CN112506060B - Ship thrust distribution method based on mixed group optimization algorithm - Google Patents
Ship thrust distribution method based on mixed group optimization algorithm Download PDFInfo
- Publication number
- CN112506060B CN112506060B CN202011478093.0A CN202011478093A CN112506060B CN 112506060 B CN112506060 B CN 112506060B CN 202011478093 A CN202011478093 A CN 202011478093A CN 112506060 B CN112506060 B CN 112506060B
- Authority
- CN
- China
- Prior art keywords
- individual
- optimal
- solution
- thrust
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses a ship thrust distribution method of a mixed group optimization algorithm, which comprises the following steps: (1) initializing various parameters according to the actual condition of the ship object; (2) obtaining an optimal solution and an optimal sub-sector by applying a mixed group optimization algorithm of 'best-effort' according to the parameter values in the step (1), the thrust distribution solution at the previous moment and the current demand force and moment; (3) determining a most suitable sector combination by adopting a dwell time switching technology and a hysteresis switching technology according to the optimal solution and the optimal sub-sector obtained in the step (2); (4) determining the azimuth angle and the thrust of each propeller by applying a 'best-effort' mixed group optimization algorithm according to the most suitable sub-sector combination selected in the step (3); (5) and (5) outputting the thrust distribution result of the step (4) and preparing the thrust distribution solution at the next sampling moment. The invention reduces the energy consumption of the ship system, reduces the abrasion of the propeller and avoids a strange structure.
Description
Technical Field
The invention relates to the field of group intelligence technology and thrust distribution technology, in particular to a ship thrust distribution method based on a mixed group optimization algorithm.
Background
With the rapid rise of the marine industry, a ship dynamic positioning system has become an indispensable system for many marine engineering ships, particularly for deep sea operation ships. The dynamic positioning ship generally adopts an over-drive system, namely, a resultant force instruction required by the system is reasonably distributed to each propeller, so that the thrust distribution problem of the dynamic positioning control system is mainly a constraint optimization problem, namely, the power consumption and the mechanical wear are minimized while a certain resultant force requirement and the physical condition constraint of the propellers are met, and the singularity and the like are avoided. Although the conventional thrust force distribution method is widely applied to the thrust force distribution system, the following problems also exist: all constraints of thrust distribution cannot be comprehensively considered; the operation is complex and is easy to fall into local extreme points. With the continuous development of emerging group intelligent algorithms and the application of the emerging group intelligent algorithms in the engineering field in recent years, the algorithms do not depend on the form of the characteristic function and the solution of an object problem, and can effectively solve the complex optimization problems of multiple constraints, nonlinearity and multiple dimensions, so that the method finds a new way for solving the thrust distribution problem.
Disclosure of Invention
The invention aims to provide a ship thrust distribution method based on a mixed group optimization algorithm, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a ship thrust distribution method of a mixed group optimization algorithm comprises the following steps:
(1) initializing various parameters according to the actual condition of the ship object;
(2) obtaining an optimal solution and an optimal sub-sector by applying a mixed group optimization algorithm c of 'best-effort' according to the parameter values in the step (1), the thrust distribution solution at the previous moment and the current demand force and moment;
(3) determining a most suitable sector combination by adopting a dwell time switching technology and a hysteresis switching technology according to the optimal solution and the optimal sub-sector obtained in the step (2);
(4) determining the azimuth angle and the thrust of each propeller by applying a 'best-effort' mixed group optimization algorithm according to the most suitable sub-sector combination selected in the step (3);
(5) and (5) outputting the thrust distribution result of the step (4) and preparing the thrust distribution solution at the next sampling moment.
Preferably, the step of the clustering algorithm in step (2) is as follows:
1) and initializing an algorithm, comprising: population initialization, individual X ═ X1，X2，…，XNCalculating and recording the current individual optimal value and the position thereof, the global optimal value and the individual thereof;
2) performing population evolution and individual updating by adopting a bidirectional optimization and exhaustive solving method;
3) selecting p1 optimal individuals for local optimization, specifically as follows:
Xi(t+1)＝Xi(t)+rand()×visual1
in order to improve the local optimization capacity, the value of visual1 in the formula cannot be too large, each individual update is used for deriving V1 children on the basis of a parent, and the individual value and the global optimal value are updated.
