sets up its adaptive neural network fault-tolerant control system
State-space model, and the state-space model of the adaptive neural network fault-tolerant control system based on differential steering system,
The state for setting up adaptive neural network fault-tolerant control system of the differential steering system in the case of wheel hub motor breaks down is empty
Between model.
Initially set up the adaptive neural network fault-tolerant control system state-space model of differential steering system：
In formula, f (x (t))=Ax (t)；G (x (t))=λ B；H (x (t))=Cx (t)；T is the time；λ1、λ2、λ3、λ4Before respectively left front, right, the probability that left back, off hind wheel hub motor breaks down.
Adaptive neural network fault-tolerant control system state-space model based on above-mentioned differential steering system, when differential system
During failure of uniting, the differential steering system state-space model of adaptive neural network fault tolerance is：
In formula, σ (x (t), u (t), w (t)) is the disturbance input function in the case of failure；
Step 4), set up the reference model and inversion model of adaptive neural network fault-tolerant control system；
Initially set up the reference model of adaptive neural network fault-tolerant control system：
In formula：xm(t) it is the state vector of reference model；um(t) it is the input dominant vector of reference model, ym(t) it is ginseng
Examine the output vector of model；Am=A；Bm=λ B；Cm=C.
Next sets up the inversion model of adaptive neural network fault-tolerant control system：
U (t)=g-1(t)[v(t)-f(x)]
In formula：V (t) is given tracking response.
Step 5), reference model, inversion model and preferable yaw based on above-mentioned adaptive neural network fault-tolerant control system
The relation of angular speed and steering wheel angle, setting up the Network Compensator of adaptive neural network fault-tolerant control system can state
For：
In formula：Δ is inversion model error；ysFor the output of s layers of neutral net；wisFor i-th of neuron to s layers of god
Weight through member；gi(x) it is i-th of neuron output value；I is is less than or equal to n natural number more than or equal to 1, and n is neuron
Number, s is the Current Situation of Neural Network number of plies.
Step 6), based on the Network Compensator of adaptive neural network fault-tolerant control system, set up adaptive neural network
The neural network control device of network fault tolerance control system is：
In formula：ueer(t) it is the compensation error of inner ring system；KpFor parameter matrix；uNNFor neural network control device
Output.
Step 7), based on adaptive neural network adjuster to the adaptive neural network under reference model and wheel hub motor failure
Error between the output of network fault tolerance control system is adaptively adjusted.
From Fig. 2 the utility model neural network control device structure charts as can be seen that inversion model error delta with it is inverse
The input of model and system output relation be：
The performance indications being defined as follows：
In formula：yjsFor j-th of neuron output；ejFor j-th of neuron error.
First with compensation error u of the off-line training in the case of variouseerSo that the output of network approaches ueer, so that complete
Into the effect of feedback compensation.
On the basis of off-line learning, the data during operation of collection differential steering system, utilize online adaptive in real time
Algorithm undated parameter is practised, in order to improve the stability of adaptive neural network fault-tolerant control system, is entered for neural network weight
Row adjustment, using following online adaptive learning algorithm：
In formula：W () is weights；T, P are positive definite matrix；Q is RBF.
In the process of moving, electronic control unit gathers steering wheel angle, yaw velocity and GES in real time, meter
Preferable yaw velocity and the difference of actual yaw velocity are calculated, is recalculated by the neural network control device of design
Wheel hub motor output torque, and from motor control unit to wheel hub motor send current signal, complete wheel hub motor normally with mistake
Steering stability control under effect situation, it is achieved thereby that a kind of four rotation with adaptive neural network faults-tolerant control function
To system and its control method.
Those skilled in the art of the present technique are it is understood that unless otherwise defined, all terms used herein (including skill
Art term and scientific terminology) there is the general understanding identical with the those of ordinary skill in the utility model art to anticipate
Justice.It should also be understood that those terms defined in such as general dictionary should be understood that with upper with prior art
The consistent meaning of meaning hereinafter, and unless defined as here, will not with idealization or excessively formal implication come
Explain.
Above-described embodiment, is entered to the purpose of this utility model, technical scheme and beneficial effect
One step is described in detail, be should be understood that and be the foregoing is only embodiment of the present utility model, is not used to limit
The utility model processed, all within spirit of the present utility model and principle, any modification, equivalent substitution and improvements done etc.,
It should be included within protection domain of the present utility model.
