CN102385665B - 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 thrust force distribution method of power location system of ship of genetic algorithm

Technical field

The present invention relates to a kind of thrust force distribution method of power location system of ship, particularly relate to a kind of distribution method adopting the Ship Dynamic Positioning Systems Based thrust of genetic algorithm.

Background technology

Genetic algorithm (Genetic Algorithm) is that the evolution laws (survival of the fittest, survival of the fittest genetic mechanism) of a class reference organic sphere develops and next randomization searching method.It is taught by the J.Holland of the U.S. first to propose for 1975, and its principal feature directly operates structure objects, there is not the successional restriction of differentiate sum functions; There is inherent Implicit Parallelism and better global optimizing ability; Adopt the optimization method of randomization, the search volume that energy automatic acquisition and guidance are optimized, adjust the direction of search adaptively, do not need the rule determined.These character of genetic algorithm, are widely used in the fields such as Combinatorial Optimization, machine learning, signal transacting, adaptive control and artificial life by people.It is modern about the gordian technique in intelligent computation.

Enter the nineties, genetic algorithm has welcome prosperous developing period, is that theoretical research or applied research have all become very popular problem.Especially the applied research of genetic algorithm seems especially active, and not only its application expands, and the ability utilizing genetic algorithm to be optimized Sum fanction study also significantly improves, and the research of industry application aspect is also among groping simultaneously.In addition some new Theories and methods also obtain and develop rapidly in applied research, and these all add new vitality to genetic algorithm undoubtedly.The applied research of genetic algorithm solves the application aspect that extend to many renewals, more through engineering approaches from the Combinatorial Optimization at initial stage.

Ship Dynamic Positioning Systems Based is a kind of control system of closed loop, measuring system is utilized to detect horizontal drift amount and the azimuth deviation amount of boats and ships under the disturbing force effect of external wind, wave, stream, robot calculator is inputted after signal transacting, after carrying out analytical calculation with special software, each thruster thrust output instruction of being installed on ship by thruster, make it to send corresponding thrust, balance is reached, in the position making ship be returned to (or remaining on) originally setting and orientation with environmental perturbation power.

How thrust is assigned on each thruster without effective method answer by prior art, and the energy that thruster is consumed is comparatively large, the easy excessive wear of thruster.So be badly in need of a kind of thrust force distribution method of power location system of ship to solve the problems referred to above.

Summary of the invention

The present invention is exactly to solve the problem, and overcomes in prior art the defect of distributing on boats and ships existing for angle of rake dynamical problem, the invention provides and adopts the thrust force distribution method of power location system of ship of genetic algorithm to address the aforementioned drawbacks and problem.

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

Adopt the thrust force distribution method of power location system of ship of genetic algorithm, it is characterized in that, it comprises the steps:

Step 31: 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 ₁; f ₂; f ₃; f ₄; f 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], $B\left(\mathrm{\α}\right)=\left[\begin{array}{ccccc}0& \cos {\mathrm{\α}}_2& \cos {\mathrm{\α}}_3& 0& 1\\ 1& \sin {\mathrm{\α}}_2& \sin {\mathrm{\α}}_3& 1& 0\\ 2.46& 2.36\sin {\mathrm{\α}}_2& -1.82\sin {\mathrm{\α}}_3& -2.16& 0\end{array}\right],$

F _minfor the thrust lower limit that each thruster can send, f _maxfor the thrust upper limit that each thruster can send, 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, α _minfor the lower limit at each thruster gyrobearing angle, α _maxfor the upper limit at each thruster gyrobearing angle, Δ α _minfor the azimuthal minimum rotational speed of each thruster, Δ α _maxfor the azimuthal maximum rotative speed of each thruster, τ is the thrust command that controller sends, and s is thrust error, ε be greater than 0 a decimal;

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

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

With the concrete grammar of the optimization problem of genetic algorithm for solving thrust be:

(1) τ ∈ i is established ³for the thrust command that controller exports, thrust command comprises the power of required surge direction, the power in swaying direction and yawing moment, f ∈ i ⁿfor the thrust that each thruster sends, then demand fulfillment τ=B (α) f, wherein,

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

(2) according to the following formula of equations in 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, Section 2 s ^tthe error of making a concerted effort of Qs representative punishment thrust command and each propeller thrust, Section 3 (α-α ₀) ^tΩ (α-α ₀) the azimuthal change of representative punishment thruster, α ₀for the angle of rake position angle of previous moment, Section 4 for avoiding singular structure; F in constraint condition _min≤ f≤f _maxfor the restriction to propeller thrust scope, Δ f _min≤ f-f ₀≤ Δ f _maxfor the restriction of thrust variation speed, α _min≤ α≤α _maxfor the restriction of the scope of azimuthal, Δ α _min≤ α-α ₀≤ Δ α _maxfor the restriction of azimuthal variation speed.

