CN108845576A - A kind of thrust distribution method based on population in conjunction with sequential quadratic programming - Google Patents
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/0206—Control of position or course in two dimensions specially adapted to water vehicles
- G05D1/0208—Control of position or course in two dimensions specially adapted to water vehicles dynamic anchoring
Abstract
The present invention relates to a kind of thrust distribution method based on population in conjunction with sequential quadratic programming, technical characterstic is：Include the following steps：Step 1, thrust allocation unit obtain the instruction of three axis of current controller, including longitudinal force, cross force and yawing torque from controller；Step 2 establishes dynamic positioning system thrust distribution model；Step 3, the objective function and constraint condition for establishing thrust assignment problem；Step 4, the optimization method combined using population with sequential quadratic programming solve thrust assignment problem.The present invention is on the basis of fully considering particle swarm algorithm and sequential quadratic programming algorithm advantage and disadvantage, the two is combined, enable and is effectively made up the shortcomings that the two, substantially increase the global optimizing performance of algorithm, dynamic positioning system thrust assignment problem can be fast and effeciently solved, there is very high practical value.
Description
Technical field
The invention belongs to dynamic positioning of vessels technical fields, are related to the thrust distribution method of Ship Dynamic Positioning Systems Based, especially
It is a kind of thrust distribution method based on population in conjunction with sequential quadratic programming.
Background technique
As the mankind continually develop ocean, traditional anchoring system can no longer meet abyssalpelagic operation
Demand.And the propeller that dynamic positioning system can be equipped with using ship itself, the interference of maritime environment is resisted, realizes ship position
Set with bow to holding, and it has many advantages, such as positioning accuracy height, mobility strong, not by sea area depth limit, is deep ocean work
One of the Support Equipment of equipment indispensability.
The task of Ship Dynamic Positioning Systems Based thrust distribution is to refer to the Three Degree Of Freedom control that Dynamic Positioning Control System device exports
It enables according to certain allocation strategy, reasonably distributes to each propeller, it is made to export desired resultant force and torque.At this stage
Ship can generally be equipped with more propellers, therefore can have countless multiple groups solutions under the premise of meeting control instruction.Considering to push away
In the case where the factors such as device energy consumption, propeller abrasion, thrust error, thrust assignment problem can be attributed to one it is non-thread
Property optimization problem.
It is found through retrieval, Publication No. CN102508431A, entitled " a kind of power positioning system of offshore drilling platform pushes away
In the patent application of force distribution method ", thrust distribution is realized with particle swarm algorithm.Publication No. CN106773741A, title
For in the patent application of " a kind of unmanned boat dynamic positioning system and method ", with the particle swarm algorithm for improving inertial factor is added
It realizes thrust distribution, although being improved algorithm, but still is particle swarm algorithm.Single particle swarm algorithm is in searching process
In, randomness is especially big, and optimizing result each time can fluctuate near optimal value, therefore is pushed away using what particle swarm algorithm generated
In power allocation result, each propeller thrust and azimuthal variation frequency are especially high, even identical control instruction, produces each time
Raw propeller instruction is not also identical, this be to the control of propeller it is very unfavorable, the frequent movement of propeller can aggravate to promote
Device abrasion, shortens the service life of propeller.
Publication No. CN103092077A in the patent of entitled " thrust distribution method of dynamic positioning system ", is utilized
Sequential quadratic programming algorithm come realize thrust distribute.But sequential quadratic programming algorithm is especially big to the dependence of initial value, initial value
It chooses and improper is likely to result in the phenomenon that can not find optimal solution.
Summary of the invention
It is an object of the invention to overcome deficiency in the prior art, it is reasonable, fast and effective and global to provide a kind of design
The strong thrust distribution method based on population in conjunction with sequential quadratic programming of optimizing performance.
A kind of thrust distribution method based on population in conjunction with sequential quadratic programming, includes the following steps：
Step 1, thrust allocation unit from controller obtain three axis of current controller instruction, including longitudinal force, cross force and
Yawing torque；
Step 2 establishes dynamic positioning system thrust distribution model；
Step 3, the objective function and constraint condition for establishing thrust assignment problem；
Step 4, the optimization method combined using population with sequential quadratic programming solve thrust assignment problem；
Moreover, the mathematical description of the dynamic positioning system thrust distribution model of the step 2 is：
τ=(X, Y, N)T=B (α) T
Wherein, T=(T1,T2…Tn)T, indicate the thrust size of n propeller；
α=(α1,α2…αn), indicate the azimuth of n propeller；
In above formula, (lxi,lyi) indicate position of i-th of propeller relative to ship rotation center.
