CN109508015A - A kind of AGV electromagnetic navigation control system based on extension control - Google Patents
A kind of AGV electromagnetic navigation control system based on extension control Download PDFInfo
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Abstract
The AGV electromagnetic navigation control system based on extension control that the present invention relates to a kind of can solve the problems such as navigation system control domain is limited, environment adaptation is poor under Traditional control.The electromagnetic navigation control system includes: electromagnetic sensor, amplification demodulatoring circuit, 32 single-chip microcontrollers, extension controller, driving motor.Conducting wire is laid with along predefined paths, wherein conducting wire is connected with alternating current, the magnetic strength induction signal of generation is converted to electric signal through electromagnetic sensor, 32 single-chip microcontrollers are passed to by amplification demodulatoring circuit again, pass through extension controller again, it converts Path error amount to the duty ratio of driving motor, using the operation posture of driving wheel differential adjustment AGV, realizes automatic tracking function.
Description
Technical field
The invention belongs to AGV Navigation Control fields, control more particularly to a kind of AGV electromagnetic navigation based on extension control
System.
Background technique
Automatic guided vehicle (Automated Guided Vehicle, AGV) is to realize that production material carries automation
Important equipment and composition.AGV has good adaptation as a kind of higher electromechanical integration automatic equipment of integration ofTechnology degree
Property, flexibility, reliability and fault-tolerant ability are, it can be achieved that produce full-range automation and informationization, it is considered to be flexible manufacturing system
Optimal material transportation mode in system.Numerous characteristics of AGV make it be widely used in a variety of industries and field, such as stored goods
Stream, processing manufacturing industry, port harbour, tobacco chemical industry and special trade etc..The logistics structure of factory constantly changes, AGV's
Using the production efficiency that will significantly improve these fields.AGV navigation system is broadly divided into laser navigation, optical navigation, electromagnetism
Navigation, ultrasonic wave navigation etc..Wherein magnetic navigation is because cost is relatively low and high reliablity, therefore is widely used.
" extension science " be by Cai Wen teach headed by the new disciplines founded of Chinese scholars, it is ground with the model of formalization
The rule and method studying carefully a possibility that things is expanded and pioneering and inventing.By extensiontheory, extenics method is applied to control field place to go
Contradictory problems in reason control, referred to as extension control.The nineties, the Wang Hangyu of East China University of Science, Li Jian etc. deliver " opinion can open up
Control ", first proposed concept, definition and the basic framework of extension control.Pan Dong, the gold of Tsinghua University are delivered with intelligent etc.
" extension control and research ", studies the structure and specific implementation of extension controller, proposes two layers of extension controller
Concept.The Yang Gang of Guangdong University of Technology, remaining power etc. forever have delivered " improvement and simulation study based on extension control algorithm ", mention
A kind of improved extension control algorithm is gone out.
Currently, electromagnetic navigation AGV mostly uses traditional control method, such as PID control, fuzzy control, Control platform is higher.When
When AGV angle, position deviation are smaller, can quickly it correct, convergence curve is more smooth.But when operating path complexity, tradition control
Being limited in scope for system, when AGV angle, position deviation are larger, can not quickly eliminate deviation.Extension control is then with extension science
State relation degree is core, thinks that uncontrollable region is handled to traditional control method, expands control domain, thus will control
The uncontrollable of system processed is converted to controllable state.
The invention proposes a kind of new AGV electromagnetic navigation control systems based on extension control, can open up control by construction
Control domain is divided into three parts: Classical field, extension range, non-domain by device processed.Traditional control is used in Classical field, for promoting system
The Control platform of system；Maximum output is used in non-domain, guarantee system returns stability region as early as possible；Then using tradition control in extension range
The mode combined with maximum output is made, and introduces correlation function K (S) and determines the two weight, has expanded the control domain of system.
