CN108177693A - Wheel hub drives the electronic differential control system of electric vehicle - Google Patents
Wheel hub drives the electronic differential control system of electric vehicle Download PDFInfo
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- CN108177693A CN108177693A CN201711461478.4A CN201711461478A CN108177693A CN 108177693 A CN108177693 A CN 108177693A CN 201711461478 A CN201711461478 A CN 201711461478A CN 108177693 A CN108177693 A CN 108177693A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D11/00—Steering non-deflectable wheels; Steering endless tracks or the like
- B62D11/02—Steering non-deflectable wheels; Steering endless tracks or the like by differentially driving ground-engaging elements on opposite vehicle sides
- B62D11/04—Steering non-deflectable wheels; Steering endless tracks or the like by differentially driving ground-engaging elements on opposite vehicle sides by means of separate power sources
-
- 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
- B60L15/2036—Electric differentials, e.g. for supporting steering vehicles
-
- 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
- B60L15/32—Control or regulation of multiple-unit electrically-propelled vehicles
-
- 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
- B60L15/32—Control or regulation of multiple-unit electrically-propelled vehicles
- B60L15/38—Control or regulation of multiple-unit electrically-propelled vehicles with automatic control
-
- 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
- B60L2240/421—Speed
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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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- Engineering & Computer Science (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Power Engineering (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Arrangement And Driving Of Transmission Devices (AREA)
Abstract
The present invention relates to a kind of wheel hub driving electric vehicle electronic differential control system, including Electronic differential control device, each In-wheel motor driving device, for measurement direction disk corner angular transducer, detect throttle position throttle position switch, measure wheel hub motor actual speed driving wheel actual speed detection module；Angular transducer and throttle position switch are communicated by CAN bus with Electronic differential control device, and each driving wheel actual speed is calculated by detecting wheel hub motor hall signal in driving wheel actual speed detection module；The Electronic differential control device includes Ackerman steering models computing module and PID neural network control modules.Present system is simple in structure, can carry out rational Electronic differential control to each wheel hub motor according to Vehicular turn angle and target travel speed.
Description
Technical field
The present invention relates to electric vehicle Electronic differential control technical field more particularly to front-wheel steer, two trailing wheels using wheel
The electronic differential control system of a kind of wheel hub driving electric vehicle that hub motor independently drives.
Background technology
Wheel hub drives electric vehicle because its energy source is extensive, simple in structure, transmission efficiency and each driving wheel energy are independent
The advantages that control, becomes one new direction of development of automobile.With traditional combustion engine automobile and single motor center driven electric vehicle
It compares, wheel hub driving each driving wheel of electric vehicle is directly driven by wheel hub motor, it can be achieved that each driving wheel is from zero to maximum speed
Electrodeless variable-speed, speed changer, transmission shaft and mechanical differential gear box needed for internal-combustion engines vehicle is omitted, complete vehicle structure obtains letter
Change, efficiently use space increase, transmission efficiency improves.
However, since wheel hub drives electric vehicle to eliminate the mechanical differential gear box that traditional combustion engine automobile has, having
While the advantages that simple in structure, more control freedom degrees, consequently also produce motor turning and coordinate between driving wheel when driving
The problem of control-electronic differential problem.Therefore, to ensure vehicle control stability and safety, vehicle is avoided in Turning travel
When because wheel slip or sliding lead to problems such as to turn to, difficult, road surface accords with degradation and vehicle is out of control, it is necessary to using electricity
Sub- differential control system carries out coordination control to each driving wheel speed, differential when making the medial and lateral driving wheel meet Turning travel
It is required that.
Electronic differential control can simplify automobile chassis framework, improve electric vehicle performance, but on different driving cycles and road surface
Under the conditions of, it is the critical issue of urgent need to resolve to the control that each driving wheel hub motor carries out effectively, accurate, stable.