Moreover, the objective function of the thrust assignment problem of the step 3 includes：It executes energy consumption, control system and pushes away
It is worn into system thrust error and propeller；Its mathematical description is：
J (α, T) represents the objective function of thrust assignment problem, and above-mentioned expression formula is meant that, by choose different α and
T, so that target function value is minimum.Wherein α and T respectively represents azimuth and the thrust size of propeller.In above formula,
For all executing agency's energy consumptions, W is weight matrix；STQS represents Three Degree Of Freedom thrust error, and Q is that a positive definite is diagonal
Battle array, S=τ-B (α) T is a slack variable；(α-α0)TΩ(α-α0) it is that propeller is worn, Ω is also a positive definite matrix, α0
For the azimuth of last moment each propeller；
Moreover, the bound for objective function of the thrust assignment problem of the step 3 is：
In above formula, T and α represent propeller thrust and the azimuth at current time, T0And α0Represent the propeller of last moment
Thrust and azimuth, Δ Tmin,ΔTmax,Δαmin,ΔαmaxRespectively represent propeller thrust and azimuthal minimum and maximum change
Change range；
Moreover, the specific steps of the step 4 include：
(1) particle swarm algorithm parameter is initialized：Population scale N, the dimension D of solution, maximum number of iterations T are set；
(2) speed of random initializtion population and position：Each propeller thrust of the speed representation of each particle and azimuth
Pace of change；The position vector of each particle represents the vector being made of the thrust and azimuth of each propeller；
(3) its target function value is gone out according to the positional information calculation of each particle, and to the optimal letter of the history of each particle
Numerical value and group's optimal function value are updated；
(4) according to the speed of particle and location update formula, the state of particle is updated, the speed of particle and position
More new formula is：
In above formula, i=1,2 ..., m indicate the quantity of population, d=1,2 ..., D, and D indicates the dimension of problem to be solved, often
The state of a particle includes location information xi(t)=[xi1(t),xi2(t),…xiD(t)] with velocity vector information vi(t)=
[vi1(t),vi2(t),…viD(t)]；Each particle is able to record the history optimal location p itself once foundi(t)=[pi1
(t),pi2(t),…piD(t)], meanwhile, the location information p of global optimum's particle in group can be shared between particleg(t)=
[pg1(t),pg2(t),…pgD(t)]；Aceleration pulse c1With c2For nonnegative constant, r1With r2For from the random number on [0,1]；And work asWhen,WhenWhen, WithRepresent the minimum and maximum speed of particle flight；
(5) fitness of more each particle：The fitness i.e. target function value of each particle is solved, finding in population has
The particle of highest fitness, and each fitness is compared with the value of last moment, the highest for obtaining its own is suitable
Response；
(6) step (3)-step (5) are repeated, until reaching maximum number of iterations, acquisition particle swarm algorithm searches out optimal
Allocation result.
(7) the optimum allocation result for obtaining particle swarm algorithm is as initial value, reapply sequential quadratic programming method into
Row solves.
(8) output sequence Novel Algorithm searches out optimum allocation as a result, the thrust output of i.e. each propeller and
Direction.
The advantages of the present invention：
1, the invention discloses a kind of dynamic positioning system thrusts combined based on population with sequential quadratic programming point
Method of completing the square, its main feature is that being surging, swaying and yawing control instruction respectively meeting dynamic positioning system horizontal plane Three Degree Of Freedom
On the basis of, realize the target that all executing agency's energy consumptions are minimum, abrasion is minimum.Include the following steps：Provide first longitudinal force,
Cross force and yawing torque Three Degree Of Freedom control instruction；Next establishes thrust distribution nonlinear mathematical model：With all execution
Mechanism energy consumption, abrasion, thrust error be the smallest objective function and consider thrust, azimuth rate and thrust forbidden zone etc. because
The constraint condition of element.Finally the method using population in conjunction with sequential quadratic programming solves thrust assignment problem, obtains each
Executing agency, azimuth instruction.The present invention is in the base for fully considering particle swarm algorithm Yu sequential quadratic programming algorithm advantage and disadvantage
On plinth, the two is combined, enables and is effectively made up the shortcomings that the two, substantially increases the global optimizing of algorithm
Can, dynamic positioning system thrust assignment problem can be fast and effeciently solved, there is very high practical value.