As above a kind of thrust force distribution method of power location system of ship adopting genetic algorithm, wherein, the solution data of solution space are expressed as the genotype string structure data in hereditary space, the various combination of these string structure data just constitutes different points; Initial solution adopts the method for completely random to produce, and this concrete calculation procedure solving the optimization problem of thrust is as follows:

Step 41: generate initial population in a coded form;

Step 42: use fitness function to carry out the Fitness analysis of each individuality in initial population;

Step 43: selection opertor is acted on colony;

Step 44: crossover operator is acted on colony;

Step 45: mutation operator is acted on colony;

Step 46: the judgement carrying out end condition;

Obtain 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 objective function and constraint condition contain nonlinear terms, greatly widened the scope of the problem that can solve.The thrust command in surging, swaying and yawing three directions can effectively be assigned on each thruster by the inventive method, and steadily, and the azimuthal position of thruster is comparatively reasonable, effectively can avoid the generation of singular structure for the change of each propeller thrust.

Accompanying drawing explanation

The present invention is described in detail below in conjunction with the drawings and specific embodiments:

Fig. 1 is that schematic diagram arranged by marine propeller.

Fig. 2 is overall framework schematic diagram of the present invention.

Fig. 3 is method schematic diagram of the present invention.

Fig. 4 is the concrete grammar that the present invention adopts genetic algorithm optimization dynamic positioning of vessels thrust and distributes.

Embodiment

The technological means realized to make the present invention, creation characteristic, reaching object and effect is easy to understand, below in conjunction with concrete diagram, setting forth the present invention further.Algorithm of the present invention is divided into two parts, and Part I is for be converted into an optimization problem by thrust assignment problem, and the second part is for adopting this optimization problem of genetic algorithm for solving.

Fig. 1 is that schematic diagram arranged by the thruster on boats and ships 100.This schematic diagram for model, with this model for calculating object, illustrates the inventive method with certain shuttler.Boats and ships 100 are provided with T1-T5 totally 5 thrusters, and T1 is bow thruster, and T2 is bow full circle swinging, and T3 is stern full circle swinging, and T4 is stern thruster, and T5 is for promoting mainly.The maximum pace of change in position angle of all-direction propeller T2 and T3 $\max \left|\stackrel{\·}{\mathrm{\α}}\right|=12\mathrm{deg}/s,$ The maximum pace of change of thrust $\max \left|\stackrel{\·}{f}\right|=6N/s.$

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 size and different directions, so there is the combination in countless multiple different thrust and direction, meets specific horizontal force and yawing moment.

Fig. 2 illustrates overall framework of the present invention, and the present invention comprises computing machine 21, and on it, mounting software performs the inventive method, angle of rake thrust assignment problem is converted into optimization problem to obtain the size and Orientation of best thrust; Controller 22, is connected with each thruster with computing machine 21, and the thrust size and Orientation of the best that controller 22 calculates according to this software controls each thruster and exports this thrust.

Fig. 3 is method schematic diagram of the present invention.First, perform step 31: thrust assignment problem is converted into optimization problem, objective function and constraint condition 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 ₁; f ₂; f ₃; f ₄; f 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], $B\left(\mathrm{\α}\right)=\left[\begin{array}{ccccc}0& \cos {\mathrm{\α}}_2& \cos {\mathrm{\α}}_3& 0& 1\\ 1& \sin {\mathrm{\α}}_2& \sin {\mathrm{\α}}_3& 1& 0\\ 2.46& 2.36\sin {\mathrm{\α}}_2& -1.82\sin {\mathrm{\α}}_3& -2.16& 0\end{array}\right],$

F _minfor the thrust lower limit that each thruster can send, f _maxfor the thrust upper limit that each thruster can send, 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, α _minfor the lower limit at each thruster gyrobearing angle, α _maxfor the upper limit at each thruster gyrobearing angle, Δ α _minfor the azimuthal minimum rotational speed of each thruster, Δ α _maxfor the azimuthal maximum rotative speed of each thruster, τ is the thrust command that controller sends, and s is thrust error, ε be greater than 0 a decimal;

Then, step 32 is performed: by the optimization problem of genetic algorithm for solving thrust.

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

The method of concrete solving-optimizing is:

If τ ∈ is i ³for the thrust command that controller exports, thrust command comprises the power of required surge direction, the power in swaying direction and yawing moment, f ∈ i ⁿfor the thrust that each thruster sends, then demand fulfillment τ=B (α) f, wherein

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

Following formula (2) is solved 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 weights coefficients, Q, Ω are weights matrix of coefficients, ε for being greater than 0 one decimals, ρ be greater than 0 a weights coefficient, f _minfor the thrust lower limit that each thruster can send, f _maxfor the thrust upper limit that each thruster can send, α _minfor the lower limit at each thruster gyrobearing angle, α _maxfor the upper limit at each thruster gyrobearing angle, f ₀for each angle of rake thrust size that a upper sampling instant calculates, α ₀for the position angle of each thruster that a upper sampling instant calculates, 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 _minfor the azimuthal minimum rotational speed of each thruster, Δ α _maxfor the azimuthal maximum rotative speed of each thruster.

