CN102385665A - Thrust force distribution method of power location system of ship adopting genetic algorithm - Google Patents

Abstract

The invention belongs to the technical field of automatic control and relates to a thrust force distribution method of a power location system of a ship adopting a genetic algorithm, which comprises a step 31, converting the problem of thrust force distribution into a problem of optimization; and a step 32, resolving the problem of the thrust force optimization by means of the genetic algorithm, wherein the target function of the problem of the optimization serves as a fitness function in the genetic algorithm and a restricting condition of the problem of the optimization serves as a resolution range in the genetic algorithm. The thrust force distribution method resolves the problem of the thrust force distribution by means of the genetic algorithm so as to effectively resolve the problem that the target function and the restricting condition contain non-linear items and accordingly greatly enlarges the range of resolvable problems. The method can effectively distribute the trust force in a longitudinal direction, a transverse direction and a yawing direction onto thrusters and can effectively avoid strange structures. The thrust force distribution method can be widely applied to the thrust force distribution of various numbers of thrusters of various ships, and generate maximum thrust force with minimum energy, thereby greatly saving energy.

Description

Adopt the dynamic positioning of vessels system thrust distribution method of genetic algorithm

Technical field

The present invention relates to a kind of dynamic positioning of vessels system thrust distribution method, particularly relate to a kind of distribution method that adopts the dynamic positioning of vessels system thrust of genetic algorithm.

Background technology

Genetic algorithm (Genetic Algorithm) is that one type of evolution rule (survival of the fittest, survival of the fittest genetic mechanism) of using for reference organic sphere develops and next randomization searching method.It is that its principal feature is directly structure objects to be operated, and does not have the successional qualification of differentiate and function by the J.Holland professor at first proposition in 1975 of the U.S.; Have inherent latent concurrency and better global optimizing ability; Adopt the optimization method of randomization, can obtain and instruct the search volume of optimization automatically, adjust the direction of search adaptively, the rule that need not confirm.These character of genetic algorithm have been widely used in fields such as Combinatorial Optimization, machine learning, signal Processing, adaptive control and artificial life by people.It is the gordian technique in the modern relevant intelligence computation.

Get into the nineties, genetic algorithm has welcome prosperous developing period, is that theoretical research or applied research have all become very popular topic.Especially the applied research of genetic algorithm seems especially active, and not only its application enlarges, and the ability of utilizing genetic algorithm to be optimized with rule learning also significantly improves, and the research of application in industry aspect is also among groping simultaneously.Some new theories and method have also obtained development rapidly in applied research in addition, and these have all increased new vitality to genetic algorithm undoubtedly.The applied research of genetic algorithm has been found the solution from the Combinatorial Optimization at initial stage and has been expanded to many renewals, the application facet of through engineering approaches more.

The dynamic positioning of vessels system is a kind of control system of closed loop; Utilize measuring system to detect the horizontal drift amount and the azimuth deviation amount of boats and ships under the disturbing force effect of extraneous wind, wave, stream, after signal Processing, import robot calculator, carry out analytical calculation with special software after; Each thruster thrust output by thruster is installed on ship is instructed; Make it to send corresponding thrust, reach balance, ship is returned on (or remaining on) original position and orientation of setting with environmental perturbation power.

Prior art does not have the effective method answer and how thrust is assigned on each thruster, and the energy that thruster is consumed is bigger, the easy excessive wear of thruster.Solve the problems referred to above so be badly in need of a kind of dynamic positioning of vessels system thrust distribution method.

Summary of the invention

The present invention is exactly in order to address the above problem, and overcomes to distribute the existing defective of angle of rake dynamical problem on the boats and ships in the prior art, and the present invention provides the dynamic positioning of vessels system thrust distribution method that adopts genetic algorithm to address the aforementioned drawbacks and problem.

To achieve these goals, technical scheme of the present invention is following:

Adopt the dynamic positioning of vessels system thrust distribution method of genetic algorithm, it is characterized in that it comprises the steps:

Step 31: the thrust assignment problem is converted into optimization problem;