Summary of the invention
This specification proposes a kind of AGV electromagnetic navigation control system based on extension control.Electricity is laid in predefined paths
Conducting wire, wherein being connected with the fixed alternating current of frequency.AGV acquires magnetic field signal, the Strength Changes of magnetic induction by electromagnetic sensor
Represent the departure degree in path.Again by extension controller, PWM is converted by magnetic strength induction signal, controls turning for driving motor
Speed adjusts operation posture using driving wheel differential.Traditional control algolithm has good when AGV path deviation amount is smaller
Control effect, but control domain is limited, and fast convergence is difficult to when deviation is larger.System described herein can open up control by building
Device processed takes corresponding control algolithm in different domains, has effectively widened control domain.
The technical solution that the present invention uses to solve above-mentioned technical problem is as follows:
A kind of AGV electromagnetic navigation control system based on extension control, which includes: electromagnetic sensing
Device, amplification demodulatoring circuit, 32 single-chip microcontrollers, extension controller, driving motor, which is characterized in that AGV is laid out using four-wheel,
Be arranged symmetrically electromagnetic sensor on front side of two sides, be arranged symmetrically driving wheel on rear side of two sides, two driving motors respectively with master
Driving wheel connection；A pair of driven is set before and after the axis line position of AGV, and 32 single-chip microcontrollers pass through electromagnetism as master controller
Sensor detects routing information, realizes automatic tracking function using driving wheel differential.
It is laid with conducting wire in the predefined paths of AGV, the fixed alternating current of frequency is connected in conducting wire, alternating current can be around
The electromagnetic field of alternation is generated, the Distribution of Magnetic Field around conducting wire is a series of concentric circles using conducting wire as axis, the magnetic on same circle
Field intensity B size is identical, and as the radius r of distance of wire increases the decline that is inversely proportional, according to Biot-Sa farr's law, two sides
The magnetic induction intensity of sensor is respectively B1, B2, calculation formula is as follows:
Wherein I is current strength, r1,r2For sensor and conductor spacing；
Electromagnetic sensor model used by AGV, simplifies LC oscillating circuit, and resonance frequency isAccording to farad
The law of electromagnetic induction, inductance coil are located in alternating magnetic field, generate induced electromotive force E, and calculation formula is as follows:
Wherein A is the sectional area of inductance coil, and N is circle number；
Induced electromotive force E is weaker, after amplification demodulatoring circuit, is passed to Chip Microcomputer A/port D, amplification demodulatoring circuit is by same
It is constituted to proportional amplifier, amplification factor is as follows:
For port voltage after A/D is converted, digital quantity is denoted as m, from formula (1) (2) (3):
Wherein r is sensor and conductor spacing；K is constant, and with coil section product, circle number, the factors such as feedback resistance are related.
By formula (4) it is found that magnetic induction m size is inversely proportional with distance r.The deviation of two side sensers is denoted as Δ m=ml-
mr, the departure degree of numerical values recited expression AGV and predefined paths, symbol expression offset direction；
AGV steering mode is double driving wheel differential speed types, by the linear differential of two-wheeled away from realization turning function, it is assumed that tire
It is pure rolling between ground, road surface evenness is established preferably to establish the relationship between driving motor duty ratio and radius of turn
AGV steering model, point P are rotation center, Vl, VrThe respectively linear velocity of left and right two-wheeled, Rl, RrThe respectively rotation of left and right two-wheeled
Turn radius, D is two-wheeled spacing, Pl, PrFor the duty ratio of two sides driving motor, Δ Pl, Δ PrFor change in duty cycle amount, rotation half
The calculation formula of diameter R is as follows:
, should be by driving wheel differential mean allocation, i.e., to keep speed V constant when steering | Δ Pl|=| Δ Pr|=Δ P, draws