2, the present invention is directed to the defect of single particle group algorithm and unique sequence Novel Algorithm, melts to the two
It closes, overcomes the disadvantage that output results change frequency is excessively high as caused by the randomness of single particle group's algorithm；While and with
Initial value of the output result of particle swarm algorithm as sequential quadratic programming algorithm, overcomes unique sequence Novel Algorithm pair
The big disadvantage of initial value degree of dependence.Blending algorithm low optimization accuracy is high, and engineering practicability is strong.
3, the effect of particle swarm algorithm of the present invention mainly provides an initial value, therefore does not need extra high essence
Degree is not needed to take a substantial amount of time and is iterated therefore under the premise of meeting low optimization accuracy, and calculating can be greatly reduced
Time meets the requirement of dynamic positioning system real-time.
4, the blending algorithm of the invention based on population in conjunction with sequential quadratic programming to the form of objective function and
The setting requirements of initial value are very low, and algorithm parameter is simple, are conveniently adjusted, and algorithm low optimization accuracy is high, can fast and effeciently solve
Certainly thrust assignment problem.
Detailed description of the invention
Fig. 1 is process flow diagram of the invention；
Fig. 2 is dynamic positioning system structural block diagram of the invention；
Fig. 3 is propeller layout drawing.
Specific embodiment
The embodiment of the present invention is described in further detail below in conjunction with attached drawing：
A kind of thrust distribution method based on population in conjunction with sequential quadratic programming, as shown in Figure 1, including following step
Suddenly：
Step 1, thrust allocation unit obtain three axis of current controller instruction τ from controller, specifically include longitudinal force X, cross
To power Y and yawing torque N, i.e. τ=(X, Y, N)T；
Step 2 establishes dynamic positioning system thrust distribution model, and mathematical description is：
τ=(X, Y, N)T=B (α) T
Wherein, T=(T1,T2…Tn)T, indicate the thrust size of n propeller；
α=(α1,α2…αn), indicate the azimuth of n propeller；
(lxi,lyi) indicate position of i-th of propeller relative to ship rotation center；
Step 3, the objective function and constraint condition for establishing thrust assignment problem；
The objective function of the thrust assignment problem of the step 3 includes：Execute energy consumption, control system and propulsion system
Thrust error and propeller abrasion；Its mathematical description is：
J (α, T) represents the objective function of thrust assignment problem, and above-mentioned expression formula is meant that, by choose different α and
T, so that target function value is minimum.Wherein α and T respectively represents azimuth and the thrust size of propeller.In above formula,
For all executing agency's energy consumptions, W is weight matrix；STQS represents Three Degree Of Freedom thrust error, and Q is that a positive definite is diagonal
Battle array, S=τ-B (α) T is a slack variable；(α-α0)TΩ(α-α0) it is that propeller is worn, Ω is also a positive definite matrix, α0
For the azimuth of last moment each propeller；
In the present embodiment, specific value is：
W=[60606060], Q=diag [1500080000100000], Ω=diag [400000400000].
The bound for objective function of the thrust assignment problem of the step 3 is：
Thrust assignment problem constraint condition is in addition to meeting equilibrium equation, it is also necessary to consider that propeller actual physics limit,
Including thrust size and variation limitation, azimuth size and variation limitation etc., mathematical description is：
In above formula, T and α represent propeller thrust and the azimuth at current time, T0And α0Represent the propeller of last moment
Thrust and azimuth, Δ Tmin,ΔTmax,Δαmin,ΔαmaxRespectively represent propeller thrust and azimuthal minimum and maximum change
Change range；
In the present embodiment, T1max=T2max=300KN, T1min=T2min=0KN；T3max=T3max=420KN, T3min=
T3min=-285KN；α1max=α2max=360 °, α1min=α2min=0 °, α3=α4=90 °；ΔT1max=Δ T2max=18KN, Δ
T1min=Δ T2min=0KN, Δ T3max=Δ T4max=25KN, Δ T3min=Δ T4min=0KN；Δα1max=Δ α2max=10 °,
Δα1min=Δ α2min=0 °.