$\min \{J=\underset{i=1}{\overset{r}{\mathrm{\Σ}}}{P}_i{\left|f\right|}^{3/2}+s^T\mathrm{Qs}+{(\mathrm{\α}-{\mathrm{\α}}_0)}^T\mathrm{\Ω}(\mathrm{\α}-{\mathrm{\α}}_0)+\frac{\mathrm{\ρ}}{\mathrm{\ϵ}+\det \left(B\left(\mathrm{\α}\right)W^{-1}{B}^T\left(\mathrm{\α}\right)\right)}\}$

s.t.B(α)f＝τ+s

f _min≤f≤f _max

Δf _min≤f-f ₀≤Δf _max

α _min≤α≤α _max

Δα _min?≤α-α ₀≤Δα _max

Wherein, f=[f ₁f ₂f ₃f ₄f ₅], f _Min=[3.2;-2.56;-2.56;-1.8;-21],

f _max＝[3.2；4.18；4.18；1.8；30]，Δf _min＝[-0.6；-0.6；-0.6；-0.6；-0.6]，Δf _max＝[0.6；0.6；0.6；0.6；0.6]，

α＝[α ₁；α ₂；α ₃；α ₄；α ₅]，α _min＝[-∞；-∞；-∞；-∞；-∞]，α _max＝[∞；∞；∞；∞；∞]，

Δα _min＝[-1.2；-1.2；-1.2；-1.2；-1.2]，

Δα _max＝[1.2；1.2；1.2；1.2；1.2]， $B\left(\mathrm{\α}\right)=\left[\begin{array}{ccccc}0& \cos {\mathrm{\α}}_2& \cos {\mathrm{\α}}_3& 0& 1\\ 1& \sin {\mathrm{\α}}_2& \sin {\mathrm{\α}}_3& 1& 0\\ 2.46& 2.36\sin {\mathrm{\α}}_2& -1.82\sin {\mathrm{\α}}_3& -2.16& 0\end{array}\right],$

τ is the thrust instruction that controller sends, and s is a thrust error, P _i, ρ, ε be coefficient, Q, Ω, W are matrix of coefficients;

Step 32: with the optimization problem of genetic algorithm for solving thrust;

The objective function of optimization problem is the fitness function in the genetic algorithm, and the constraint condition of optimization problem is the solution space in the genetic algorithm.

A kind of dynamic positioning of vessels system thrust distribution method that adopts genetic algorithm as above, wherein, this step 32 uses the concrete grammar of the optimization problem of genetic algorithm for solving thrust to be:

(1) establishes

and be the thrust instruction of controller output; The thrust instruction comprises the power of needed surge direction, the power and the yawing moment of swaying direction; The thrust that

sends for each propeller; Then need satisfy τ=B(α) f; Wherein

$B\left(\mathrm{\α}\right)=\left(\begin{array}{cccc}\cos {\mathrm{\α}}_1& \cos {\mathrm{\α}}_2& L& \cos {\mathrm{\α}}_n\\ \sin {\mathrm{\α}}_1& \sin {\mathrm{\α}}_2& L& \sin {\mathrm{\α}}_n\\ -y_1\cos {\mathrm{\α}}_1+x_1\sin {\mathrm{\α}}_1& -y_2\cos {\mathrm{\α}}_2+x_2\sin {\mathrm{\α}}_2& L& -y_n\cos {\mathrm{\α}}_n+x_n\sin {\mathrm{\α}}_n\end{array}\right)$

α _iBe i angle of rake position angle, (x _i, y _i) be i angle of rake coordinate, n is a thruster quantity;

(2) find the solution following formula according to formula in the step (1):

$\min \{J=\underset{i=1}{\overset{r}{\mathrm{\Σ}}}{P}_i{\left|f\right|}^{3/2}+s^T\mathrm{Qs}+{(\mathrm{\α}-{\mathrm{\α}}_0)}^T\mathrm{\Ω}(\mathrm{\α}-{\mathrm{\α}}_0)+\frac{\mathrm{\ρ}}{\mathrm{\ϵ}+\det \left(B\left(\mathrm{\α}\right)W^{-1}{B}^T\left(\mathrm{\α}\right)\right)}\}$

s.t.B(α)f＝τ+s

f _min≤f≤f _max

Δf _min≤f-f ₀≤Δf _max

α _min≤α≤α _max

Δα _min≤α-α ₀≤Δα _max

Wherein, first energy consumption that representative is total, the error of making a concerted effort of the instruction of second punishment thrust and each propeller thrust, the 3rd the azimuthal variation of punishment thruster, α ₀Be the angle of rake position angle of previous moment, the 4th is used to avoid singular structure; Constraint condition comprises the restriction to the thruster maximum thrust, the restriction of the maximum pace of change of thrust, azimuthal scope, the restriction of the maximal rate of azimuthal variation.