Enter extension controller, is input with magnetic induction deviation Δ m, the change in duty cycle amount Δ P of driving motor is output, and foundation can open up
Controller；
The realization of the extension controller is divided into the selection of characteristic quantity, the calculating of correlation function, the division of feature mode, control
The realization of algorithm processed；
The departure of magnetic induction is Δ m=ml-mr, numerical values recited represents the departure degree of AGV and predefined paths, just
Minus symbol represents offset direction, and the change rate of magnetic induction difference isIt can be expressed as follows:
Δ m (t) is the deviation of current sample time, and Δ m (t-1) is last moment deviation；
The selection of the characteristic quantity, with the quotient of deviation and change rate, i.e.,It is selected as characteristic quantity, indicates magnetic induction deviation
Variation tendency, by characteristic quantityIt is denoted as Ω；Its symbol is denoted as φ, and value range is denoted as ψ, and change of error trend is denoted as ξ, builds
Basic-element model shown under Liru: basic-element model J1: if Ω > 0 shows the inclined absolute value of the difference of magnetic induction | Δ m | increasing；
Basic-element model J2: as-α≤Ω < 0, show with current change rateAbsolute value of the bias | Δ m | it is being reduced rapidly；Primitive
Model J3If: Ω <-α, and show absolute value of the bias | and Δ m | reducing, but trend is unobvious.Wherein the selection of α is with AGV's
Operating condition is related, for dividing domain；
Domain is divided into " Classical field " " extension range " " non-domain ", wherein V1 is Classical field, and boundary is Ω ∈ (- α, 0)；V2
For extension range, boundary is Ω ∈ (- ∞ ,-α)；V3 is non-domain, and boundary is Ω ∈ (0 ,+∞)；
The Association function calculates as follows:
α is used for the parameter tuning of control algolithm for dividing domain boundary, β, and the two selection has with the operating condition of AGV
It closes, when (1) Ω ∈ (0 ,+∞), K (S) < -1, the inclined absolute value of the difference of magnetic induction | Δ m | increasing；(2) as Ω ∈ (- ∞ ,-α)
When, -1 < K (S) < 0, the inclined absolute value of the difference of magnetic induction | Δ m | it is gradually reduced but trend is slow；(3) as Ω ∈ (- α, 0), K (S)
>=0, magnetic induction absolute value of the bias | Δ m | it is being reduced rapidly；(4) as Δ m=0 orWhen, Association function K (S) nothing
Meaning.
The measure models are chosen, and as K (S) >=0, system is located at Classical field, takes M1Measure models；-1<K(S)<0
When, system is located at extension range, takes M2Measure models；When K (S) < -1, system is located at non-domain, takes M3Measure models；K (S) nothing
When meaning, M is divided into if Δ m=01Measure models,Then it is divided into M2Measure models；
Corresponding control algolithm is taken in the realization of the control algolithm under different measure models:
Measure models M1, system is in Classical field, and using fuzzy-adaptation PID control, formula is as follows:
Wherein Δ m (t) is the magnetic induction deviation at current time, and Δ m (t-1) is the magnetic induction deviation of last moment,
Because AGV navigation system is nonlinear system, Kp, KI, KDParameter tuning it is complex, therefore introduce fuzzy controller；
Measure models M2, system is in extension range, fuzzy-adaptation PID control combined with maximum output, introduces the degree of association
Function K (s) determines the two weight, k1=| 1-K (S) |, k2=| K (S) |, formula is as follows:
u2=k1U (fuzzy)+k2·u(max) (9)
Measure models M3, significant condition is in non-domain, and extension controller can not make uncontrollable state become controllable at this time
State, to make system return stability region as early as possible, output should take amplitude, and formula is as follows:
u3=u (max) (10)
By formula (8) (9) (10) it is found that the control algolithm of AGV navigation system is as follows:
The beneficial effects of the present invention are: propose a kind of AGV electromagnetic navigation control system based on extension control, the system
By constructing extension controller, corresponding control algolithm is taken in different domains, has effectively widened control domain, keeps system former
Come uncontrollable region to realize controllably.
Detailed description of the invention
Fig. 1 AGV mechanical structure schematic diagram.
Fig. 2 induced magnetic field distribution schematic diagram.
Fig. 3 amplification demodulatoring circuit figure.
Fig. 4 control flow chart.
Fig. 5 Path error schematic diagram.
Fig. 6 AGV turns to schematic diagram.