Step 4, the optimization method combined using population with sequential quadratic programming solve thrust assignment problem；
The specific steps of the step 4 include：
(1) particle swarm algorithm parameter is initialized：Population scale (number of candidate solution) N, the dimension D of solution, maximum is arranged to change
Generation number T；
In the present embodiment, present invention does not require particle swarm algorithms to obtain point-device solution, need to only obtain optimal solution
Approximate range, thus population invariable number and the number of iterations can accordingly be arranged it is smaller, to increase algorithm calculating speed.Therefore originally
Population invariable number N=10, the dimension D=6 of solution, maximum number of iterations T=50 in invention.
(2) speed of random initializtion population and position：Each propeller thrust of the speed representation of each particle and azimuth
Pace of change；The position vector of each particle represents the vector being made of the thrust and azimuth of each propeller；
(3) its target function value is gone out according to the positional information calculation of each particle, and to the optimal letter of the history of each particle
Numerical value and group's optimal function value are updated；
(4) according to the speed of particle and location update formula, the state of particle is updated, the speed of particle and position
More new formula is：
In above formula, i=1,2 ..., m indicate the quantity of population, d=1,2 ..., D, and D indicates the dimension of problem to be solved, often
The state of a particle includes location information xi(t)=[xi1(t),xi2(t),…xiD(t)] with velocity vector information vi(t)=
[vi1(t),vi2(t),…viD(t)]；Each particle is able to record the history optimal location p itself once foundi(t)=[pi1
(t),pi2(t),…piD(t)], meanwhile, the location information p of global optimum's particle in group can be shared between particleg(t)=
[pg1(t),pg2(t),…pgD(t)]；Aceleration pulse c1With c2For nonnegative constant, r1With r2For from the random number on [0,1]；And work asWhen,WhenWhen, WithRepresent the minimum and maximum speed of particle flight；
In the present embodiment, aceleration pulse c is taken1=0.4 and c2=0.6 is nonnegative constant；
(5) fitness of more each particle：The fitness i.e. target function value of each particle is solved, finding in population has
The particle of highest fitness, and each fitness is compared with the value of last moment, the highest for obtaining its own is suitable
Response；
(6) step (3)-step (5) are repeated, until reaching maximum number of iterations, acquisition particle swarm algorithm searches out optimal
Allocation result.
(7) the optimum allocation result for obtaining particle swarm algorithm is as initial value, reapply sequential quadratic programming method into
Row solves.
(8) output sequence Novel Algorithm searches out optimum allocation as a result, the thrust output of i.e. each propeller and
Direction.
Dynamic positioning system of the invention is as shown in Fig. 2, mainly include measuring system, impeller system, control system etc..
(1) measuring system by the ship status data measured (including position, bow to and posture information) first carry out signal
Processing, including the processing such as elimination of burst noise, time and spacial alignment, to improve the accuracy of signal.Signal after again will be processed
State observer is passed to, generally treated metrical information is filtered by the way of Extended Kalman filter, with
The noise in measuring signal can be effective filtered out by Extended Kalman filter technology for feedback control, reduce propeller mill
Damage and energy consumption.
(2) control system mainly calculates holding or changes vessel position/bow to required ship horizontal plane Three Degree Of Freedom
Control force (square) mainly includes deviation feedback and wind feed forward；Wherein, 1. deviation feedback is to utilize the ship exported from measuring system
Position, bow are calculated and are kept or change ship position to the states such as, speed, revolution angular speed and position/bow to the deviation of setting value
Set/bow is to required deviation feedback force (square).2. wind feed forward is the wind speed obtained using wind sensor measurement, wind direction measurement
Value calculates wind load according to ship wind-power model.Wind load feedforward control power (square) and wind load are equal in magnitude, direction phase
Instead.