A kind of dynamic positioning of vessels system thrust distribution method that adopts genetic algorithm as above, wherein, fitness function is the objective function in this step (1); Solution space is the constraint condition in this step (1), and with the genotype string structure data that data are expressed as hereditary space of separating of solution space, the various combination allosteric of these string structure data has become different points; Initial solution adopts the method for completely random to produce, and this concrete calculation procedure of optimization problem of finding the solution thrust is following:

Step 41: generate initial population with coding form;

Step 42: use fitness function to carry out the fitness assessment of each individuals in the initial population;

Step 43: will select operator to act on colony;

Step 44: crossover operator is acted on colony;

Step 45: mutation operator is acted on colony;

Step 46: carry out the judgement of end condition;

Obtain the size and its azimuth angle alpha of each propeller thrust f.

The invention has the beneficial effects as follows: adopt genetic algorithm for solving thrust assignment problem, efficiently solve the problem that the objective function constraints contains nonlinear terms, greatly widened the scope of the problem that can find the solution.The inventive method can be effectively with the thrust command assignment of surging, swaying and three directions of yawing to each thruster, each propeller thrust changes steadily, and the azimuthal position of thruster is comparatively reasonable, can effectively avoid the generation of singular structure.

Description of drawings

Specify the present invention below in conjunction with accompanying drawing and embodiment:

Fig. 1 arranges synoptic diagram for marine propeller.

Fig. 2 is an overall framework synoptic diagram of the present invention.

Fig. 3 is a method synoptic diagram of the present invention.

The concrete grammar that Fig. 4 adopts genetic algorithm optimization dynamic positioning of vessels thrust to distribute for the present invention.

Embodiment

For technological means, creation characteristic that the present invention is realized, reach purpose and effect and be easy to understand and understand, below in conjunction with concrete diagram, further set forth the present invention.Algorithm of the present invention is divided into two parts, and first is for to be converted into an optimization problem with the thrust assignment problem, and second part is for adopting this optimization problem of genetic algorithm for solving.

Fig. 1 is that the thruster on the boats and ships 100 is arranged synoptic diagram.This synoptic diagram is a model with certain shuttler, is calculating object with this model, and the inventive method is described.Boats and ships 100 are provided with T1-T5 totally 5 thrusters, and T1 is the bow thruster, and T2 is the bow full circle swinging, and T3 is the stern full circle swinging, and T4 is the stern thruster, and T5 is for promoting mainly.Full rotating thrusters T2 and T3, the maximum rate of change in azimuth

Thrust maximum change rate

Each thruster parameter is as shown in the table.

Thruster	Position (m)	Maximum thrust (N)	Minimum thrust (N)
				Bow thruster T1	(2.46，0)	?3.2	?-3.2
Bow full circle swinging T2	(2.36，0)	?4.18	?-2.56
				Stern full circle swinging T3	(-1.82，0)	?4.18	?-2.56
Stern thruster T4	(-2.16，0)	?1.8	?-1.8
				Promote mainly T5	(-2.42，0)	?30	?-21

Because the angle of rake number of boats and ships is generally more than 3, each thruster all can produce the thrust of different sizes and different directions, so exist the countless a plurality of different thrusts and the combination of direction, satisfies specific horizontal force and yawing moment.

Fig. 2 has represented overall framework of the present invention, and the present invention comprises computing machine 21, and install software is carried out the inventive method on it, and angle of rake thrust assignment problem is converted into the size and Orientation that optimization problem obtains best thrust; Controller 22 is connected with each thruster with computing machine 21, and controller 22 is exported this thrust according to each thruster of thrust size and Orientation control of the best that this computed in software goes out.

Fig. 3 is a method synoptic diagram of the present invention.At first, execution in step 31: the thrust assignment problem is converted into optimization problem, and the objective function constraints is as follows:

$\min \{J=\underset{i=1}{\overset{r}{\mathrm{\Σ}}}{P}_i{\left|f\right|}^{3/2}+s^T\mathrm{Qs}+{(\mathrm{\α}-{\mathrm{\α}}_0)}^T\mathrm{\Ω}(\mathrm{\α}-{\mathrm{\α}}_0)+\frac{\mathrm{\ρ}}{\mathrm{\ϵ}+\det \left(B\left(\mathrm{\α}\right)W^{-1}{B}^T\left(\mathrm{\α}\right)\right)}\}$

s.t.B(α)f＝τ+s

f _min≤f≤f _max

Δf _min≤f-f ₀≤Δf _max

α _min≤α≤α _max

Δα _min≤α-α ₀≤Δα _max

Wherein, f=[f ₁f ₂f ₃f ₄f ₅], f _Min=[3.2;-2.56;-2.56;-1.8;-21],

f _max＝[3.2；4.18；4.18；1.8；30]，Δf _min＝[-0.6；-0.6；-0.6；-0.6；-0.6]，Δf _max＝[0.6；0.6；0.6；0.6；0.6]，