Invention content
The electric vehicle that the purpose of the present invention is be directed to front-wheel steer, two trailing wheels are independently driven using wheel hub motor, provides
A kind of electronic differential control system, the system can carry out two driving wheels according to electric vehicle difference Turning travel operating mode rational
Electronic differential control.Specific solution is as follows：
Wheel hub drives the electronic differential control system of electric vehicle, it includes Electronic differential control device, each wheel hub motor drives
Dynamic device, the angular transducer for measurement direction disk corner, the throttle position switch of detection throttle position, measurement wheel hub motor
The driving wheel actual speed detection module of actual speed；Angular transducer and throttle position switch pass through CAN bus and electronics
Differential controller communicates, and each driving wheel is calculated by detecting wheel hub motor hall signal in driving wheel actual speed detection module
Actual speed；
The Electronic differential control device includes Ackerman steering models computing module and PID neural network control moulds
Block；
Ackerman steering models computing module Vehicular turn angle according to measured by angular transducer, throttle position switch
δ and target travel speed vcValue, Vehicular turn is calculated, and driving wheel target velocity in medial and lateral is respectively v when driving1、v2；
It is respectively v that medial and lateral driving wheel actual speed, which is calculated, in driving wheel actual speed detection module1'、v2'；
Using the deviation of medial and lateral driving wheel actual speed and target velocity as the input of PIDNN control modules, meter
Calculation obtains each driving wheel PWM speed-regulating signals, and completes the closed-loop control to each driving wheel speed by motor driver, makes driving
Wheel actual speed follows target velocity, realizes differential control.
In said program, Ackerman steering models computing module is according to Vehicular turn angle δ and target travel speed vc's
Vehicular turn medial and lateral driving wheel target velocity v when driving is calculated in value1、v2Expression formula is respectively：
In formula：K is rotated for medial and lateral to nodal distance, and L is wheelbase.
In said program, each drive is calculated by detecting wheel hub motor hall signal in driving wheel actual speed detection module
Driving wheel actual speed, without installing speed probe.
In said program, PIDNN control modules respectively within, the deviation of outside driven wheel actual speed and target velocity makees
For input, each driving wheel PWM speed-regulating signals are calculated, and the closed loop control to each driving wheel speed is completed by motor driver
System makes driving wheel actual speed follow target velocity, realizes differential control.
The advantage of the invention is that：
1. angular transducer and throttle position switch are communicated by existing CAN bus with Electronic differential control device, knot
Structure is simple；
2. Vehicular turn is when driving, according to medial and lateral driving wheel actual speed and the deviation of target velocity, using PIDNN
Each driving wheel PWM speed-regulating signals are calculated in control module, and complete the closed loop to each driving wheel speed by motor driver
Control.Compared to PID controller, the advantages of PIDNN control modules have had both PID controller and neuroid, according to difference
Operating mode adjusts the output of PIDNN control modules in real time, improves the robustness of electronic differential control system, so as to remain most
Excellent control effect.
Description of the drawings
Fig. 1 is the electronic differential control system structure diagram of the present invention.
Fig. 2 is the Ackerman steering model schematic diagrames of the present invention.
Fig. 3 is the wheel hub motor hall signal detects schematic diagram of the present invention.
Fig. 4 is the PIDNN structure diagrams of the present invention.
Fig. 5 is the SPIDNN control system architecture schematic diagrams of the present invention.
Specific embodiment
Below in conjunction with attached drawing, the present invention is described further.
Wheel hub drives the electronic differential control system of electric vehicle, as shown in Figure 1, including Electronic differential control device (1), interior
Side wheel hub motor driver (2), outside wheel hub motor driver (3), the angular transducer for measurement direction disk (8) corner
(4), the throttle position switch (5) of detection throttle position, the interior side drive wheel of measurement inside wheel hub motor actual speed are practical
Rotating speed measring module (6), the outside driven wheel actual speed detection module (7) for measuring outside wheel hub motor actual speed；Angle
Sensor (4) and throttle position switch (5) are communicated by CAN bus with Electronic differential control device (1), medial and lateral driving wheel
Medial and lateral In-wheel motor driving wheel is calculated by detecting wheel hub motor hall signal in actual speed detection module (6) (7)
(9) (10) actual speed；
Electronic differential control system MCU uses chip model, and for STM32F103RCT6, CAN transceiver is adopted in CAN bus
With PCA82C250 chips, wheel hub motor uses permanent-magnet brushless DC electric machine, rated voltage 48V, rated power 500W；Motor drives
The power switch circuit of dynamic device supports analog signal and pwm signal both of which using three phase full bridge circuit, input control signal.