Fig. 7 extension controller structure chart.
Fig. 8 extension control domain divides figure.
Fig. 9 fuzzy structure chart.
Specific embodiment
With reference to the accompanying drawings and detailed description, the present invention is furture elucidated, it should be understood that following specific embodiments are only
For illustrating the present invention rather than limiting the scope of the invention.
Navigation system is AGV core component, and AGV navigation mode common at present mainly has vision guided navigation, laser to lead
Boat and magnetic navigation, vision guided navigation is due to easy its bad adaptability affected by environment, and the hardware cost of laser navigation is higher, and magnetic navigation is then
Simple, at low cost and strong antijamming capability is not only controlled, can work, be most widely used under circumstances.Magnetic navigation mode
It is divided into electromagnetic navigation and tape navigates, because of electromagnetic navigation strong antijamming capability, cost is relatively low, therefore uses which.
As shown in Figure 1, AGV is laid out using four-wheel, 32 single-chip microcontrollers are as master controller, by electromagnetic sensor to path
Information is detected, and realizes that automatic tracking function is symmetric wherein 1,2 is electromagnetic sensor using driving wheel differential；3,
4 be driving wheel；5,6 be driving motor；7,8 be driven wheel.
It is laid with conducting wire in the predefined paths of AGV, is connected with the fixed alternating current of frequency.It is managed according to Maxwell's electromagnetic field
By alternating current can generate the electromagnetic field of alternation around.Distribution of Magnetic Field around conducting wire is using conducting wire as a series of of axis
Concentric circles, the magnetic field strength B size on same circle is identical, and as the radius r of distance of wire increase is inversely proportional decline, such as Fig. 2
It is shown.According to Biot-Sa farr's law, the magnetic induction intensity of two side sensers is respectively B1, B2, calculation formula is as follows:
Wherein I is current strength, r1,r2For sensor and conductor spacing.
Electromagnetic sensor model used by AGV, can simplify LC oscillating circuit, and resonance frequency isAccording to
Faraday's electromagnetic induction law, inductance coil are located in alternating magnetic field, generate induced electromotive force E, and calculation formula is as follows:
Wherein A is the sectional area of inductance coil, and N is circle number.
Induced electromotive force E is weaker, and by circuit as shown in Figure 3, Chip Microcomputer A/port D is passed to after amplification.Amplification demodulator
Circuit is made of proportional amplifier in the same direction, and amplification factor is as follows:
For port voltage after A/D is converted, digital quantity is denoted as m.From formula (1) (2) (3):
Wherein r is sensor and conductor spacing；K is constant, and with coil section product, circle number, the factors such as feedback resistance are related.
By formula (4) it is found that magnetic induction m size is inversely proportional with distance r.The deviation of two side sensers is denoted as Δ m=ml-
mr, the departure degree of numerical values recited expression AGV and predefined paths, symbol expression offset direction, as shown in Figure 5.
AGV steering mode is double driving wheel differential speed types, by the linear differential of two-wheeled away from realization turning function.It might as well assume
It is pure rolling, road surface evenness between tire and ground.For the relationship preferably established between driving motor duty ratio and radius of turn, build
Liru AGV steering model shown in fig. 6.Point P is rotation center, Vl, VrThe respectively linear velocity of left and right two-wheeled, Rl, RrRespectively
The radius of turn of left and right two-wheeled, D are two-wheeled spacing, Pl, PrFor the duty ratio of two sides driving motor, Δ Pl, Δ PrFor duty ratio change
Change amount.The calculation formula of radius of turn R is as follows:
, should be by driving wheel differential mean allocation, i.e., to keep speed V constant when steering | Δ Pl|=| Δ Pr|=Δ P.Draw
Enter extension controller, is input with magnetic induction deviation Δ m, the change in duty cycle amount Δ P of driving motor is output, and foundation can open up
Controller.