α＝[α ₁；α ₂；α ₃；α ₄；α ₅]，α _min＝[-∞；-∞；-∞；-∞；-∞]，α _max＝[∞；∞；∞；∞；∞]，

Δα _min＝[-1.2；-1.2；-1.2；-1.2；-1.2]，

Δα _max＝[1.2；1.2；1.2；1.2；1.2]， $B\left(\mathrm{\α}\right)=\left[\begin{array}{ccccc}0& \cos {\mathrm{\α}}_2& \cos {\mathrm{\α}}_3& 0& 1\\ 1& \sin {\mathrm{\α}}_2& \sin {\mathrm{\α}}_3& 1& 0\\ 2.46& 2.36\sin {\mathrm{\α}}_2& -1.82\sin {\mathrm{\α}}_3& -2.16& 0\end{array}\right],$

τ is the thrust instruction that controller sends, and s is a thrust error, P _i, ρ, ε be coefficient, Q, Ω, W are matrix of coefficients.

Then, execution in step 32: with the optimization problem of genetic algorithm for solving thrust.

The objective function of optimization problem is the fitness function in the genetic algorithm, and the constraint condition of optimization problem is the solution space in the genetic algorithm.

The method of concrete solving-optimizing is:

If

is the thrust instruction of controller output; The thrust instruction comprises the power of needed surge direction, the power and the yawing moment of swaying direction; The thrust that

sends for each thruster; Then need satisfy τ=B (α) f, wherein

$B\left(\mathrm{\α}\right)=\left(\begin{array}{cccc}\mathrm{Cos}{\mathrm{\α}}_1& \mathrm{Cos}{\mathrm{\α}}_2& L& \mathrm{Cos}{\mathrm{\α}}_n\\ \mathrm{Sin}{\mathrm{\α}}_1& \mathrm{Sin}{\mathrm{\α}}_2& L& \mathrm{Sin}{\mathrm{\α}}_n\\ -y_1\mathrm{Cos}{\mathrm{\α}}_1+x_1\mathrm{Sin}{\mathrm{\α}}_1& -y_2\mathrm{Cos}{\mathrm{\α}}_2+x_2\mathrm{Sin}{\mathrm{\α}}_2& L& -y_n\mathrm{Cos}{\mathrm{\α}}_n+x_n\mathrm{Sin}{\mathrm{\α}}_n\end{array}\right)$ Formula (1)

α _iBe i angle of rake position angle, (x _i, y _i) be i angle of rake coordinate, n is a thruster quantity;

Find the solution following formula (2) according to formula (1):

$\min \{J=\underset{i=1}{\overset{r}{\mathrm{\Σ}}}{P}_i{\left|f\right|}^{3/2}+s^T\mathrm{Qs}+{(\mathrm{\α}-{\mathrm{\α}}_0)}^T\mathrm{\Ω}(\mathrm{\α}-{\mathrm{\α}}_0)+\frac{\mathrm{\ρ}}{\mathrm{\ϵ}+\det \left(B\left(\mathrm{\α}\right)W^{-1}{B}^T\left(\mathrm{\α}\right)\right)}\}$

s.t.B(α)f＝τ+s

f _Min≤f≤f _MaxFormula (2)

Δf _min≤f-f ₀≤Δf _max

α _min≤α≤α _max

Δα _min≤α-α ₀≤Δα _max

Wherein, P _iBe the weights coefficients, Q, Ω are the weights matrix of coefficients, and ε is greater than 0 one decimals, and ρ is a weights coefficient greater than 0, f _MinBe the thrust lower limit that each thruster can send, f _MaxBe the thrust upper limit that each thruster can send, α _MinBe the lower limit at each thruster gyrobearing angle, α _MaxBe the upper limit at each thruster gyrobearing angle, f ₀Be each angle of rake thrust size that a last sampling instant calculates, α ₀The position angle of each thruster that calculates for a last sampling instant, Δ f _MinFor each thruster sends the minimum change speed of thrust, Δ f _MaxFor each thruster sends the maximum pace of change of thrust, Δ α _MinBe the azimuthal minimum rotational speed of each thruster, Δ α _MaxBe the azimuthal maximum rotative speed of each thruster.