Claims (1)
1. a kind of differential steering system, it is characterised in that including steering wheel angle sensor（1）, steering wheel（2）, steering column
（3）, rack and pinion steering gear（5）, first to fourth wheel, first to fourth wheel hub motor, front axle（7）, finished vehicle electronic control
Unit processed（8）, batteries（9）, vehicle speed sensor（12）, yaw-rate sensor（13）, rear axle（14）And motor control
Device（15）；
The steering column（3）One end and steering wheel（2）It is fixedly linked, the other end passes through rack and pinion steering gear（5）And institute
State front axle（7）It is connected；
The steering wheel angle sensor（1）It is arranged on steering column（3）On, for obtaining steering wheel angle；
The vehicle speed sensor（12）And yaw-rate sensor（13）It is arranged on automobile, is respectively used to obtain automobile
Speed and yaw velocity；
First wheel, the second wheel are separately positioned on the front axle（7）Two ends, the 3rd wheel, the 4th wheel point
The rear axle is not arranged on（14）Two ends；
First to fourth wheel hub motor is correspondingly arranged on first to fourth wheel respectively, for driving first to the
Four wheels；
The batteries（9）It is arranged on automobile, for powering；
The finished vehicle electronic control unit（8）Respectively with steering wheel angle sensor（1）, vehicle speed sensor（12）, yaw angle speed
Spend sensor（13）, electric machine controller（15）, batteries（9）It is electrically connected, for according to steering wheel angle sensor（1）、
Vehicle speed sensor（12）And yaw-rate sensor（13）The data measured calculate the torque of four wheel hub motors and produce phase
The current signal answered passes to the electric machine controller（15）；
The electric machine controller（15）Respectively with four wheel hub motors, batteries（9）It is electrically connected, is received for basis
Current signal controls four wheel hub motor work.
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|---|---|---|---|
| CN201720045778.3U CN206537164U (en) | 2017-01-16 | 2017-01-16 | A kind of differential steering system |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201720045778.3U CN206537164U (en) | 2017-01-16 | 2017-01-16 | A kind of differential steering system |
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| Publication Number | Publication Date |
|---|---|
| CN206537164U true CN206537164U (en) | 2017-10-03 |
Family
ID=59938933
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| CN201720045778.3U Active CN206537164U (en) | 2017-01-16 | 2017-01-16 | A kind of differential steering system |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106882080A (en) * | 2017-01-16 | 2017-06-23 | 南京航空航天大学 | A kind of differential steering system and its adaptive neural network fault tolerant control method |
| CN108860296A (en) * | 2018-08-24 | 2018-11-23 | 厦门理工学院 | Electric car electronic differential control system and electric car based on steering angle closed loop |
| CN110228375A (en) * | 2019-04-30 | 2019-09-13 | 南京航空航天大学 | A kind of distribution driving control method of the vehicle without deflecting roller pivot stud |
| CN110531772A (en) * | 2019-09-12 | 2019-12-03 | 四川阿泰因机器人智能装备有限公司 | A kind of control method and its system of grain-levelling machine device people |
| CN111824256A (en) * | 2020-07-13 | 2020-10-27 | 南京航空航天大学 | Steer-by-wire system with adaptive fault-tolerant control function and control method thereof |
| CN112026777A (en) * | 2020-07-23 | 2020-12-04 | 南京航空航天大学 | Vehicle composite steering system and mode switching control method thereof |
-
2017
- 2017-01-16 CN CN201720045778.3U patent/CN206537164U/en active Active
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106882080A (en) * | 2017-01-16 | 2017-06-23 | 南京航空航天大学 | A kind of differential steering system and its adaptive neural network fault tolerant control method |
| CN108860296A (en) * | 2018-08-24 | 2018-11-23 | 厦门理工学院 | Electric car electronic differential control system and electric car based on steering angle closed loop |
| CN108860296B (en) * | 2018-08-24 | 2023-07-28 | 厦门理工学院 | Electronic differential control system of electric automobile and electric automobile based on steering angle closed loop |
| CN110228375A (en) * | 2019-04-30 | 2019-09-13 | 南京航空航天大学 | A kind of distribution driving control method of the vehicle without deflecting roller pivot stud |
| CN110531772A (en) * | 2019-09-12 | 2019-12-03 | 四川阿泰因机器人智能装备有限公司 | A kind of control method and its system of grain-levelling machine device people |
| CN110531772B (en) * | 2019-09-12 | 2022-12-20 | 四川阿泰因机器人智能装备有限公司 | Control method and system of grain leveling robot |
| CN111824256A (en) * | 2020-07-13 | 2020-10-27 | 南京航空航天大学 | Steer-by-wire system with adaptive fault-tolerant control function and control method thereof |
| CN112026777A (en) * | 2020-07-23 | 2020-12-04 | 南京航空航天大学 | Vehicle composite steering system and mode switching control method thereof |
| CN112026777B (en) * | 2020-07-23 | 2021-09-17 | 南京航空航天大学 | Vehicle composite steering system and mode switching control method thereof |
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