Specific implementation step is as follows：
Step 1：Outboard wheel hub motor driving wheel (9) (10) target velocity in calculating
As shown in Fig. 2, δ is automobile steering angle, R1、R2Respectively medial and lateral driving wheel turns to half to Vehicular turn when driving
Diameter, R are the turning radius of two driving wheel midpoint c；K is rotated for medial and lateral to nodal distance, and L is wheelbase, and O is automobile when turning to
Circular motion center.Middle plane geometry relationship can obtain according to fig. 2：
R=Lcot δ
Ackerman steering models assume that car body is rigid, do not consider that vehicle rolls and vertical load offset pair when driving
The influence of medial and lateral wheel, driving wheel is pure rolling to Vehicular turn when driving.According to instantaneous center of velocity theorem, Vehicular turn traveling
When, the ratio between inboard wheel speed and its turning radius should be equal to the ratio between outside wheel speed and its turning radius, and equal to vehicle
The ratio between corresponding turning radius of speed of upper any point.With the speed v of two driving wheel midpoint ccSpeed is travelled as vehicle target
Degree, the turning radius are R.Then Vehicular turn when driving, medial and lateral driving wheel target velocity v1、v2With vcMeet with ShiShimonoseki
System：
With vcAs the benchmark of driving wheel target velocity, by above-mentioned acquired R, R1、R2Substituting into above formula can obtain：
It is respectively Vehicular turn medial and lateral In-wheel motor driving wheel (9) (10) target velocity v when driving1、v2About vehicle
Target travel speed vcWith the restriction relation of steering angle sigma.
Step 2：Detect medial and lateral In-wheel motor driving wheel (9) (10) actual speed
Driving wheel actual speed detection module utilizes STM32F103RCT6 chips, and timer is set as input capture mould
Driving wheel actual speed is calculated by detecting wheel hub motor hall signal in formula.When hall signal rising edge arrives in triggering
Disconnected function, the counter of timer are configured to the count mode that progressively increases upwards, and counter is gradually increased from 0~65535, is filled it up with
65535 triggering Overflow flag automatic clears repeat to recycle next time, if count frequency is f.Remember when entering interrupt function every time
The value of this lower hour counter, if the value for being currently entering interruption logging counter is Value_pre, the Counter Value of last time record
For Value_ex.When count frequency f selections are suitable, the magnitude relationship of Value_pre and Value_ex exist as (a) in Fig. 3,
(b) two kinds of situations.
In Fig. 3 shown in (a), hall signal triggers the Counter Value of interruption logging in 0~65,535 1 cycles twice
It is interior, pass twice through current value in the value of interrupt function recordMore than last record value Value_e.X then Halls
The time interval of two adjacent rising edges of signal is：
In Fig. 3 shown in (b), hall signal triggers the Counter Value of interruption logging not in one 0~65535 cycle twice
It is interior.Overflow indicator automatic clear can be triggered after increasing to 65535 due to the value of counter, causes to trigger interrupt function record twice
Value in current value Value_pre be less than last record value Value_ex.Then between the time of two adjacent rising edges of hall signal
It is divided into：
For two kinds of situations being likely to occur, driving wheel actual speed calculation formula is：
When driving wheel does pure rolling, actual speed is:
In formula：R is driving wheel radius, and v units are km/h.
