CN104765368B - A kind of unmanned boat coursespeed cooperative control method based on fuzzy adaptive algorithm - Google Patents
A kind of unmanned boat coursespeed cooperative control method based on fuzzy adaptive algorithm Download PDFInfo
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Abstract
The invention discloses a kind of unmanned boat coursespeed cooperative control method based on fuzzy adaptive algorithm, for the more difficult accurate determination of motion model in conventional control methods, the problems such as dynamic response is undesirable, antijamming capability is weaker, fitness of the USV control methods under uncertain water conditions is improved using FUZZY ALGORITHMS FOR CONTROL, avoids the ship motion modeling problem in conventional method；Propose the improved output USV FUZZY ALGORITHMS FOR CONTROLs of two input two, add range ambiguity domain function to input as algorithm, and course and the speed of a ship or plane are all included into autonomous control scope, carry out Collaborative Control, the convergence rate of USV motion controls is improved, enhances the robustness of control；Using heading angle deviation rate as the uncertain measurement with running status extent of deviation of running environment, auto-adjustment control spacing parameter, enable change of the control algolithm according to control environment, adaptive adjustment control parameter, further enhancing the intelligent of self-control system.
Description
Technical field
The invention belongs to ship autokinetic movement control technology field, in particular to a kind of nothing based on fuzzy adaptive algorithm
People's ship coursespeed cooperative control method.
Background technology
Autokinetic movement control is that unmanned boat (Unmanned Surface Vehicle, abbreviation USV) is different from someone's manipulation
One of core technology of ship.In autokinetic movement control problem, practical and effective coursespeed control algolithm is that USV platforms exist
The key of complicated navigational duty is completed in various uncertain aquatic environments.
Current USV coursespeed control methods mainly include：(1) course is kept as target is automatically controlled, makes ship
Oceangoing ship can follow given course automatically, but course is given not in the range of autonomous control.Corresponding control algolithm is main
Including：The conventional control algorithms such as the PID control based on precise motion model, Lyapunov controls, Backstepping controls, base
In the adaptive control algorithms such as the variable-structure control of on-line identification model parameter, the FUZZY ALGORITHMS FOR CONTROL based on fuzzy rule etc..
(2) on the basis of being kept in course, by the given scope for including autonomous control in course, by ship carry computer by control algolithm from
It is main to draw course set-point, closed-loop control is carried out, but the speed of a ship or plane gives and is not included in the range of autonomous control.
Problems be present in above control method：(1) in terms of control algolithm, the conventional control based on mathematical models is calculated
In method, the more difficult accurate determination of ship motion model, and in uncertain and strong nonlinearity aquatic environment, LTI
Motion model parameters are difficult to the change of real-time and accurate description ship motion state；In adaptive control algorithm, adaptively
Function and the more difficult determination of accurate interference model；In FUZZY ALGORITHMS FOR CONTROL, algorithm structure, the fuzzy field of input/output variable, mould
The determination of paste rule needs abundant engineering experience.(2) in terms of control strategy, the Collaborative Control degree of coursespeed compared with
Weak, the scope of autonomous control algorithm is only included course in the main contribution for considering course to motion control results, and less consideration
The speed of a ship or plane is to motion control rapidity, the influence of robustness and independence.
The content of the invention
The main object of the present invention is in view of the shortcomings of the prior art, there is provided a kind of nobody based on fuzzy adaptive algorithm
Ship coursespeed Collaborative Control algorithm, avoids the ship motion modeling problem in conventional method, improves fuzzy control strategy
And algorithm, adaptive factor has been incorporated, has improved adaptability and robustness of the control method in uncertain aquatic environment.
To achieve the above object, a kind of unmanned boat coursespeed association based on fuzzy adaptive algorithm designed by the present invention
Same control method, comprises the following steps：
Comprise the following steps：
Step 1：Obtain and set unmanned boat autonomous navigation target point latitude and longitude coordinates；
Step 2：Gather longitude and latitude, the course angle of unmanned boat：
Step 3：Whether identical compare longitude and latitude and the unmanned boat autonomous navigation target point longitude and latitude of the collection, phase
Same then return to step 1, differs, continues next step；
Step 4：According to the longitude and latitude of the collection and course angle calculate unmanned boat and target point heading angle deviation amount P and
Air line distance departure D；
Step 5：The solution of fuzzy adaptivecontroller algorithm is carried out to the heading angle deviation amount P and air line distance departure D
Calculate, obtain the membership function of the heading angle deviation amount P and air line distance departure D in fuzzy set；
Step 6：By the membership function meter of the heading angle deviation amount P and air line distance departure D in fuzzy set
Calculate and export rudder angle deflection controlled quentity controlled variable U and accelerator open degree controlled quentity controlled variable V, the rudder angle deflects controlled quentity controlled variable U and accelerator open degree controlled quentity controlled variable V
Correspond respectively to the course of unmanned boat and speed of a ship or plane control；
Step 7：By some cycles time circulate operation step 2 to 6.