Section 2 s ^tqs punishes the error of making a concerted effort of thrust command and each propeller thrust, Section 3 (α-α ₀) ^tΩ (α-α ₀) the azimuthal change of punishment thruster, Section 4 for avoiding singular structure.

F in constraint condition _min≤ f≤f _maxfor the restriction to propeller thrust scope, Δ f _min≤ f-f ₀≤ Δ f _maxfor the restriction of thrust variation speed, α _min≤ α≤α _maxfor the restriction of the scope of azimuthal, Δ α _min≤ α-α ₀≤ Δ α _maxfor the restriction of azimuthal variation speed.

Fig. 4 is the concrete grammar that the present invention adopts genetic algorithm optimization dynamic positioning of vessels thrust and distributes.In above-mentioned solving-optimizing process, first the solution data of solution space are expressed as the genotype string structure data in hereditary space, the various combination of these string structure data just constitutes different points; Initial solution adopts the method for completely random to produce, and concrete solving-optimizing step is as follows:

Step 41: generate initial population in a coded form.

Step 42: use fitness function to carry out the Fitness analysis of each individuality in initial population.If the fitness function value that all chromosome is corresponding is almost equal, then performs step 43, if unequal, then terminate.

Step 43: selection opertor is acted on colony.The object selected the individuality optimized is genetic directly to the next generation or produces new individuality by pairing intersection be genetic to the next generation again.

Step 44: crossover operator is acted on colony.So-called intersection refers to is replaced restructuring the part-structure of two parent individualities and is generated new individual operation.

Step 45: mutation operator is acted on colony.Some genic value of individuality string in colony is changed;

Step 46: the judgement carrying out end condition.

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

The invention has the beneficial effects as follows: adopt genetic algorithm for solving thrust assignment problem, effectively can solve the problem that objective function and constraint condition contain nonlinear terms, widen the scope of the problem that can solve greatly.In addition, as can be seen from result of calculation, the thrust command in surging, swaying and yawing three directions can effectively be assigned on each thruster by this algorithm, and the change of each propeller thrust steadily, and the azimuthal position of thruster is comparatively reasonable, effectively can avoid the generation of singular structure.The present invention can be widely used in various boats and ships, the thrust of thruster of various quantity is distributed, and can produce best thrust, economize energy greatly with minimum energy.

More than show and describe 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; what describe in above-described embodiment and instructions just illustrates principle of the present invention; the present invention also has various changes and modifications without departing from the spirit and scope of the present invention, and these changes and improvements all fall in the claimed scope of the invention.Application claims protection domain is defined by appending claims and equivalent thereof.

Claims (2)

1. adopt the thrust force distribution method of power location system of ship of genetic algorithm, it is characterized in that, comprise the steps:

Step 31, is converted into optimization problem by thrust assignment problem;

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

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 ₁; f ₂; f ₃; f ₄; f 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]，

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

F _minfor the thrust lower limit that each thruster can send, f _maxfor the thrust upper limit that each thruster can send, 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, α _minfor the lower limit at each thruster gyrobearing angle, α _maxfor the upper limit at each thruster gyrobearing angle, Δ α _minfor the azimuthal minimum rotational speed of each thruster, Δ α _maxfor the azimuthal maximum rotative speed of each thruster, τ is the thrust command that controller sends, and s is thrust error, ε be greater than 0 a decimal;

Step 32, by the optimization problem of genetic algorithm for solving thrust;

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

With the concrete grammar of the optimization problem of genetic algorithm for solving thrust be:

(1) τ ∈ i is established ³for the thrust command that controller exports, thrust command comprises the power of required surge direction, the power in swaying direction and yawing moment, f ∈ i ⁿfor the thrust that each thruster sends, then demand fulfillment τ=B (α) f, wherein,

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

(2) according to the following formula of equations in 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, Section 2 s ^tthe error of making a concerted effort of Qs representative punishment thrust command and each propeller thrust, Section 3 (α-α ₀) ^tΩ (α-α ₀) the azimuthal change of representative punishment thruster, α ₀for the angle of rake position angle of previous moment, Section 4 for avoiding singular structure; F in constraint condition _min≤ f≤f _maxfor the restriction to propeller thrust scope, Δ f _min≤ f-f ₀≤ Δ f _maxfor the restriction of thrust variation speed, α _min≤ α≤α _maxfor the restriction of the scope of azimuthal, Δ α _min≤ α-α ₀≤ Δ α _maxfor the restriction of azimuthal variation speed.

2. adopt the thrust force distribution method of power location system of ship of genetic algorithm according to claim 1, it is characterized in that, the solution data of solution space are expressed as the genotype string structure data in hereditary space, the various combination of these string structure data just constitutes different points; Initial solution adopts the method for completely random to produce, and this concrete calculation procedure solving the optimization problem of thrust is as follows:

Step 41, generates initial population in a coded form;

Step 42, uses fitness function to carry out the Fitness analysis of each individuality in initial population;

Step 43, acts on colony by selection opertor;

Step 44, acts on colony by crossover operator;

Step 45, acts on colony by mutation operator;

Step 46, carries out the judgement of end condition;

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