What control system finally exported be ship keep or change position/bow to required horizontal plane Three Degree Of Freedom i.e.
The control force of (longitudinally, laterally, bow to), passes to thrust allocation unit for the control force, is distributed by thrust, and three axis are controlled
Power is converted into the control instruction of each executing agency, and then by the movement of propeller, reach holding or change vessel position/bow to
Purpose.
As shown in Figure 3, it can be seen that the propeller of controlled device according to the present invention configures, wherein 1# and 2# is promoted
Device is all-direction propeller；3#, 4# propeller are conduit propeller.Wherein position (the l of each propellerxi,lyi) be respectively：1#
Propeller (- 34.5, -12.0)；2# propeller (- 34.5,12.0)；3# propeller (22.0,0)；4# propeller (28.4,0).
The working principle of the invention is：
By ship status observer obtain ship physical location and bow to and with setting position and bow to being compared
Compared with, the deviation of the two is passed into controller, by controller output needed for Three Degree Of Freedom control instruction, distributed using thrust
The control instruction of each executing agency is obtained, the effect of external environment is offset with this.The invention mainly relates to thrust distribution to optimize
How the control instruction of controller output is become the control instruction of each executing agency by method.
It is emphasized that embodiment of the present invention be it is illustrative, without being restrictive, therefore the present invention includes
It is not limited to embodiment described in specific embodiment, it is all to be obtained according to the technique and scheme of the present invention by those skilled in the art
Other embodiments, also belong to the scope of protection of the invention.
Claims (5)
1. a kind of thrust distribution method based on population in conjunction with sequential quadratic programming, it is characterised in that：Include the following steps：
Step 1, thrust allocation unit obtain the instruction of three axis of current controller, including longitudinal force, cross force and yawing from controller
Torque；
Step 2 establishes dynamic positioning system thrust distribution model；
Step 3, the objective function and constraint condition for establishing thrust assignment problem；
Step 4, the optimization method combined using population with sequential quadratic programming solve thrust assignment problem.
2. a kind of thrust distribution method based on population in conjunction with sequential quadratic programming according to claim 1, special
Sign is：The mathematical description of the dynamic positioning system thrust distribution model of the step 2 is：τ=(X, Y, N)T=B (α) T
Wherein, T=(T1,T2…Tn)T, indicate the thrust size of n propeller；
α=(α1,α2…αn), indicate the azimuth of n propeller；
In above formula, (lxi,lyi) indicate position of i-th of propeller relative to ship rotation center.
3. a kind of thrust distribution method based on population in conjunction with sequential quadratic programming according to claim 1, special
Sign is：The objective function of the thrust assignment problem of the step 3 includes：Execute energy consumption, control system and propulsion system
Thrust error and propeller abrasion；Its mathematical description is：
J (α, T) represents the objective function of thrust assignment problem, and above-mentioned expression formula is meant that, by choosing different α and T, makes
It is minimum to obtain target function value；Wherein α and T respectively represents azimuth and the thrust size of propeller；In above formula,For institute
There is executing agency's energy consumption, W is weight matrix；STQS represents Three Degree Of Freedom thrust error, and Q is a positive definite diagonal matrix, S=
τ-B (α) T is a slack variable；(α-α0)TΩ(α-α0) it is that propeller is worn, Ω is also a positive definite matrix, α0It is upper one
The azimuth of moment each propeller.
4. a kind of thrust distribution method based on population in conjunction with sequential quadratic programming according to claim 1, special
Sign is：The bound for objective function of the thrust assignment problem of the step 3 is：
In above formula, T and α represent propeller thrust and the azimuth at current time, T0And α0Represent the propeller thrust of last moment
With azimuth, Δ Tmin,ΔTmax,Δαmin,ΔαmaxRespectively represent propeller thrust and azimuthal minimum and maximum variation model
It encloses.