4) Selecting p2 worst individuals for local optimization, specifically as follows:
Xi(t+1)＝Xi(t)+rand()×visual2
in order to make the individual jump out of the current unfavorable position, the value of visual2 in the formula cannot be too small, V2 children are derived on the basis of a parent for each individual update, and the individual value and the global optimal value are updated.
5) And if the iteration times reach the set value, ending the process, otherwise, executing the step 2).
Preferably, the bidirectional optimization and exhaustive search method in the step 2) comprises the following steps:
let each individual optimal solution be denoted as PibestThe global optimal solution is denoted as Pgbest；
a. Each individual update is as follows:
Xi(t+1)＝Xi(t)+ΔXi(t)
wherein
i≠j，sl1，sl2Is the moving step length;
calculating individual adaptive values and judging whether optimization is obtained, if so, updating the individual optimal solution and the individual optimal adaptive value, and executing step c, otherwise, executing step b;
b. each individual update is as follows:
Xi(t+1)＝Xi(t)-ΔXi(t)
calculating individual adaptive values and judging whether optimization is obtained, if so, updating the individual optimal solution and the individual optimal adaptive value, and executing d, otherwise, executing e;
c. each individual update is as follows:
Xi(t+1)＝Xi(t)+cpiΔXi(t)，cpi＞1
calculating individual adaptive value and judging whether the optimization is obtained, if so, updating the individual optimal solution and the individual optimal adaptive value, and updating cpiC is executed, otherwise g is executed;
cpi＝cpi(1+c1×rand())
d. each individual update is as follows:
Xi(t+1)＝Xi(t)-cniΔXx(t)，cni＞1
calculating individual adaptive value and judging whether the optimization is obtained, if so, updating the individual optimal solution and the individual optimal adaptive value, and updating cniExecuting d, otherwise executing g;
cni＝cni(1+c2×rand())
e. if the number of times of the attempt reaches a set value m, executing f if the number of times of the attempt reaches the set value m, otherwise executing a;
f. after the m times of trial, if the individual fitness value is not improved and is not the global optimum, executing a random operator, and if the individual fitness value is the global optimum, keeping the current individual value;
g. and (5) finishing the evolution of the individuals, and updating the global optimal value.
Preferably, how to switch the specific judgment measure by the combination of the sectors in the step (3) is as follows:
Ibestsearching the sub-sector combination of the optimal thrust distribution obtained by all the sub-sectors by using an 'extreme solution' mixed group optimization algorithm, InIs the optimum sub-sector combination in which the handover is not yet performed, IPIs referred to as InThe optimum sub-sector combination previously selected, t being IPSwitch to InTime until the present time, tdtsMinimum switching time, t, for dwell time switchinghThe maximum return switching time is set for preventing switching caused by transient interference, and the value of the maximum return switching time is not more than tdtsJP (-) is a cost function value that does not contain angle changes, JPlimIs the minimum change of the cost function to be limited during the lag switching, S (-) is the sum of the relaxation variables, SdtsThe maximum value of the sum and the change of the relaxation variables needing to be limited during the hysteresis switching is a percentage.
Preferably, the calculation of the azimuth angle and the thrust magnitude of each thruster in the step (4) is as follows:
1) when I isn＝inowAnd Ibest＝inowWhen i isnowFor the current sector combination, the 'extreme solution' mixed group optimization algorithm a is used for solving, the azimuth angle of the final propeller is determined according to the solved optimal angle solution and the global optimal angle solution, and finally the 'extreme solution' mixed group optimization algorithm b is used for solving according to the solved azimuth angleThe magnitude of the thrust;
2) when I isn＝inowAnd Ibest≠inowWhen is, or In≠inowAnd i isnowWhen the thruster is not in the forbidden area, the azimuth angle and the thrust of the thruster are solved by directly applying the extreme solution mixed group optimization algorithm a;
3) in other cases, firstly, the size of the azimuth angle of the propeller is determined, and then the thrust size of the propeller is solved by using a mixed group optimization algorithm b of 'extreme solution' under the condition that the azimuth angle is known.
Compared with the prior art, the invention has the beneficial effects that: on the basis of innovatively providing a mixed group optimization algorithm of 'best-effort' and combining the algorithm with a supervision and switching thrust distribution mechanism, the formed thrust distribution strategy can reduce the energy consumption of a ship system and simultaneously reduce the mechanical wear of a propeller and avoid a singular structure as much as possible.