The foundation of extension controller is mainly used for expanding control domain, thinks uncontrollable region to traditional control method
It is handled, so that the uncontrollable of control system is converted to controllable state.The structure of extension controller is as shown in fig. 7, be divided into
The selection of characteristic quantity, the calculating of correlation function, the division of feature mode, the realization of control algolithm.
The departure of magnetic induction is Δ m=ml-mr, numerical values recited represents the departure degree of AGV and predefined paths, just
Minus symbol represents offset direction.The change rate of magnetic induction difference isIt can be expressed as follows:
Δ m (t) is the deviation of current sample time, and Δ m (t-1) is last moment deviation.
The selection of the characteristic quantity, with the quotient of deviation and change rate, i.e.,It is selected as characteristic quantity, indicates magnetic induction deviation
Variation tendency.By characteristic quantityIt is denoted as Ω；Its symbol is denoted as φ, and value range is denoted as ψ, and change of error trend is denoted as ξ, builds
Basic-element model shown under Liru.Basic-element model J1: if Ω > 0 shows the inclined absolute value of the difference of magnetic induction | Δ m | increasing.
Basic-element model J2: as-α≤Ω < 0, show with current change rateAbsolute value of the bias | Δ m | it is being reduced rapidly.Primitive
Model J3If: Ω <-α, and show absolute value of the bias | and Δ m | reducing, but trend is unobvious.Wherein the selection of α is with AGV's
Operating condition is related, for dividing domain.
Domain is divided into " Classical field " " extension range " " non-domain ", as shown in Figure 8.Wherein, V1 is Classical field, and boundary is Ω ∈
(-α,0)；V2 is extension range, and boundary is Ω ∈ (- ∞ ,-α)；V3 is non-domain, and boundary is Ω ∈ (0 ,+∞).
The Association function calculates as follows:
α is used for the parameter tuning of control algolithm for dividing domain boundary, β, and the two selection has with the operating condition of AGV
It closes.(1) when Ω ∈ (0 ,+∞), K (S) < -1, the inclined absolute value of the difference of magnetic induction | Δ m | increasing.(2) as Ω ∈ (- ∞ ,-α)
When, -1 < K (S) < 0, the inclined absolute value of the difference of magnetic induction | Δ m | it is gradually reduced but trend is slow.(3) as Ω ∈ (- α, 0), K (S)
>=0, magnetic induction absolute value of the bias | Δ m | it is being reduced rapidly.(4) as Δ m=0 orWhen, Association function K (S) nothing
Meaning.
The measure models are chosen, as shown in table 1.As K (S) >=0, system is located at Classical field, takes M1Estimate mould
Formula；When -1 < K (S) < 0, system is located at extension range, takes M2Measure models；When K (S) < -1, system is located at non-domain, takes M3Estimate
Mode；When K (S) is meaningless, M is divided into if Δ m=01Measure models,Then it is divided into M2Measure models.
Table 1
Corresponding control algolithm is taken in the realization of the control algolithm under different measure models.
Measure models M1, system is in Classical field.Using fuzzy-adaptation PID control, formula is as follows:
Wherein Δ m (t) is the magnetic induction deviation at current time, and Δ m (t-1) is the magnetic induction deviation of last moment.
Because AGV navigation system is nonlinear system, Kp, KI, KDParameter tuning it is complex, therefore introduce fuzzy controller, structure as scheme
Shown in 9.
Measure models M2, system is in extension range, fuzzy-adaptation PID control combined with maximum output, introduces the degree of association
Function K (s) determines the two weight, k1=| 1-K (S) |, k2=| K (S) |.Formula is as follows:
u2=k1U (fuzzy)+k2·u(max) (9)
Measure models M3, significant condition is in non-domain.Extension controller can not make uncontrollable state become controllable at this time
State, to make system return stability region as early as possible, output should take amplitude.Formula is as follows:
u3=u (max) (10)
By formula (8) (9) (10) it is found that the control algolithm of AGV navigation system is as follows:
Although the illustrative specific embodiment of the present invention is described above, in order to the technology of the art
Personnel are it will be appreciated that the present invention, but the present invention is not limited only to the range of specific embodiment, to the common skill of the art
For art personnel, as long as long as various change the attached claims limit and determine spirit and scope of the invention in, one
The innovation and creation using present inventive concept are cut in the column of protection.
Claims (3)
1. a kind of AGV electromagnetic navigation control system based on extension control, which includes: electromagnetic sensing
Device, amplification demodulatoring circuit, 32 single-chip microcontrollers, extension controller, driving motor, which is characterized in that AGV is laid out using four-wheel,
Be arranged symmetrically electromagnetic sensor on front side of two sides, be arranged symmetrically driving wheel on rear side of two sides, two driving motors respectively with master
Driving wheel connection；A pair of driven is set before and after the axis line position of AGV, and 32 single-chip microcontrollers pass through electromagnetism as master controller
Sensor detects routing information, realizes automatic tracking function using driving wheel differential.
2. a kind of AGV electromagnetic navigation control system based on extension control as described in claim 1, which is characterized in that in AGV
Predefined paths in be laid with conducting wire, the fixed alternating current of frequency is connected in conducting wire, alternating current can generate the electricity of alternation around
Magnetic field, the Distribution of Magnetic Field around conducting wire are a series of concentric circles using conducting wire as axis, the magnetic field strength B size phase on same circle
Together, and as the radius r of distance of wire increases the decline that is inversely proportional, according to Biot-Sa farr's law, the magnetic induction of two side sensers
Intensity is respectively B1, B2, calculation formula is as follows:
Wherein I is current strength, r1,r2For sensor and conductor spacing；
Electromagnetic sensor model used by AGV, simplifies LC oscillating circuit, and resonance frequency isAccording to faraday's electricity
Law of magnetic induction, inductance coil are located in alternating magnetic field, generate induced electromotive force E, and calculation formula is as follows:
Wherein A is the sectional area of inductance coil, and N is circle number；
Induced electromotive force E is weaker, after amplification demodulatoring circuit, is passed to Chip Microcomputer A/port D, amplification demodulatoring circuit by comparing in the same direction
Example amplifier is constituted, and amplification factor is as follows:
For port voltage after A/D is converted, digital quantity is denoted as m, from formula (1) (2) (3):
Wherein r is sensor and conductor spacing；K is constant, and with coil section product, circle number, the factors such as feedback resistance are related.
By formula (4) it is found that magnetic induction m size is inversely proportional with distance r.The deviation of two side sensers is denoted as Δ m=ml-mr,
Numerical values recited indicates that the departure degree of AGV and predefined paths, symbol indicate offset direction；
AGV steering mode is double driving wheel differential speed types, by the linear differential of two-wheeled away from realization turning function, it is assumed that tire and ground
It is pure rolling between face, road surface evenness is established AGV and turned preferably to establish the relationship between driving motor duty ratio and radius of turn
To model, point P is rotation center, Vl, VrThe respectively linear velocity of left and right two-wheeled, Rl, RrThe respectively rotation of left and right two-wheeled half
Diameter, D are two-wheeled spacing, Pl, PrFor the duty ratio of two sides driving motor, Δ Pl, Δ PrFor change in duty cycle amount, radius of turn R's
Calculation formula is as follows:
, should be by driving wheel differential mean allocation, i.e., to keep speed V constant when steering | Δ Pl|=| Δ Pr|=Δ P, introducing can
Controller is opened up, is input with magnetic induction deviation Δ m, the change in duty cycle amount Δ P of driving motor is output, establishes extension control
Device.
3. a kind of AGV electromagnetic navigation control system based on extension control as claimed in claim 2, which is characterized in that described
The realization of extension controller is divided into the selection of characteristic quantity, the calculating of correlation function, the division of feature mode, the reality of control algolithm
It is existing；
The departure of magnetic induction is Δ m=ml-mr, numerical values recited represents the departure degree of AGV and predefined paths, positive and negative symbol
Number offset direction is represented, the change rate of magnetic induction difference isIt can be expressed as follows:
Δ m (t) is the deviation of current sample time, and Δ m (t-1) is last moment deviation；
The selection of the characteristic quantity, with the quotient of deviation and change rate, i.e.,It is selected as characteristic quantity, indicates the change of magnetic induction deviation
Change trend, by characteristic quantityIt is denoted as Ω；Its symbol is denoted as φ, and value range is denoted as ψ, and change of error trend is denoted as ξ, establishes such as
Basic-element model shown in lower: basic-element model J1: if Ω > 0 shows the inclined absolute value of the difference of magnetic induction | Δ m | increasing；Primitive
Model J2: as-α≤Ω < 0, show with current change rateAbsolute value of the bias | Δ m | it is being reduced rapidly；Basic-element model
J3If: Ω <-α, and show absolute value of the bias | and Δ m | reducing, but trend is unobvious.The wherein selection and the operation of AGV of α
Operating condition is related, for dividing domain；
Domain is divided into " Classical field " " extension range " " non-domain ", wherein V1 is Classical field, and boundary is Ω ∈ (- α, 0)；V2 is can
Domain is opened up, boundary is Ω ∈ (- ∞ ,-α)；V3 is non-domain, and boundary is Ω ∈ (0 ,+∞)；
The Association function calculates as follows:
α is used for the parameter tuning of control algolithm for dividing domain boundary, β, and the two selection is related with the operating condition of AGV,
(1) when Ω ∈ (0 ,+∞), K (S) < -1, the inclined absolute value of the difference of magnetic induction | Δ m | increasing；(2) as Ω ∈ (- ∞ ,-α)
When, -1 < K (S) < 0, the inclined absolute value of the difference of magnetic induction | Δ m | it is gradually reduced but trend is slow；(3) as Ω ∈ (- α, 0), K (S)
>=0, magnetic induction absolute value of the bias | Δ m | it is being reduced rapidly；(4) as Δ m=0 orWhen, Association function K (S) nothing
Meaning；
The measure models are chosen, and as K (S) >=0, system is located at Classical field, takes M1Measure models；When -1 < K (S) < 0, it is
System is located at extension range, takes M2Measure models；When K (S) < -1, system is located at non-domain, takes M3Measure models；K (S) is meaningless
When, M is divided into if Δ m=01Measure models,Then it is divided into M2Measure models；
Corresponding control algolithm is taken in the realization of the control algolithm under different measure models:
Measure models M1, system is in Classical field, and using fuzzy-adaptation PID control, formula is as follows:
Wherein Δ m (t) is the magnetic induction deviation at current time, and Δ m (t-1) is the magnetic induction deviation of last moment, because of AGV
Navigation system is nonlinear system, Kp, KI, KDParameter tuning it is complex, therefore introduce fuzzy controller；
Measure models M2, system is in extension range, fuzzy-adaptation PID control combined with maximum output, introduces Association function K
(s) the two weight, k are determined1=| 1-K (S) |, k2=| K (S) |, formula is as follows:
u2=k1U (fuzzy)+k2·u(max) (9)
Measure models M3, significant condition is in non-domain, and extension controller can not make uncontrollable state become controllable state at this time,
To make system return stability region as early as possible, output should take amplitude, and formula is as follows:
u3=u (max) (10)
By formula (8) (9) (10) it is found that the control algolithm of AGV navigation system is as follows:
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110512928A (en) * | 2019-08-27 | 2019-11-29 | 北京航空航天大学 | A kind of automobile carrying device and its method for carrying |
| CN111173327A (en) * | 2019-11-25 | 2020-05-19 | 北京航空航天大学 | Automatic centering device and method for car carrier |
| CN112945235A (en) * | 2021-01-29 | 2021-06-11 | 天津市科睿思奇智控技术有限公司 | Method for angle detection and safety protection of translation machine based on magnetic field detection |
| CN113156937A (en) * | 2021-02-05 | 2021-07-23 | 浙江亿控自动化设备有限公司 | Magnetic navigation control algorithm applied to double steering wheels |
| CN114167852A (en) * | 2020-09-11 | 2022-03-11 | 苏州科瓴精密机械科技有限公司 | Robot system and robot obstacle avoidance method based on magnetic field signals |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102662401A (en) * | 2012-05-25 | 2012-09-12 | 上海电力学院 | Electromagnetic sensing tracking based navigation system |
| CN106843223A (en) * | 2017-03-10 | 2017-06-13 | 武汉理工大学 | A kind of intelligent avoidance AGV cart systems and barrier-avoiding method |
| US20180017398A1 (en) * | 2016-07-12 | 2018-01-18 | Toyota Motor Engineering & Manufacturing North America, Inc. | Apparatus and method of determining an optimized route for a highly automated vechicle |
| CN107600176A (en) * | 2017-08-29 | 2018-01-19 | 江苏大学 | A kind of intelligent vehicle active steering control method theoretical based on extension control |
| CN108120434A (en) * | 2017-12-20 | 2018-06-05 | 东风汽车集团有限公司 | A kind of AGV tracks method for correcting error, system and double navigation system |
| CN108216231A (en) * | 2018-01-12 | 2018-06-29 | 合肥工业大学 | One kind can open up united deviation auxiliary control method based on steering and braking |
-
2018
- 2018-12-26 CN CN201811596939.3A patent/CN109508015B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102662401A (en) * | 2012-05-25 | 2012-09-12 | 上海电力学院 | Electromagnetic sensing tracking based navigation system |
| US20180017398A1 (en) * | 2016-07-12 | 2018-01-18 | Toyota Motor Engineering & Manufacturing North America, Inc. | Apparatus and method of determining an optimized route for a highly automated vechicle |
| CN106843223A (en) * | 2017-03-10 | 2017-06-13 | 武汉理工大学 | A kind of intelligent avoidance AGV cart systems and barrier-avoiding method |
| CN107600176A (en) * | 2017-08-29 | 2018-01-19 | 江苏大学 | A kind of intelligent vehicle active steering control method theoretical based on extension control |
| CN108120434A (en) * | 2017-12-20 | 2018-06-05 | 东风汽车集团有限公司 | A kind of AGV tracks method for correcting error, system and double navigation system |
| CN108216231A (en) * | 2018-01-12 | 2018-06-29 | 合肥工业大学 | One kind can open up united deviation auxiliary control method based on steering and braking |
Non-Patent Citations (2)
| Title |
|---|
| 潘东 等: "可拓控制的探索与研究", 《控制理论与应用》 * |
| 陈无畏 等: "基于功能分配的汽车悬架/转向***可拓控制及稳定性分析", 《机械工程学报》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110512928A (en) * | 2019-08-27 | 2019-11-29 | 北京航空航天大学 | A kind of automobile carrying device and its method for carrying |
| CN111173327A (en) * | 2019-11-25 | 2020-05-19 | 北京航空航天大学 | Automatic centering device and method for car carrier |
| CN111173327B (en) * | 2019-11-25 | 2021-03-05 | 北京航空航天大学 | Automatic centering device and method for car carrier |
| CN114167852A (en) * | 2020-09-11 | 2022-03-11 | 苏州科瓴精密机械科技有限公司 | Robot system and robot obstacle avoidance method based on magnetic field signals |
| WO2022052230A1 (en) * | 2020-09-11 | 2022-03-17 | 苏州科瓴精密机械科技有限公司 | Robot system, and robot obstacle avoidance method based on magnetic field signal |
| CN112945235A (en) * | 2021-01-29 | 2021-06-11 | 天津市科睿思奇智控技术有限公司 | Method for angle detection and safety protection of translation machine based on magnetic field detection |
| CN112945235B (en) * | 2021-01-29 | 2023-03-17 | 天津市科睿思奇智控技术有限公司 | Method for angle detection and safety protection of translation machine based on magnetic field detection |
| CN113156937A (en) * | 2021-02-05 | 2021-07-23 | 浙江亿控自动化设备有限公司 | Magnetic navigation control algorithm applied to double steering wheels |
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