Preferably, the specific steps of the step 4 include：
Step 4.1：Range deviation the amount X, Yi Dongwei in longitudinal are obtained according to the longitude and latitude course angle of the collection
Positive direction, the range deviation amount Y on latitude direction, to the north of be positive direction, course angle θ, using real north as reference；
Step 4.2：It is obtained by the following formula the heading angle deviation amount P and air line distance departure D：
Preferably, the specific steps of the step 5 include：
Step 5.1：Heading angle deviation amount membership function is introduced to the heading angle deviation amount P
Pj：
The membership function of heading angle deviation amount described in formula PjBe an identification number be j fuzzy set, j ∈ integers,Belong to the membership function of each fuzzy set for departure；
Step 5.2：Air line distance departure degree of membership is introduced to the air line distance departure D
Function Dj：
The membership function of air line distance departure described in formula DjBe an identification number be j fuzzy set, j ∈ integers,Belong to the membership function of each fuzzy set for departure.
Preferably, the heading angle deviation amount membership function P of the step 5.1jIn, j=-2 is made, -1,0,1,2 generation respectively
The big NB of table negative sense, negative sense small NS, zero-deviation ZE, positive small PS, positive big PB, PjMembership function clusterFor：
The air line distance departure membership function D of the step 5.2jIn, j=0 is made, 1,2 represents zero-deviation Z respectively,
Little deviation S, large deviation B, DjMembership function clusterFor：
Preferably, the specific steps of the step 6 include：
Step 6.1：Rudder angle deflection controlled quentity controlled variable membership function is introduced to rudder angle deflection controlled quentity controlled variable U
The deflection controlled quentity controlled variable membership function of rudder angle described in formula UjBe an identification number be j fuzzy set, j ∈ integers,Belong to the membership function of each fuzzy set for departure, make j=-2, -1,0,1,2 to represent negative sense respectively big
NB, negative sense small NS, zero-deviation ZE, positive small PS, positive big PB, UjMembership function clusterFor：
Step 6.2：Accelerator open degree controlled quentity controlled variable membership function V is introduced to the accelerator open degree controlled quentity controlled variable Vj,
The membership function of accelerator open degree controlled quentity controlled variable described in formula VjIt is the fuzzy set that an identification number is j, j ∈ are from whole
Number,Belong to the membership function of each fuzzy set for departure, make j=0,1,2 represents zero throttle Z, small oil respectively
Door S, large throttle B, VjMembership function clusterFor：
Step 6.3：Introduce intermediate variable ωi、ωk, i ∈ integers, k ∈ integers, calculation formula is：
Wherein symbol " ∧ ", which refers to, takes small, that is, takes common factor；
Step 6.4：According to the intermediate variable ωi、ωkRudder angle deflection controlled quentity controlled variable U is calculated, calculation formula is：
Step 6.5：According to the intermediate variable ωi、ωkCalculation of throttle aperture controlled quentity controlled variable V, calculation formula are：
Preferably, also include after the step 6.5：
Step 6.6：Unit of account time heading angle deviation rate Pt=P/t, unit take angle degrees second；
Step 6.7：As the heading angle deviation rate PtDuring 30 jiaos of degrees seconds of ＞, the control interval is 1 second；As 30 jiaos of degrees second ＞
The heading angle deviation rate PtDuring 5 jiaos of degrees seconds of ＞, the control interval is 2 seconds；As the heading angle deviation rate Pt<During 5 jiaos of degrees seconds,
Control interval is 5 seconds.
For the more difficult accurate determination of motion model in conventional control methods, dynamic response is undesirable, antijamming capability is weaker；
Auto-adaptive function and the more difficult determination of accurate interference model in adaptive control algorithm；Algorithm structure in FUZZY ALGORITHMS FOR CONTROL, input
The fuzzy field of output variable, the more difficult determination of fuzzy rule and optimization；The problems such as coursespeed collaboration degree is weaker.The present invention proposes
A kind of unmanned boat coursespeed cooperative control method based on fuzzy adaptive algorithm.