Drawings
FIG. 1 is a schematic flow chart of the "best-effort" mixed group optimization algorithm of the present invention;
FIG. 2 is a schematic thrust sharing flow diagram of the present invention;
FIG. 3 is a thrust force plot of the thrust force distribution output of the present invention;
fig. 4 is an azimuthal plot of the thrust sharing output of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution: a ship thrust distribution method of a mixed group optimization algorithm comprises the following steps:
(1) initializing the following parameters according to the actual condition of the ship object: the method comprises the steps of setting the thrust and azimuth angles of all the thrusters, the resultant force and resultant moment of all the thrusters, a ship thrust coefficient matrix, the maximum and minimum thrusts, the maximum increase and decrease of the thrust, the maximum change rate of the azimuth angles, a ship position construction matrix, power and thrust relation parameters, the current optimal sector combination, the previous optimal sector combination, the global optimal sector combination, the minimum switching time of dwell time switching, and the maximum return switching time set for switching due to transient interference prevention;
(2) according to the parameter values in the step (1), the thrust distribution solution at the previous moment and the current demand force and moment, a mixed group optimization algorithm c (cost function comprises energy consumption, force and moment deviation and a singular structure) is obtained by applying the extreme solution in the figure 1 to obtain an optimal solution and an optimal sub-sector;
(3) determining a most suitable sector combination by adopting a dwell time switching technology and a hysteresis switching technology according to the optimal solution and the optimal sub-sector obtained in the step (2), wherein the specific judgment measures are as follows;
Ibestsearching the sub-sector combination of the optimal thrust distribution obtained by all the sub-sectors by using an 'extreme solution' mixed group optimization algorithm, InIs the optimum sub-sector combination in which the handover is not yet performed, IPIs referred to as InThe optimum sub-sector combination previously selected, t being IPSwitch to InTime until the present time, tdtsMinimum switching time, t, for dwell time switchinghThe maximum return switching time is set for preventing switching caused by transient interference, and the value of the maximum return switching time is not more than tdtsJP (-) is a cost function value that does not contain angle changes, JPlimIs the minimum change of the cost function to be limited during the lag switching, S (-) is the sum of the relaxation variables, SdtsThe maximum value of the sum and the change of the relaxation variables needing to be limited during the hysteresis switching is a percentage.
(4) And (3) according to the most suitable sub-sector combination in the step (3), determining the azimuth angle and the thrust of each propeller by using a 'pole-to-pole-solving' mixed group algorithm in the figure 1, and specifically comprising the following steps:
1) when I isn＝inowAnd Ibest＝inowWhen i isnowAnd for the current sector combination, solving by using a 'minimal solution' mixed group optimization algorithm a (cost function comprises energy consumption, force and moment deviation, azimuth angle change rate and singular structure), determining the azimuth angle of the final propeller according to the solved optimal angle solution and combining with a global optimal angle solution, and finally solving the final thrust size by using a 'minimal solution' mixed group optimization algorithm b (cost function comprises energy consumption, force and moment deviation) according to the solved azimuth angle.
2) When I isn＝inowAnd Ibest≠inowWhen is, or In≠inowAnd i isnowWhen the thruster is not in the forbidden area, the 'extreme solution' mixed group optimization algorithm a (cost functions comprise energy consumption, force and moment deviation, azimuth angle change rate and singular structures) is directly applied to solve the azimuth angle and thrust of the thruster.
3) In other cases, the azimuth angle of the propeller is firstly determined, and then the thrust magnitude of the propeller is solved by using a mixed group optimization algorithm b (cost function comprises energy consumption and force and moment deviation) under the condition that the azimuth angle is known.
(5) And (5) outputting the thrust distribution result of the step (4) and preparing the thrust distribution solution at the next sampling moment.
The technical scheme of the invention is explained in detail by combining the examples of the ship model of the CybershipIII and the figures 1, 2, 3 and 4 as follows:
(1) initializing the following parameters according to the actual condition of the ship object: the thrust magnitude and azimuth are [0, 0, 0, 90, 0, 0], all sector combinations are initially 1, the resultant force and resultant moment magnitudes are both 0, all switching times are 3 sampling periods, and other parameters are as described in table 1:
table 1 partial parameters of the propeller:
wherein KptIs a relation parameter of power and thrust.