In the objective function first

Represent total energy consumption, second s ^TThe error of making a concerted effort of Qs punishment thrust instruction and each propeller thrust, the 3rd (α-α ₀) ^TΩ (α-α ₀) the azimuthal variation of punishment thruster, the 4th

Be used to avoid singular structure.

F in the constraint condition _Min≤f≤f _MaxBe restriction, Δ f to the propeller thrust scope _Min≤f-f ₀≤Δ f _MaxBe the restriction of thrust pace of change, α _Min≤α≤α _MaxBe restriction, Δ α to azimuthal scope _Min≤α-α ₀≤Δ α _MaxRestriction for azimuthal variation speed.

The concrete grammar that Fig. 4 adopts genetic algorithm optimization dynamic positioning of vessels thrust to distribute for the present invention.In above-mentioned solving-optimizing process, fitness function is the objective function in the formula (1); Solution space is the constraint condition in the formula (1), and at first with the genotype string structure data that data are expressed as hereditary space of separating of solution space, the various combination of these string structure data has just constituted different points; Initial solution adopts the method for completely random to produce, and concrete solving-optimizing step is following:

Step 41: generate initial population with coding form.

Step 42: use fitness function to carry out the fitness assessment of each individuals in the initial population.If the fitness function value that all chromosomes are corresponding is almost equal, then execution in step 43, if unequal, then finish.

Step 43: will select operator to act on colony.The purpose of selection is the direct hereditary next generation of arriving of the individuality of an optimization or the hereditary again next generation of arriving of the new individuality of intersection generation that passes through to match.

Step 44: crossover operator is acted on colony.So-called intersection is meant the operation of replacing the individual part-structure of two parents reorganization and the new individuality of generation.

Step 45: mutation operator is acted on colony.Some genic value to the individuality string in the colony changes;

Step 46: carry out the judgement of end condition.

After using the present invention's genetic algorithm for solving as above to finish, both size and the optimal allocation of its azimuth angle alpha of the thrust f that sent of each thruster of T1-T5.

The invention has the beneficial effects as follows: adopt genetic algorithm for solving thrust assignment problem, can effectively solve the problem that the objective function constraints contains nonlinear terms, widened the scope of the problem that can find the solution greatly.In addition; Can find out from result of calculation, this algorithm can be effectively with the thrust command assignment of surging, swaying and three directions of yawing to each thruster, each propeller thrust changes steadily; And the azimuthal position of thruster is comparatively reasonable, can effectively avoid the generation of singular structure.The present invention can be widely used in the thrust of the thruster of various boats and ships, various quantity and distribute, and can use minimum energy to produce best thrust, energy savings greatly.

More than show and described ultimate principle of the present invention, principal character and advantage of the present invention.The technician of the industry should understand; The present invention is not restricted to the described embodiments; That describes in the foregoing description and the instructions just explains principle of the present invention; The present invention also has various changes and modifications under the prerequisite that does not break away from spirit and scope of the invention, and these variations and improvement all fall in the scope of the invention that requires protection.The present invention requires protection domain to be defined by appending claims and equivalent thereof.

Claims (3)

1. adopt the dynamic positioning of vessels system thrust distribution method of genetic algorithm, it is characterized in that, comprise the steps:

Step 31 is converted into optimization problem with the thrust assignment problem;

$\min \{J=\underset{i=1}{\overset{r}{\mathrm{\Σ}}}{P}_i{\left|f\right|}^{3/2}+s^T\mathrm{Qs}+{(\mathrm{\α}-{\mathrm{\α}}_0)}^T\mathrm{\Ω}(\mathrm{\α}-{\mathrm{\α}}_0)+\frac{\mathrm{\ρ}}{\mathrm{\ϵ}+\det \left(B\left(\mathrm{\α}\right)W^{-1}{B}^T\left(\mathrm{\α}\right)\right)}\}$