5. a kind of thrust distribution method based on population in conjunction with sequential quadratic programming according to claim 1, special
Sign is：The specific steps of the step 4 include：
(1) particle swarm algorithm parameter is initialized：Population scale N, the dimension D of solution, maximum number of iterations T are set；
(2) speed of random initializtion population and position：Each propeller thrust of the speed representation of each particle and azimuthal change
Change speed；The position vector of each particle represents the vector being made of the thrust and azimuth of each propeller；
(3) its target function value is gone out according to the positional information calculation of each particle, and to the history optimal function value of each particle
And group's optimal function value is updated；
(4) according to the speed of particle and location update formula, the state of particle is updated, the speed and location updating of particle
Formula is：
In above formula, i=1,2 ..., m indicate the quantity of population, d=1,2 ..., D, and D indicates the dimension of problem to be solved, each grain
The state of son includes location information xi(t)=[xi1(t),xi2(t),…xiD(t)] with velocity vector information vi(t)=[vi1
(t),vi2(t),…viD(t)]；Each particle is able to record the history optimal location p itself once foundi(t)=[pi1(t),
pi2(t),…piD(t)], meanwhile, the location information p of global optimum's particle in group can be shared between particleg(t)=[pg1
(t),pg2(t),…pgD(t)]；Aceleration pulse c1With c2For nonnegative constant, r1With r2For from the random number on [0,1]；And work asWhen,WhenWhen,
WithRepresent the minimum and maximum speed of particle flight；
(5) fitness of more each particle：The fitness i.e. target function value for solving each particle, finding has highest in population
The particle of fitness, and each fitness is compared with the value of last moment, obtain the highest fitness of its own；
(6) step (3)-step (5) are repeated, until reaching maximum number of iterations, obtains particle swarm algorithm and search out optimum allocation
As a result；
(7) the optimum allocation result for obtaining particle swarm algorithm reapplies sequential quadratic programming method and is asked as initial value
Solution；
(8) output sequence Novel Algorithm searches out optimum allocation as a result, i.e. each propeller thrust output and direction.
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| CN109799705B (en) * | 2019-01-01 | 2022-01-14 | 武汉船用电力推进装置研究所（中国船舶重工集团公司第七一二研究所） | Thrust distribution method for reducing wear of dynamic positioning ship propeller |
| CN109799826A (en) * | 2019-02-20 | 2019-05-24 | 上海振华重工(集团)股份有限公司 | The thrust distribution method of marine propulsion systems |
| CN109765910A (en) * | 2019-03-08 | 2019-05-17 | 哈尔滨工程大学 | A kind of dynamic positioning of vessels thrust distribution method based on improved differential evolution algorithm |
| CN110456658A (en) * | 2019-07-24 | 2019-11-15 | 哈尔滨工程大学 | A kind of mutarotation of dynamic positioning ship turns center movement control emulation mode |
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| CN111061285A (en) * | 2019-12-12 | 2020-04-24 | 哈尔滨工程大学 | Ship dynamic positioning thrust distribution method |
| CN111061285B (en) * | 2019-12-12 | 2022-08-02 | 哈尔滨工程大学 | Ship dynamic positioning thrust distribution method |
| CN111483568A (en) * | 2020-04-29 | 2020-08-04 | 上海交通大学 | Dynamic positioning model test control method and system based on wind current feedforward |
| CN111483569A (en) * | 2020-04-29 | 2020-08-04 | 上海交通大学 | Floating-support installation model test control method and system based on environmental force feedforward |
| CN112083651A (en) * | 2020-08-04 | 2020-12-15 | 上海交通大学 | Dynamic thrust distribution method for double-pump water jet propulsion unmanned ship |
| CN112327619B (en) * | 2020-10-22 | 2022-12-09 | 智慧航海（青岛）科技有限公司 | Thrust distribution optimization method based on multi-algorithm combination |
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| CN112394641A (en) * | 2020-11-25 | 2021-02-23 | 哈尔滨工程大学 | Ship real-time thrust distribution method based on double-thread parallel computation |
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| CN115826606A (en) * | 2023-02-14 | 2023-03-21 | 中国船舶集团有限公司第七〇七研究所 | Dynamic positioning control method of self-elevating ship platform |
| CN115826606B (en) * | 2023-02-14 | 2023-04-21 | 中国船舶集团有限公司第七〇七研究所 | Dynamic positioning control method for self-elevating ship platform |
| CN115994456A (en) * | 2023-03-21 | 2023-04-21 | 中国船舶集团有限公司第七〇七研究所 | Full-rotation propeller thrust attenuation processing method for ship thrust distribution |
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