The present invention the advantage is that compared with prior art：(1) USV control methods are improved using FUZZY ALGORITHMS FOR CONTROL to exist
Fitness under uncertain water conditions, avoids the ship motion modeling problem in conventional method；(2) with classical fuzzy control
Based on, it is proposed that the improved output USV FUZZY ALGORITHMS FOR CONTROLs of two input two, it is defeated as algorithm to add range ambiguity domain function
Enter, and course and the speed of a ship or plane all included into autonomous control scope, carry out Collaborative Control, improve the convergence rate of USV motion controls,
Enhance the robustness of control；(3) control auto-adjustment control spacing parameter, is made as independent variable using heading angle deviation rate
Algorithm adaptive adjustment control parameter, can further enhancing the intelligence of self-control system according to the change of control effect
Can property.
Brief description of the drawings
Fig. 1 unmanned boat fuzzy self-adaption coursespeed Collaborative Control algorithm flow charts；
Fig. 2 heading angle deviation amount P membership function curves；
Fig. 3 air line distance departure D membership function curves；
Fig. 4 rudder angles deflect controlled quentity controlled variable U membership function curves；
Fig. 5 accelerator open degree controlled quentity controlled variable V membership function curves；
Fig. 6 rudder angles deflect controlled quentity controlled variable fuzzy inference rule；
Fig. 7 accelerator open degree controlled quentity controlled variable fuzzy inference rules；
Fig. 8 testcraft sea trial result curves.
Embodiment
Below in conjunction with the drawings and specific embodiments, the invention will be described in further detail.Unmanned boat fuzzy self-adaption course
Speed of a ship or plane Collaborative Control algorithm flow chart is as shown in figure 1, comprise the following steps：
Step 1：Obtain and set unmanned boat autonomous navigation target point latitude and longitude coordinates；
Step 2：Gather longitude and latitude, the course angle of unmanned boat：
Step 3：Whether identical compare longitude and latitude and the unmanned boat autonomous navigation target point longitude and latitude of collection, it is identical, return
Step 1, differ, continue next step；
Step 4：The heading angle deviation amount P and straight line of unmanned boat and target point are calculated according to the longitude and latitude of collection and course angle
Range deviation amount D；Specific steps include：
Step 4.1：According to the longitude and latitude of collection and course angle obtain in longitudinal range deviation amount X (to the east of be just
Direction), range deviation amount Y on latitude direction (to the north of be positive direction), target course θ (using real north as reference)；
Step 4.2：It is obtained by the following formula heading angle deviation amount P and air line distance departure D：
Step 5：The resolving of fuzzy adaptivecontroller algorithm is carried out to heading angle deviation amount P and air line distance departure D, is obtained
Obtain the membership function of heading angle deviation amount P and air line distance departure D in fuzzy set；Specific steps include：
Step 5.1：Heading angle deviation amount membership function P is introduced to heading angle deviation amount Pj：
Heading angle deviation amount membership function P in formulajBe an identification number be j fuzzy set, j ∈ integers,For
Departure belongs to the membership function of each fuzzy set；
In heading angle deviation amount membership function PjIn, j=-2 is made, -1,0,1,2 represents NB (negative sense is big), NS respectively
(negative sense is small), ZE (zero-deviation), PS (positive small), PB (positive big), PjMembership function clusterFor：
Membership function clusterAs shown in Fig. 2 design parameter is drawn by experiment and expertise.
Step 5.2：Air line distance departure membership function is introduced to air line distance departure D
Dj：
Formula cathetus range deviation amount membership function DjBe an identification number be j fuzzy set, j ∈ integers,
Belong to the membership function of each fuzzy set for departure.
In air line distance departure membership function DjIn, j=0 is made, 1,2 represents Z (zero-deviation) respectively, and S is (small partially
Difference), B (large deviation), DjMembership function clusterFor：
Membership function clusterAs shown in figure 3, design parameter is drawn by experiment and expertise.