s.t.B(α)f＝τ+s

f _min≤f≤f _max

Δf _min≤f-f ₀≤Δf _max

α _min≤α≤α _max

Δα _min≤α-α ₀≤Δα _max

Wherein, f=[f ₁f ₂f ₃f ₄f ₅], f _Min=[3.2;-2.56;-2.56;-1.8;-21],

f _max＝[3.2；4.18；4.18；1.8；30]，Δf _min＝[-0.6；-0.6；-0.6；-0.6；-0.6]，

Δf _max＝[0.6；0.6；0.6；0.6；0.6]，α＝[α ₁；α ₂；α ₃；α ₄；α ₅]，α _min＝[-∞；-∞；-∞；-∞；-∞]，

α _max＝[∞；∞；∞；∞；∞]，Δα _min＝[-1.2；-1.2；-1.2；-1.2；-1.2]，

Δα _max＝[1.2；1.2；1.2；1.2；1.2]， $B\left(\mathrm{\α}\right)=\left[\begin{array}{ccccc}0& \cos {\mathrm{\α}}_2& \cos {\mathrm{\α}}_3& 0& 1\\ 1& \sin {\mathrm{\α}}_2& \sin {\mathrm{\α}}_3& 1& 0\\ 2.46& 2.36\sin {\mathrm{\α}}_2& -1.82\sin {\mathrm{\α}}_3& -2.16& 0\end{array}\right],$

τ is the thrust instruction that controller sends, and s is a thrust error, P _i, ρ, ε be coefficient, Q, Ω, W are matrix of coefficients;

Step 32 is with the optimization problem of genetic algorithm for solving thrust;

The objective function of optimization problem is the fitness function in the genetic algorithm, and the constraint condition of optimization problem is the solution space in the genetic algorithm.

2. according to the dynamic positioning of vessels system thrust distribution method of the said employing genetic algorithm of claim 1, it is characterized in that this step 32 uses the concrete grammar of the optimization problem of genetic algorithm for solving thrust to be:

(1) establishes and be the thrust instruction of controller output; The thrust instruction comprises the power of needed surge direction, the power and the yawing moment of swaying direction; The thrust that

sends for each propeller; Then need satisfy τ=B(α) f; Wherein

$B\left(\mathrm{\α}\right)=\left(\begin{array}{cccc}\cos {\mathrm{\α}}_1& \cos {\mathrm{\α}}_2& L& \cos {\mathrm{\α}}_n\\ \sin {\mathrm{\α}}_1& \sin {\mathrm{\α}}_2& L& \sin {\mathrm{\α}}_n\\ -y_1\cos {\mathrm{\α}}_1+x_1\sin {\mathrm{\α}}_1& -y_2\cos {\mathrm{\α}}_2+x_2\sin {\mathrm{\α}}_2& L& -y_n\cos {\mathrm{\α}}_n+x_n\sin {\mathrm{\α}}_n\end{array}\right)$

α _iBe i angle of rake position angle, (x _i, y _i) be i angle of rake coordinate, n is a thruster quantity;

(2) find the solution following formula according to formula in the step (1):

$\min \{J=\underset{i=1}{\overset{r}{\mathrm{\Σ}}}{P}_i{\left|f\right|}^{3/2}+s^T\mathrm{Qs}+{(\mathrm{\α}-{\mathrm{\α}}_0)}^T\mathrm{\Ω}(\mathrm{\α}-{\mathrm{\α}}_0)+\frac{\mathrm{\ρ}}{\mathrm{\ϵ}+\det \left(B\left(\mathrm{\α}\right)W^{-1}{B}^T\left(\mathrm{\α}\right)\right)}\}$

s.t.B(α)f＝τ+s

f _min≤f≤f _max

Δf _min≤f-f ₀≤Δf _max

α _min≤α≤α _max

Δα _min≤α-α ₀≤Δα _max

Wherein, first energy consumption that representative is total, the error of making a concerted effort of the instruction of second punishment thrust and each propeller thrust, the 3rd the azimuthal variation of punishment thruster, α ₀Be the angle of rake position angle of previous moment, the 4th is used to avoid singular structure; Constraint condition comprises the restriction to the thruster maximum thrust, the restriction of the maximum pace of change of thrust, azimuthal scope, the restriction of the maximal rate of azimuthal variation.

3. according to the dynamic positioning of vessels system thrust distribution method of the said employing genetic algorithm of claim 2, it is characterized in that fitness function is the objective function in this step (1); Solution space is the constraint condition in this step (1), and with the genotype string structure data that data are expressed as hereditary space of separating of solution space, the various combination allosteric of these string structure data has become different points; Initial solution adopts the method for completely random to produce, and this concrete calculation procedure of optimization problem of finding the solution thrust is following:

Step 41 generates initial population with coding form;

Step 42 uses fitness function to carry out the fitness assessment of each individuals in the initial population;

Step 43 will select operator to act on colony;

Step 44 acts on colony with crossover operator;

Step 45 acts on colony with mutation operator;

Step 46 is carried out the judgement of end condition;

Obtain the size and its azimuth angle alpha of each propeller thrust f.

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