(2) According to the parameter values in the step (1), the thrust distribution solution at the previous moment and the current demand force and moment, a mixed group optimization algorithm c (cost function comprises energy consumption, force and moment deviation and a singular structure) is obtained by applying the extreme solution in the figure 1 to obtain an optimal solution and an optimal sub-sector;
(3) determining a most suitable sector combination by adopting a dwell time switching technology and a hysteresis switching technology according to the optimal solution and the optimal sub-sector obtained in the step (2), wherein the specific judgment measures are as follows;
Ibestsearching the sub-sector combination of the optimal thrust distribution obtained by all the sub-sectors by using an 'extreme solution' mixed group optimization algorithm, InIs the optimum sub-sector combination in which the handover is not yet performed, IPIs referred to as InThe optimum sub-sector combination previously selected, t being IPSwitch to InTime until the present time, tdtsMinimum switching time, t, for dwell time switchinghThe maximum return switching time is set for preventing switching caused by transient interference, and the value of the maximum return switching time is not more than tdtsJP (-) is a cost function value that does not contain angle changes, JPlimIs the minimum change of the cost function to be limited during the lag switching, S (-) is the sum of the relaxation variables, SdtsThe maximum value of the sum and the change of the relaxation variables needing to be limited during the hysteresis switching is a percentage.
(4) And (3) according to the most suitable sub-sector combination in the step (3), determining the azimuth angle and the thrust of each propeller by using a 'pole-to-pole-solving' mixed group algorithm in the figure 1, and specifically comprising the following steps:
1) when I isn＝inowAnd Ibest＝inowWhen i isnowAnd for the current sector combination, solving by using a 'minimal solution' mixed group optimization algorithm a (cost function comprises energy consumption, force and moment deviation, azimuth angle change rate and singular structure), determining the azimuth angle of the final propeller according to the solved optimal angle solution and by combining with a global optimal angle solution, and finally solving the final thrust by using a 'minimal solution' mixed group optimization algorithm b (cost function comprises energy consumption, force and moment deviation) according to the solved azimuth angle.
2) When I isn＝inowAnd Ibest≠inowWhen is, or In≠inowAnd i isnowWhen the thruster is not in the forbidden area, the 'extreme solution' mixed group optimization algorithm a (cost functions comprise energy consumption, force and moment deviation, azimuth angle change rate and singular structures) is directly applied to solve the azimuth angle and thrust of the thruster.
3) In other cases, the azimuth angle of the propeller is firstly determined, and then the thrust magnitude of the propeller is solved by using a mixed group optimization algorithm b (cost function comprises energy consumption and force and moment deviation) under the condition that the azimuth angle is known.
(5) And (5) outputting the thrust distribution result of the step (4) and preparing the thrust distribution solution at the next sampling moment.
The invention takes a Cybership III ship model as an embodiment, and the thrust requirement of the embodiment is in the first 100 sampling periods: the x axis 1N is randomly generated left and right, the y axis is always 0.5N, the moment is 0, and the last 100 sampling periods are as follows: the x axis-1N is randomly generated, the y axis is randomly generated about 0.5N, and the moment is 0.
The results show that:
it can be seen from fig. 3 that in the first 100 using periods, because the y-axis has a fixed expected thrust, the ship bow propellers act, the two propellers at the stern can change synchronously with the change of the x-axis, and in the last 100 using periods, because the expected thrust at the y-axis changes randomly, the thrust of the two propellers at the stern does not change synchronously any more and the ship bow propellers do not have a constant value any more, but the thrust of all the propellers does not run away, and the change amplitude except the switching time process is smaller, which indicates that the algorithm has smaller energy consumption. As can be seen from fig. 4, the azimuth angle changes of the two propellers at the stern in the whole process are stable and do not change around +/-180 degrees (such as around the value when the energy consumption is minimum regardless of the singular structure), which shows that the algorithm can reduce the mechanical wear of the propellers and avoid the singular structure as much as possible.