Step 6：Calculated simultaneously by the membership function of heading angle deviation amount P and air line distance departure D in fuzzy set
Rudder angle deflection controlled quentity controlled variable U and accelerator open degree controlled quentity controlled variable V is exported, rudder angle deflection controlled quentity controlled variable U and accelerator open degree controlled quentity controlled variable V is corresponded to respectively
In to the course of unmanned boat and speed of a ship or plane control.Specific steps include：
Step 6.1：Rudder angle deflection controlled quentity controlled variable membership function U is introduced to rudder angle deflection controlled quentity controlled variable Uj,
Formula centered rudder angular deflection controlled quentity controlled variable membership function UjThe fuzzy set that an identification number is j, j ∈ from integer,As departure belongs to the membership function of each fuzzy set, and rudder angle deflection controlled quentity controlled variable U fuzzy inference rule is such as
Shown in Fig. 6.J=-2 is made, -1,0,1,2 represents NB (negative sense is big), NS (negative sense is small) respectively, ZE (zero-deviation), PS (positive small),
PB (positive big), UjMembership function clusterFor：
Step 6.2：Accelerator open degree controlled quentity controlled variable membership function V is introduced to accelerator open degree controlled quentity controlled variable Vj,
Accelerator open degree controlled quentity controlled variable membership function V in formulajBe an identification number be j fuzzy set, j ∈ integers,
Belong to the membership function of each fuzzy set for departure.Accelerator open degree controlled quentity controlled variable V fuzzy inference rule is as shown in Figure 7.j
=0,1,2 represents Z (zero throttle), S (small throttle), B (large throttle), V respectivelyjMembership function clusterFor：
Fuzzy inference rule table is formulated, as shown in Figure 6 and Figure 7, is counted by heading angle deviation amount P and air line distance departure D
Calculate output rudder angle deflection controlled quentity controlled variable U and accelerator open degree controlled quentity controlled variable V, U and V correspond to the control of unmanned boat angular velocity of satellite motion and linear velocity
Amount processed, wherein Fig. 6 are the fuzzy inference rule that rudder angle deflects controlled quentity controlled variable U, and the fuzzy reasoning that Fig. 7 is accelerator open degree controlled quentity controlled variable V is advised
Then, last controlled quentity controlled variable U and V de-fuzzy specific steps include：
Step 6.3：Introduce intermediate variable ωi、ωk, i ∈ integers, k ∈ integers, calculation formula is：
Wherein symbol " ∧ ", which refers to, takes small, that is, takes common factor；
Step 6.4：According to intermediate variable ωi、ωkRudder angle deflection controlled quentity controlled variable U is calculated, calculation formula is：
Step 6.5：According to intermediate variable ωi、ωkCalculation of throttle aperture controlled quentity controlled variable V, calculation formula are：
The specific steps that control interval adaptively adjusts include：
Step 6.6：Unit of account time heading angle deviation rate Pt=P/t, unit take angle degrees second (°/S)；
Step 6.7：By heading angle deviation rate PtDrawn according to the extent of deviation of the uncertainty of running environment and running status
It is divided into 3 grades.As heading angle deviation rate PtDuring 30 jiaos of degrees seconds of ＞, large deviation state is represented, frequency, control interval are controlled using height
For 1 second；As 30 jiaos of degrees second ＞ heading angle deviation rates PtDuring 5 jiaos of degrees seconds of ＞, general bias state is represented, frequency is controlled in
Rate, control interval are 2 seconds；As deviation ratio Pt<During 5 jiaos of degrees seconds, little deviation state is represented, using low control frequency, control interval
For 5 seconds.Design parameter is drawn by experiment and expertise.
Step 7：By some cycles time circulate operation step 2 to 6.
Measurement result is analyzed
In order to verify the validity of unmanned boat fuzzy self-adaption coursespeed Collaborative Control algorithm proposed by the present invention and Shandong
Rod, carried out on actual unmanned boat test platform under wind, unrestrained disturbed condition, the autonomous navigation experiment of static target tracking,
Part of test results is as shown in Figure 8 obtained.
As a result show, the algorithm can not carry out the complicated and unknown situation of unmanned boat motion modeling, disturbed condition
Under, driving testcraft independently tracks target, and flight path is smooth, strong interference immunity.During the navigation that horizontal stormy waves interference be present,
Mesh, can fast and accurately be arrived at autonomous Adjustment Tests ship course and the speed of a ship or plane according to real-time course deviation and range deviation
Punctuate.