Step 3：The PWM tune of each driving wheel speed is calculated by PID neuroid PIDNN control modules (12)
Fast signal
As shown in figure 4, by taking Vehicular turn when driving inside wheel hub motor driving wheel (9) as an example, then in arbitrary sampling instant
K, by interior side drive wheel target velocity v1(k), actual speed v1' (k) respectively as SPIDNN input, each layer neuron it is defeated
Enter, export and be respectively：
(1) input layers
The input layer of SPIDNN includes the set-point and actual value of two neurons, respectively input control system regulated quantity,
Its input is respectively：
net1(k)=v1(k)
net2(k)=v1'(k)
In formula：v1(k) for kth time sampling the target velocity of inside wheel hub motor driving wheel (9), v when1' (k) time adopt for kth
The actual speed of inside wheel hub motor driving wheel (9) during sample；
Input layer it is respective output be：
xi(k)=neti(k)
(2) hidden layers
The hidden layer of SPIDNN is the key stratum in neuroid, is made of three neurons, respectively ratio nerve
Member, integration neuron and differential neuron.It respectively inputs and is respectively：
In formula：wijFor input layer to hidden layer connection weight weight values, xi(k) it is each neuron output value of input layer；
The output of three neurons of hidden layer is respectively：
Ratio neuron：x'1(k)=net'1(k)
Integrate neuron：x'2(k)=x'2(k-1)+net'2(k)
Differential neuron：x'3(k)=net'3(k)-net'3(k-1)
(3) output layers
The output layer of SPIDNN only comprising a neuron, completes the integration output to network signal, and input is：
In formula：w'jFor hidden layer to output layer connection weight weight values, x'j(k) it is each neuron output value of hidden layer.
Since the output of output layer neuron is exactly the output of SPIDNN,：
V (k)=x " (k)=net " (k)
In formula：V (k) is kth time sampling instant, the output of SPIDNN.
Under primary condition, each layer connection weight initial values of SPIDNN are respectively：w1j=+1, w2j=-1；w'1=KP, w'2=KI,
w'3=KD；At this point, each neuron input summation of hidden layer is：
In formula：E (k) is the inclined of wheel hub motor driving wheel (9) actual speed and target velocity on the inside of kth time sampling instant
Difference；
The output v (k) of SPIDNN networks is：
In formula：KP、KI、KDRespectively PID controller ratio, integration, differentiation element coefficient；E (k) is kth time sampling instant
The deviation of interior side drive wheel actual speed and target velocity；E (k-1) is wheel hub motor driving wheel on the inside of -1 sampling instant of kth
(9) deviation of actual speed and target velocity.
In an initial condition, PID neuroids PIDNN control modules (12) possess approximately controls with PID controller
Effect, on this basis, gathered data sample according to back propagation algorithm on-line training and learns to adjust each connection weight of network
wij、w'jValue；It adjusts the output of PID neuroid PIDNN control modules (12) in real time according to different operating modes, improves electricity
The robustness of sub- differential control system, so as to remain optimal control effect.SPIDNN back propagation algorithms are as follows：
The purpose of SPIDNN back propagation learnings is to make the deviation mean value of square of network reality output amount and preferable output quantity
For minimum.Deviation mean value of square expression formula is：
In formula：L is sampling number, v1(k) for kth time sampling the target velocity of interior side drive wheel, v when1' (k) be kth time
The actual speed of interior side drive wheel during sampling.
Each layer connection weight weight values of SPIDNN are adjusted according to gradient method, through n0After step training and study, each layer connection weight weight values
Iterative equation be：
In formula：η is Learning Step.
It can be obtained according to above-mentioned equation, the connection weight weight values w of input layer to hidden layerijWith the connection weight of hidden layer to output layer
Weight values wj' circular respectively by it is following it is various determine.
(1) hidden layers are to output layer
The iterative formula of hidden layer to output layer connection weight weight values is：
To w' in deviation mean value of square expression formula EjAsk local derviation that can obtain：
Therefore it can obtain, through n0After step training and study, hidden layer is to each weighted value iterative formula of output layer：
In formula：η'jFor hidden layer to output layer weighted value Learning Step.
(2) input layers are to hidden layer
The iterative formula of input layer to hidden layer connection weight weight values is：
To w in deviation mean value of square expression formula EijAsk local derviation that can obtain：
Therefore it can obtain, through n0After step training and study, the weighted value iterative formula of input layer to hidden layer is：
In formula：ηijFor input layer to hidden layer weighted value Learning Step.
By each layer connection weight weight values of constantly regulate SPIDNN, the output v (k) of network is adjusted, under different driving cycles
Medial and lateral In-wheel motor driving wheel (9) (10) actual speed is made accurately and rapidly to follow target velocity, the control being optimal
Effect.
Step 4：
As shown in figure 5, according to the output v (k) of SPIDNN control modules, medial and lateral In-wheel motor driving wheel is calculated
(9) the PWM speed-regulating signals of (10) rotating speed adjust each driving wheel speed using medial and lateral motor driver (2) (3).According to step
Two detections obtain inside wheel hub motor driving wheel (9) actual speed v1' (k), inside wheel hub electricity is calculated further according to step 1
Machine driving wheel (9) target velocity is v1(k), using the deviation of actual speed and target velocity again as PID neuroids
The input of PIDNN control modules (12), i.e. repeatedly step 3, so as to complete to close to nearside wheel hub motor driving wheel (9) rotating speed
Ring controls.Similarly, when driving, outside wheel hub motor driving wheel (10) speed control uses Vehicular turn and inside wheel hub motor drives
The identical control method of driving wheel (9), makes that medial and lateral In-wheel motor driving wheel (9) (10) actual speed is quick respectively, accurately follows
Respective target velocity, so as to complete the differential control of medial and lateral In-wheel motor driving wheel (9) (10).
The system can carry out rational Electronic differential control according to electric vehicle difference Turning travel operating mode to two driving wheels,
Angular transducer and throttle position switch are communicated by existing CAN bus with Electronic differential control device, simple in structure；Vehicle
During Turning travel, according to medial and lateral driving wheel actual speed and the deviation of target velocity, calculated using PIDNN control modules
The closed-loop control to each driving wheel speed is completed to each driving wheel PWM speed-regulating signals, and by motor driver.Compared to PID
The advantages of controller, PIDNN control modules have had both PID controller and neuroid, adjusts in real time according to different operating modes
The output of PIDNN control modules improves the robustness of electronic differential control system, so as to remain optimal control effect
Fruit.
The above description is merely a specific embodiment, but protection scope of the present invention is not limited thereto, any
The change or replacement expected without creative work should all be covered subject to the scope of the present invention.
Claims (3)
1. wheel hub drives the electronic differential control system of electric vehicle, it is characterised in that：The system comprises Electronic differential controls
Device, In-wheel motor driving device, angular transducer, throttle position switch, driving wheel actual speed detection module；The angle passes
Sensor is used for measurement direction disk corner, and the driving wheel actual speed detection module is used to measure wheel hub motor actual speed, institute
Angular transducer and throttle position switch is stated to communicate with Electronic differential control device by CAN bus；
The Electronic differential control device includes Ackerman steering models computing module and PID neuroids PIDNN control moulds
Block；Vehicular turn angle δ and throttle position switch of the Ackerman steering model computing modules according to measured by angular transducer
Measured vehicle target travel speed vcValue, Vehicular turn medial and lateral driving wheel target velocity point when driving is calculated
It Wei not v1、v2；Ackerman steering models computing module is according to Vehicular turn angle δ and target travel speed vcValue be calculated
Vehicular turn medial and lateral driving wheel target velocity v when driving1、v2Expression formula is respectively：
In formula：K is rotated for medial and lateral to nodal distance, and L is wheelbase；
The practical speed of medial and lateral driving wheel is calculated by detecting wheel hub motor hall signal in driving wheel actual speed detection module
Degree is respectively v1'、v2'；
Using the deviation of medial and lateral driving wheel actual speed and target velocity as the input of PIDNN control modules, calculate
The closed-loop control to driving wheel speed is completed to each driving wheel PWM speed-regulating signals, and by motor driver, makes driving wheel practical
Speed follower target velocity realizes differential control.
2. the electronic differential control system of wheel hub driving electric vehicle according to claim 1, the PIDNN control modules
PID control rule is effectively merged with neuroid, SPIDNN is the most basic form of PIDNN, and basic structure is by one
A 2 × 3 × 1 three layers of Feedforward Neural Network composition：Respectively the input layer comprising two neurons, three neurons it is hidden
Output layer containing layer and a neuron；For SPIDNN single variable control systems, in order to make the set-point r of system control amount
And inputs of the deviation e of its value of feedback y as SPIDNN hidden layers, the connection weight initial value design of input layer to hidden layer are：
w1j=+1, w2j=-1
In formula：wijFor input layer to hidden layer connection weight weight values；
The selection principle of hidden layer to output layer connection weight initial value be make its output in connection weight initial value be equivalent to be
Output when system is using PID controller, therefore hidden layer to output layer connection weight initial value is respectively set as system using PID
The coefficient of each link during controller, i.e.,：
w'1=KP, w'2=KI, w'3=KD
In formula：w'jFor hidden layer to output layer connection weight weight values；KP、KI、KDRespectively PID controller ratio, integration, differential ring
Save coefficient；
When each connection weight refetches initial value, the output of SPIDNN control systems is：
In formula：K is sampling sequence number；E (k) is the deviation of the set-point r and its value of feedback y of kth time sampling instant system control amount；
E (k) is the deviation of the set-point r and its value of feedback y of -1 sampling instant system control amount of kth；
When each connection weights of SPIDNN refetch initial value, the output of control system is equivalent to PID controller output, possesses and is controlled with PID
The approximate control effect of device processed, then by inverse algorithms on-line training and learn to adjust network connection weight wij、w'jValue,
The advantages of so as to which SPIDNN be made to have both PID controller and neuroid, the robustness of lifting system reach preferably control effect
Fruit.
3. the electronic differential control system of wheel hub driving electric vehicle according to claim 2, it is characterised in that：The electronics
Differential control system is applied to front-wheel steer, two trailing wheels use the electric vehicle that wheel hub motor independently drives.
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| CN114734834A (en) * | 2022-05-05 | 2022-07-12 | 安徽理工大学环境友好材料与职业健康研究院(芜湖) | Electronic differential control system for double motors of rear axle of electric vehicle |
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| CN110001417A (en) * | 2019-04-24 | 2019-07-12 | 山西零度智控设备有限公司 | Double DC motor driver electronic differentials prevent car body from toppling control system and method |
| CN110209162A (en) * | 2019-05-06 | 2019-09-06 | 珠海达明科技有限公司 | A kind of 4 wheel driven AGV autonomous tracing in intelligent vehicle based on Ackermann steering principle |
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| CN113119753A (en) * | 2021-05-26 | 2021-07-16 | 合肥工业大学 | Distributed type electric-drive vehicle differential steering self-adaptive control method |
| CN113119753B (en) * | 2021-05-26 | 2023-02-28 | 合肥工业大学 | Distributed type electric-drive vehicle differential steering self-adaptive control method |
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| CN114454958A (en) * | 2021-12-31 | 2022-05-10 | 湖南中联重科智能高空作业机械有限公司 | Aerial work platform and control method and control system thereof |
| WO2023169231A1 (en) * | 2022-03-11 | 2023-09-14 | 南京泉峰科技有限公司 | Riding-type mowing device |
| CN114734834A (en) * | 2022-05-05 | 2022-07-12 | 安徽理工大学环境友好材料与职业健康研究院(芜湖) | Electronic differential control system for double motors of rear axle of electric vehicle |
| CN115583157A (en) * | 2022-10-14 | 2023-01-10 | 安徽奇米智能科技有限公司 | Agricultural implement bearing movement chassis system |
| CN115610243A (en) * | 2022-10-14 | 2023-01-17 | 安徽奇米智能科技有限公司 | Drive control method, terminal and storage medium for agricultural implement bearing motion chassis |
| CN115583157B (en) * | 2022-10-14 | 2024-09-10 | 安徽奇米智能科技有限公司 | Farm tool bearing movement chassis system |
| CN116461607A (en) * | 2023-05-12 | 2023-07-21 | 爱搏特科技(深圳)有限公司 | Distributed drive-by-wire and steering-by-wire method and related device |
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