In conclusion, the invention combines the algorithm with a supervision and switching thrust distribution mechanism on the basis of innovatively providing a mixed group optimization algorithm which can be obtained as much as possible, and the formed thrust distribution strategy can reduce the energy consumption of a ship system and simultaneously reduce the mechanical wear of a propeller and avoid a singular structure as much as possible.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (4)
1. A ship thrust distribution method of a mixed group optimization algorithm is characterized by comprising the following steps: the method comprises the following steps:
(1) initializing various parameters according to the actual condition of the ship object;
(2) obtaining an optimal solution and an optimal sub-sector by applying a 'best-effort' mixed group algorithm according to the parameter values in the step (1), the thrust distribution solution at the previous moment and the current demand force and moment;
(3) determining a most suitable sector combination by adopting a dwell time switching technology and a hysteresis switching technology according to the optimal solution and the optimal sub-sector obtained in the step (2);
(4) determining the azimuth angle and the thrust of each propeller by using a 'best-effort' mixed group algorithm according to the most suitable sub-sector combination selected in the step (3);
(5) outputting the thrust distribution result of the step (4) and preparing the thrust distribution solution at the next sampling moment;
wherein, the 'extreme best solution' mixed group algorithm related in the step (2) and the step (4) is implemented by the following specific steps:
1) and initializing an algorithm, comprising: population initialization, individual X ═ X1，X2，…，XNCalculating and recording the current individual optimal value and the position thereof, the global optimal value and the individual thereof;
2) performing population evolution and individual updating by adopting a bidirectional optimization and exhaustive solving method;
3) selecting p1 optimal individuals for local optimization, specifically as follows:
Xi(t+1)＝Xi(t)+rand()×visual1
in order to improve the local optimization capacity, the value of visual1 in the formula cannot be too large, each individual updates V1 filial generations derived on the basis of a parent generation, and updates individual values and global optimal values;
4) selecting p2 worst individuals for local optimization, specifically as follows:
Xi(t+1)＝Xi(t)+rand()×visual2
in order to enable the individuals to jump out of the current unfavorable positions, the value of visual2 in the formula cannot be too small, each individual updates V2 filial generations derived on the basis of a parent generation, and updates individual values and global optimal values;
5) and if the iteration times reach the set value, ending the process, otherwise, executing the step 2).
2. The ship thrust allocation method of the mixed group optimization algorithm according to claim 1, wherein: the bidirectional optimizing and exhaustive solving method in the step 2) comprises the following steps:
let each individual optimal solution be denoted as PibestThe global optimal solution is denoted as Pgbest；
a. Each individual update is as follows:
Xi(t+1)＝Xi(t)+ΔXi(t)
wherein
i≠j，sl1，sl2Is the moving step length;
calculating individual adaptive values and judging whether optimization is obtained, if so, updating the individual optimal solution and the individual optimal adaptive value, and executing step c, otherwise, executing step b;
b. each individual update is as follows:
Xi(t+1)＝Xi(t)-ΔXi(t)
calculating individual adaptive values and judging whether optimization is obtained, if so, updating the individual optimal solution and the individual optimal adaptive value, and executing d, otherwise, executing e;
c. each individual update is as follows:
Xi(t+1)＝Xi(t)+cpiΔXi(t)，cpi＞1
calculating individual adaptive value and judging whether the optimization is obtained, if so, updating the individual optimal solution and the individual optimal adaptive value, and updating cpiC is executed, otherwise g is executed;
cpi＝cpi(1+c1×rand())
d. each individual update is as follows:
Xi(t+1)＝Xi(t)-cniΔXi(t)，cni＞1
calculating individual adaptive value and judging whether the optimization is obtained, if so, updating the individual optimal solution and the individual optimal adaptive value, and updating cniExecuting d, otherwise executing g;
cni＝cni(1+c2×rand())
e. if the number of times of the attempt reaches a set value m, executing f if the number of times of the attempt reaches the set value m, otherwise executing a;
f. after the m times of trial, if the individual fitness value is not improved and is not the global optimum, executing a random operator, and if the individual fitness value is the global optimum, keeping the current individual value;
g. and (5) finishing the evolution of the individuals, and updating the global optimal value.
3. The ship thrust allocation method of the mixed group optimization algorithm according to claim 1, wherein: the specific judgment measure for how to switch the combination of the sectors in the step (3) is as follows:
Ibestsearching the sub-sector combination of the optimal thrust distribution obtained by all the sub-sectors by using an 'extreme solution' mixed group optimization algorithm, InIs the optimum sub-sector combination in which the handover is not yet performed, IPIs referred to as InThe optimum sub-sector combination previously selected, t being IPSwitch to InTime until the present time, tdtsMinimum switching time, t, for dwell time switchinghThe maximum return switching time is set for preventing switching caused by transient interference, and the value of the maximum return switching time is not more than tdtsJP (-) is a cost function value that does not contain angle changes, JPlimIs the minimum change of the cost function to be limited during the lag switching, S (-) is the sum of the relaxation variables, SdtsThe maximum value of the sum and the change of the relaxation variables needing to be limited during the hysteresis switching is a percentage.