Claims (4)
1. a kind of unmanned boat coursespeed cooperative control method based on fuzzy adaptive algorithm, it is characterised in that including as follows
Step：
Step 1：Obtain and set unmanned boat autonomous navigation target point latitude and longitude coordinates；
Step 2：Gather longitude and latitude, the course angle of unmanned boat：
Step 3：Whether identical compare longitude and latitude and the unmanned boat autonomous navigation target point longitude and latitude of the collection, it is identical then
Return to step 1, is differed, and continues next step；
Step 4：The heading angle deviation amount P and air line distance of unmanned boat and target point are calculated according to the longitude and latitude of collection and course angle
Departure D；
Step 5：The resolving of fuzzy adaptivecontroller algorithm is carried out to the heading angle deviation amount P and air line distance departure D, is obtained
Obtain the membership function of the heading angle deviation amount P and air line distance departure D in fuzzy set；
Step 6：Calculated simultaneously by the membership function of the heading angle deviation amount P and air line distance departure D in fuzzy set
Rudder angle deflection controlled quentity controlled variable U and accelerator open degree controlled quentity controlled variable V is exported, rudder angle deflection controlled quentity controlled variable U and accelerator open degree controlled quentity controlled variable V is corresponded to respectively
In to the course of unmanned boat and speed of a ship or plane control；
Step 7：By some cycles time circulate operation step 2 to 6；
The specific steps of the step 6 include：
Step 6.1：Rudder angle deflection controlled quentity controlled variable membership function is introduced to rudder angle deflection controlled quentity controlled variable U
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<mtr>
<mtd>
<mn>0</mn>
<mo>,</mo>
<mi>x</mi>
<mo>></mo>
<mn>10</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
<mrow>
<msub>
<mi>&mu;</mi>
<msub>
<mi>V</mi>
<mn>1</mn>
</msub>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mn>1</mn>
<mo>-</mo>
<mo>|</mo>
<mfrac>
<mrow>
<mi>x</mi>
<mo>-</mo>
<mn>30</mn>
</mrow>
<mn>30</mn>
</mfrac>
<mo>|</mo>
<mo>,</mo>
<mo>|</mo>
<mi>x</mi>
<mo>-</mo>
<mn>30</mn>
<mo>|</mo>
<mo><</mo>
<mn>30</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
<mo>,</mo>
<mo>|</mo>
<mi>x</mi>
<mo>-</mo>
<mn>30</mn>
<mo>|</mo>
<mo>&GreaterEqual;</mo>
<mn>30</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
<mrow>
<msub>
<mi>&mu;</mi>
<msub>
<mi>V</mi>
<mn>2</mn>
</msub>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mn>0</mn>
<mo>,</mo>
<mi>x</mi>
<mo>&le;</mo>
<mn>30</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mfrac>
<mrow>
<mi>x</mi>
<mo>-</mo>
<mn>30</mn>
</mrow>
<mn>30</mn>
</mfrac>
<mo>,</mo>
<mn>30</mn>
<mo><</mo>
<mi>x</mi>
<mo>&le;</mo>
<mn>60</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>1</mn>
<mo>,</mo>
<mi>x</mi>
<mo>></mo>
<mn>60</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>;</mo>
</mrow>
Step 6.3：Introduce intermediate variable ωi、ωk, i ∈ integers, k ∈ integers, calculation formula is：
Wherein symbol " ∧ ", which refers to, takes small, that is, takes common factor；For PiMembership function cluster,For PkDegree of membership letter
Number cluster,For DiMembership function cluster,For DkMembership function cluster；
Step 6.4：According to the intermediate variable ωi、ωkRudder angle deflection controlled quentity controlled variable U is calculated, calculation formula is：
μU(x)=[ωi∧μUi(x)]∪[ωk∧μUk(x)]
<mrow>
<mi>U</mi>
<mo>=</mo>
<mfrac>
<mrow>
<mo>&Integral;</mo>
<msub>
<mi>x&mu;</mi>
<mi>U</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mi>d</mi>
<mi>x</mi>
</mrow>
<mrow>
<mo>&Integral;</mo>
<msub>
<mi>&mu;</mi>
<mi>U</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mi>d</mi>
<mi>x</mi>
</mrow>
</mfrac>
<mo>;</mo>
</mrow>
Step 6.5：According to the intermediate variable ωi、ωkCalculation of throttle aperture controlled quentity controlled variable V, calculation formula are：
μV(x)=[ωi∧μVi(x)]∪[ωk∧μVk(x)]
<mrow>
<mi>V</mi>
<mo>=</mo>
<mfrac>
<mrow>
<mo>&Integral;</mo>
<msub>
<mi>x&mu;</mi>
<mi>V</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mi>d</mi>
<mi>x</mi>
</mrow>
<mrow>
<mo>&Integral;</mo>
<msub>
<mi>&mu;</mi>
<mi>V</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mi>d</mi>
<mi>x</mi>
</mrow>
</mfrac>
<mo>;</mo>
</mrow>
Step 6.6：Unit of account time heading angle deviation rate Pt=P/t, unit take angle degrees second；
Step 6.7：As the heading angle deviation rate PtDuring 30 jiaos of degrees seconds of ＞, the control interval is 1 second；When described in 30 jiaos of degrees second ＞
Heading angle deviation rate PtDuring 5 jiaos of degrees seconds of ＞, the control interval is 2 seconds；As the heading angle deviation rate Pt<During 5 jiaos of degrees seconds, control
At intervals of 5 seconds.
2. a kind of unmanned boat coursespeed cooperative control method based on fuzzy adaptive algorithm according to claim 1,
It is characterized in that：The specific steps of the step 4 include：
Step 4.1：The range deviation amount X in longitudinal is obtained according to the longitude and latitude of the collection and course angle, to the east of be just
Direction, the range deviation amount Y on latitude direction, to the north of be positive direction, course angle θ, using real north as reference；
Step 4.2：It is obtained by the following formula the heading angle deviation amount P and air line distance departure D：
<mrow>
<mi>P</mi>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mi>&theta;</mi>
<mo>+</mo>
<mi>a</mi>
<mi>r</mi>
<mi>c</mi>
<mi>t</mi>
<mi>a</mi>
<mi>n</mi>
<mo>(</mo>
<mfrac>
<mi>Y</mi>
<mi>X</mi>
</mfrac>
<mo>)</mo>
<mo>+</mo>
<mn>90</mn>
<mo>...</mo>
<mo>...</mo>
<mi>X</mi>
<mo><</mo>
<mn>0</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mi>&theta;</mi>
<mo>+</mo>
<mi>a</mi>
<mi>r</mi>
<mi>c</mi>
<mi>t</mi>
<mi>a</mi>
<mi>n</mi>
<mo>(</mo>
<mfrac>
<mi>Y</mi>
<mi>X</mi>
</mfrac>
<mo>)</mo>
<mo>-</mo>
<mn>90</mn>
<mo>...</mo>
<mo>...</mo>
<mi>X</mi>
<mo>></mo>
<mn>0</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
<mrow>
<mi>D</mi>
<mo>=</mo>
<msqrt>
<mrow>
<mo>(</mo>
<msup>
<mi>X</mi>
<mn>2</mn>
</msup>
<mo>+</mo>
<msup>
<mi>Y</mi>
<mn>2</mn>
</msup>
<mo>)</mo>
</mrow>
</msqrt>
<mo>.</mo>
</mrow>
3. a kind of unmanned boat coursespeed cooperative control method based on fuzzy adaptive algorithm according to claim 1,
It is characterized in that：The specific steps of the step 5 include：
Step 5.1：Heading angle deviation amount membership function is introduced to the heading angle deviation amount P
Pj：
<mrow>
<msub>
<mi>P</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mo>{</mo>
<mi>x</mi>
<mo>,</mo>
<msub>
<mi>&mu;</mi>
<msub>
<mi>P</mi>
<mi>j</mi>
</msub>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
<mi>x</mi>
<mo>&Element;</mo>
<mo>&lsqb;</mo>
<mn>0</mn>
<mo>,</mo>
<mn>360</mn>
<mo>&rsqb;</mo>
<mo>}</mo>
</mrow>
The membership function of heading angle deviation amount described in formula PjBe an identification number be j fuzzy set, j ∈ integers,For
Departure belongs to the membership function of each fuzzy set；
Step 5.2：Air line distance departure degree of membership is introduced to the air line distance departure D
Function Dj：
<mrow>
<msub>
<mi>D</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mo>{</mo>
<mi>x</mi>
<mo>,</mo>
<msub>
<mi>&mu;</mi>
<msub>
<mi>D</mi>
<mi>j</mi>
</msub>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
<mi>x</mi>
<mo>&Element;</mo>
<mo>&lsqb;</mo>
<mn>0</mn>
<mo>,</mo>
<mi>&infin;</mi>
<mo>)</mo>
<mo>}</mo>
</mrow>
The membership function of air line distance departure described in formula DjBe an identification number be j fuzzy set, j ∈ integers,
Belong to the membership function of each fuzzy set for departure.
4. a kind of unmanned boat coursespeed cooperative control method based on fuzzy adaptive algorithm according to claim 3,
It is characterized in that：The heading angle deviation amount membership function P of the step 5.1jIn, make j=-2, -1,0,1,2 represent respectively it is negative
To big NB, negative sense small NS, zero-deviation ZE, positive small PS, positive big PB, PjMembership function clusterFor：
<mrow>
<msub>
<mi>&mu;</mi>
<msub>
<mi>P</mi>
<mrow>
<mo>-</mo>
<mn>2</mn>
</mrow>
</msub>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mn>1</mn>
<mo>-</mo>
<mo>|</mo>
<mfrac>
<mrow>
<mi>x</mi>
<mo>-</mo>
<mn>50</mn>
</mrow>
<mn>50</mn>
</mfrac>
<mo>|</mo>
<mo>,</mo>
<mo>|</mo>
<mi>x</mi>
<mo>-</mo>
<mn>50</mn>
<mo>|</mo>
<mo><</mo>
<mn>50</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
<mo>,</mo>
<mo>|</mo>
<mi>x</mi>
<mo>-</mo>
<mn>50</mn>
<mo>|</mo>
<mo>&GreaterEqual;</mo>
<mn>50</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
<mrow>
<msub>
<mi>&mu;</mi>
<msub>
<mi>P</mi>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mn>1</mn>
<mo>-</mo>
<mo>|</mo>
<mfrac>
<mrow>
<mi>x</mi>
<mo>-</mo>
<mn>120</mn>
</mrow>
<mn>60</mn>
</mfrac>
<mo>|</mo>
<mo>,</mo>
<mo>|</mo>
<mi>x</mi>
<mo>-</mo>
<mn>120</mn>
<mo>|</mo>
<mo><</mo>
<mn>60</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
<mo>,</mo>
<mo>|</mo>
<mi>x</mi>
<mo>-</mo>
<mn>120</mn>
<mo>|</mo>
<mo>&GreaterEqual;</mo>
<mn>60</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
<mrow>
<msub>
<mi>&mu;</mi>
<msub>
<mi>P</mi>
<mn>0</mn>
</msub>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mn>1</mn>
<mo>-</mo>
<mo>|</mo>
<mfrac>
<mrow>
<mi>x</mi>
<mo>-</mo>
<mn>180</mn>
</mrow>
<mn>30</mn>
</mfrac>
<mo>|</mo>
<mo>,</mo>
<mo>|</mo>
<mi>x</mi>
<mo>-</mo>
<mn>180</mn>
<mo>|</mo>
<mo><</mo>
<mn>30</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
<mo>,</mo>
<mo>|</mo>
<mi>x</mi>
<mo>-</mo>
<mn>180</mn>
<mo>|</mo>
<mo>&GreaterEqual;</mo>
<mn>30</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
<mrow>
<msub>
<mi>&mu;</mi>
<msub>
<mi>P</mi>
<mn>1</mn>
</msub>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mn>1</mn>
<mo>-</mo>
<mo>|</mo>
<mfrac>
<mrow>
<mi>x</mi>
<mo>-</mo>
<mn>240</mn>
</mrow>
<mn>60</mn>
</mfrac>
<mo>|</mo>
<mo>,</mo>
<mo>|</mo>
<mi>x</mi>
<mo>-</mo>
<mn>240</mn>
<mo>|</mo>
<mo><</mo>
<mn>60</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
<mo>,</mo>
<mo>|</mo>
<mi>x</mi>
<mo>-</mo>
<mn>240</mn>
<mo>|</mo>
<mo>&GreaterEqual;</mo>
<mn>60</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
3
<mrow>
<msub>
<mi>&mu;</mi>
<msub>
<mi>P</mi>
<mn>2</mn>
</msub>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mn>1</mn>
<mo>-</mo>
<mo>|</mo>
<mfrac>
<mrow>
<mi>x</mi>
<mo>-</mo>
<mn>310</mn>
</mrow>
<mn>50</mn>
</mfrac>
<mo>|</mo>
<mo>,</mo>
<mo>|</mo>
<mi>x</mi>
<mo>-</mo>
<mn>310</mn>
<mo>|</mo>
<mo><</mo>
<mn>50</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
<mo>,</mo>
<mo>|</mo>
<mi>x</mi>
<mo>-</mo>
<mn>310</mn>
<mo>|</mo>
<mo>&GreaterEqual;</mo>
<mn>50</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>;</mo>
</mrow>
The air line distance departure membership function D of the step 5.2jIn, j=0 is made, 1,2 represents zero-deviation Z, little deviation respectively
S, large deviation B, DjMembership function clusterFor：
<mrow>
<msub>
<mi>&mu;</mi>
<msub>
<mi>D</mi>
<mn>0</mn>
</msub>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mn>1</mn>
<mo>-</mo>
<mfrac>
<mi>x</mi>
<mn>50</mn>
</mfrac>
<mo>,</mo>
<mn>0</mn>
<mo><</mo>
<mi>x</mi>
<mo>&le;</mo>
<mn>50</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
<mo>,</mo>
<mi>x</mi>
<mo>></mo>
<mn>50</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
<mrow>
<msub>
<mi>&mu;</mi>
<msub>
<mi>D</mi>
<mn>1</mn>
</msub>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mn>1</mn>
<mo>-</mo>
<mo>|</mo>
<mfrac>
<mrow>
<mi>x</mi>
<mo>-</mo>
<mn>50</mn>
</mrow>
<mn>50</mn>
</mfrac>
<mo>|</mo>
<mo>,</mo>
<mo>|</mo>
<mi>x</mi>
<mo>-</mo>
<mn>50</mn>
<mo>|</mo>
<mo><</mo>
<mn>50</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
<mo>,</mo>
<mo>|</mo>
<mi>x</mi>
<mo>-</mo>
<mn>50</mn>
<mo>|</mo>
<mo>&GreaterEqual;</mo>
<mn>50</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
<mrow>
<msub>
<mi>&mu;</mi>
<msub>
<mi>D</mi>
<mn>2</mn>
</msub>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mn>0</mn>
<mo>,</mo>
<mi>x</mi>
<mo>&le;</mo>
<mn>50</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mfrac>
<mrow>
<mi>x</mi>
<mo>-</mo>
<mn>50</mn>
</mrow>
<mn>50</mn>
</mfrac>
<mo>,</mo>
<mn>50</mn>
<mo><</mo>
<mi>x</mi>
<mo>&le;</mo>
<mn>100</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>1</mn>
<mo>,</mo>
<mi>x</mi>
<mo>></mo>
<mn>100</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>.</mo>
</mrow>
4
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| CN105775092A (en) * | 2016-01-25 | 2016-07-20 | 武汉尼维智能科技有限公司 | Course control system and method for unmanned surface vehicle |
| CN105785974B (en) * | 2016-01-27 | 2018-08-24 | 中国船舶重工集团公司第七一〇研究所 | A kind of course fault-tolerant control system towards drive lacking Autonomous Underwater Vehicle |
| CN107255923B (en) * | 2017-06-14 | 2020-04-03 | 哈尔滨工程大学 | RBF identification-based under-actuated unmanned ship track tracking control method of ICA-CMAC neural network |
| GR20180100033A (en) * | 2018-01-25 | 2019-09-06 | Δημητριος Νικολαος Βοσκακης | Artificial-intelligence craft's navigation system |
| CN108303982B (en) * | 2018-01-31 | 2021-11-30 | 深圳力子机器人有限公司 | Automatic guide transport vehicle, and control method and control system thereof |
| CN108445877A (en) * | 2018-02-06 | 2018-08-24 | 武汉理工大学 | Based on the bionic movement control system and control method under mobile 3G/4G network environments |
| CN108459503B (en) * | 2018-02-28 | 2021-03-30 | 哈尔滨工程大学 | Unmanned surface vehicle track planning method based on quantum ant colony algorithm |
| CN109164701A (en) * | 2018-07-17 | 2019-01-08 | 袁乐铮 | A kind of course heading control method and system based on fuzzy control |
| CN109283842B (en) * | 2018-08-02 | 2022-01-07 | 哈尔滨工程大学 | Unmanned ship track tracking intelligent learning control method |
| CN109808853A (en) * | 2019-03-05 | 2019-05-28 | 上海大学 | A kind of unmanned boat bow is to speed of a ship or plane multi-level control system and its control method |
| CN110398963A (en) * | 2019-07-13 | 2019-11-01 | 珠海格力智能装备有限公司 | Trackless navigation correction control method, device, storage medium and controller |
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| CN100568144C (en) * | 2008-09-04 | 2009-12-09 | 湖南大学 | Mobile robot's multirow is for merging automatic navigation method under a kind of circumstances not known |
| CN103309244B (en) * | 2013-05-29 | 2016-05-04 | 哈尔滨工程大学 | One owes to drive unmanned boat semi-physical system and dedicated emulated method thereof |
| CN103760902B (en) * | 2014-01-14 | 2016-06-08 | 上海海事大学 | A kind of unmanned water surface ship straight line path tracking based on fuzzy control |
| CN103777522B (en) * | 2014-01-21 | 2016-09-28 | 上海海事大学 | Unmanned water surface ship line tracking method based on fuzzy |
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2015
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