4. The ship thrust allocation method of the mixed group optimization algorithm according to claim 1, wherein: the calculation steps of the azimuth angle and the thrust of each propeller in the step (4) are as follows:
1) when I isn＝inowAnd Ibest＝inowWhen i isnowFor the current sector combination, a mixed group algorithm a of 'extreme solution' is used for solving, the cost function of the algorithm a comprises energy consumption, force and moment deviation, azimuth angle change rate and singular structure, the azimuth angle of the final propeller is determined according to the solved optimal angle solution and by combining with the global optimal angle solution, and finally 'extreme solution' is used according to the solved azimuth angle "Obtaining the final thrust by a mixed group algorithm;
2) when I isn＝inowAnd Ibest≠inowWhen is, or In≠inowAnd i isnowWhen the thruster is not in the forbidden area, the azimuth angle and the thrust of the thruster are solved by directly using a mixed group optimization algorithm a of 'best-effort' and the cost function of the algorithm a comprises energy consumption, force and moment deviation, azimuth angle change rate and a singular structure;
3) in other cases, firstly, the azimuth angle of the propeller is determined, then under the condition that the azimuth angle is known, the thrust of the propeller is solved by using a 'extreme solution' mixed group algorithm b, and the cost function of the algorithm b comprises energy consumption, force and moment deviation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011478093.0A CN112506060B (en) | 2020-12-15 | 2020-12-15 | Ship thrust distribution method based on mixed group optimization algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011478093.0A CN112506060B (en) | 2020-12-15 | 2020-12-15 | Ship thrust distribution method based on mixed group optimization algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112506060A CN112506060A (en) | 2021-03-16 |
| CN112506060B true CN112506060B (en) | 2021-11-02 |
Family
ID=74973714
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011478093.0A Active CN112506060B (en) | 2020-12-15 | 2020-12-15 | Ship thrust distribution method based on mixed group optimization algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112506060B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109683479B (en) * | 2018-12-26 | 2021-07-06 | 上海交通大学 | Dynamic positioning thrust distribution device and method based on artificial neural network |
| CN114815590B (en) * | 2022-04-22 | 2024-06-18 | 中国科学院沈阳自动化研究所 | Thrust distribution method of underwater robot based on optimized energy consumption |
| CN116300426B (en) * | 2023-05-10 | 2023-11-14 | 南通大学 | Thrust distribution method with supervision mechanism |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103129729A (en) * | 2013-01-28 | 2013-06-05 | 中交天津航道局有限公司 | Thrust distribution optimizing method for power positioning system of dredge boat |
| CN106773722A (en) * | 2017-02-17 | 2017-05-31 | 哈尔滨工程大学 | A kind of thrust force distribution method of power location system of ship based on artificial fish-swarm algorithm |
| CN108490770A (en) * | 2018-02-28 | 2018-09-04 | 哈尔滨工程大学 | A kind of thrust force distribution method of power location system of ship based on hybrid algorithm |
| CN108594651A (en) * | 2018-03-16 | 2018-09-28 | 上海交通大学 | A kind of dynamic positioning of vessels thrust distribution intelligent optimization method |
| CN108845576A (en) * | 2018-06-28 | 2018-11-20 | 中国船舶重工集团公司第七0七研究所 | A kind of thrust distribution method based on population in conjunction with sequential quadratic programming |
| CN111324037A (en) * | 2020-02-28 | 2020-06-23 | 上海交通大学 | Thrust allocation method based on combination of genetic algorithm and simulated annealing algorithm |
| CN111812976A (en) * | 2020-06-06 | 2020-10-23 | 智慧航海(青岛)智能***工程有限公司 | Ship thrust distribution system and thrust distribution method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4339016B2 (en) * | 2002-05-20 | 2009-10-07 | 川崎重工業株式会社 | Thrust distribution method and thrust distribution apparatus |
-
2020
- 2020-12-15 CN CN202011478093.0A patent/CN112506060B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103129729A (en) * | 2013-01-28 | 2013-06-05 | 中交天津航道局有限公司 | Thrust distribution optimizing method for power positioning system of dredge boat |
| CN106773722A (en) * | 2017-02-17 | 2017-05-31 | 哈尔滨工程大学 | A kind of thrust force distribution method of power location system of ship based on artificial fish-swarm algorithm |
| CN108490770A (en) * | 2018-02-28 | 2018-09-04 | 哈尔滨工程大学 | A kind of thrust force distribution method of power location system of ship based on hybrid algorithm |
| CN108594651A (en) * | 2018-03-16 | 2018-09-28 | 上海交通大学 | A kind of dynamic positioning of vessels thrust distribution intelligent optimization method |
| CN108845576A (en) * | 2018-06-28 | 2018-11-20 | 中国船舶重工集团公司第七0七研究所 | A kind of thrust distribution method based on population in conjunction with sequential quadratic programming |
| CN111324037A (en) * | 2020-02-28 | 2020-06-23 | 上海交通大学 | Thrust allocation method based on combination of genetic algorithm and simulated annealing algorithm |
| CN111812976A (en) * | 2020-06-06 | 2020-10-23 | 智慧航海(青岛)智能***工程有限公司 | Ship thrust distribution system and thrust distribution method |
Non-Patent Citations (3)
| Title |
|---|
| The_optimal_thrust_allocation_based_on_QPSO_algorithm_for_dynamic_positioning_vessels;Ji, Ming 等;《IEEE》;20141231;全文 * |
| 动力定位船舶伪逆法与混沌粒子群法相融合的推力分配算法研究;刘明 等;《海洋工程》;20161231;第34卷(第4期);全文 * |
| 基于OIPSO算法的环境最优动力定位控制器设计;刘明 等;《船舶工程》;20151231;第37卷(第6期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112506060A (en) | 2021-03-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112506060B (en) | Ship thrust distribution method based on mixed group optimization algorithm | |
| CN108255062B (en) | Power positioning energy-saving thrust distribution method based on improved differential evolution mechanism | |
| CN111400864B (en) | Ship collision avoidance decision optimization method based on prospect theory | |
| CN113485121B (en) | Distributed multi-ship cooperative power positioning control method | |
| CN108572550A (en) | A kind of online real-time thrust distribution method based on machine learning | |
| CN111812976A (en) | Ship thrust distribution system and thrust distribution method | |
| Perez et al. | Model predictive rudder roll stabilization control for ships | |
| CN116859728A (en) | Ship dynamic positioning system thrust distribution method based on improved multi-target particle swarm algorithm | |
| Oyama et al. | Real-coded adaptive range genetic algorithm and its application to aerodynamic design | |
| CN113591206B (en) | Ship shape optimization design method and system based on geometric deformation spatial characteristics | |
| Mohamed-Seghir | The branch-and-bound method and genetic algorithm in avoidance of ships collisions in fuzzy environment | |
| CN116009554A (en) | Ship thrust distribution method based on particle swarm optimization | |
| CN113126503B (en) | Intelligent ship thrust distribution method considering propeller faults | |
| Ding et al. | Thrust allocation of dynamic positioning based on improved differential evolution algorithm | |
| CN110472333B (en) | Automatic balancing method for two-dimensional airfoil numerical simulation | |
| CN109754108A (en) | Unit Economic load distribution method based on fluctuating acceleration coefficient Chaos-Particle Swarm Optimization | |
| CN114019985A (en) | Unmanned ship rudder direction control design method based on fractional order PID and particle swarm algorithm | |
| Rousselon | Optimization for sail design | |
| Xia et al. | Research on Thrust Allocation Optimization with Main Propeller-rudder Based on Improved Genetic Algorithm | |
| Stone et al. | Ship hull structural scantling optimization | |
| CN113075884B (en) | Thrust distribution method based on adaptive genetic-least square interconnection prediction system | |
| Parsons et al. | Michigan Conceptual Ship Design Software Environment-User's Manuals | |
| CN112001033B (en) | Bionic crab airfoil optimization design method based on combined CST algorithm | |
| CN116929367A (en) | Thrust distribution method and device based on neural network algorithm multi-step planning | |
| Suyama | A Comparison of Design of Reward Function in Ship Trajectory Tracking with Reinforcement Learning Framework |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |