CN110794856A - Wave compensation platform control method based on improved particle swarm optimization algorithm - Google Patents
Wave compensation platform control method based on improved particle swarm optimization algorithm Download PDFInfo
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Abstract
The invention provides a wave compensation platform control method based on an improved particle swarm optimization algorithm, which comprises the following steps: step 1, obtaining a dynamics and kinematics model of a wave compensation platform according to a mechanism modeling method; step 2, initializing the particle swarm scale; step 3, PiRecording the search optimum position, P, of the ith particlegRecording the position of the optimal particle in the current population; step 4, updating the particle swarm according to the improved speed updating formula and the position updating formula; step 5, judging whether the iteration times are reached, and outputting P if the iteration times are reachedgOtherwise, returning to the step 4. Compared with a genetic algorithm with mature technology, the method has the advantages of simple parameter design and easy algorithm realization. And the defects that the traditional particle swarm algorithm is low in convergence precision and easy to get early are overcome.
Description
Technical Field
The invention relates to the technical field of heave compensation control, in particular to a heave compensation platform control method based on an improved particle swarm optimization algorithm.
Background
When a ship executes a work task in the sea, the irregular fluctuation of sea waves can cause the ship to present complex motions such as rolling motion, pitching motion and heaving motion, thereby influencing the operation safety of the ship and shipborne equipment. In the case of high wind and sea conditions, cargo lifting between ships causes ship collision, violent impact of cargo on the ship deck, and in extreme cases, wire rope breakage, which causes irreparable damage to workers, cargo and ships, and this safety event has a great impact on the safety of marine transportation and operations. In view of the above safety problems, it is necessary to take a powerful compensation measure for the marine vessel, so as to minimize the influence of wind and waves on the working vessel. The wave compensation platform can effectively compensate rolling motion, pitching motion and heave motion of ships, can improve the safety of ships and shipborne equipment, and improves the work efficiency of ships and shipborne equipment.
Among the numerous optimization algorithms, genetic algorithms mature with respect to technology such as chinese published patent No.: CN201610624618, a brand-new particle swarm optimization algorithm, simple parameter design, and easy implementation. And the traditional particle swarm optimization algorithm is as disclosed in Chinese patent No.: CN201711153618 has the disadvantages of low convergence accuracy and easy precocity. Improving particle swarm optimization algorithms solves these problems well.
Particle Swarm Optimization (PSO), a cluster Optimization algorithm proposed by Eberhart and Kennedy in 1995. The basic concept of the method is derived from the research on the bee-brood phenomena (bird swarms, ant swarms, fish swarms and the like) in real life, the most adopted model in the bee-brood elaboration research is a classic Boids model which is proposed by Reynolds in 1986 and is used for simulating the behavior of the bee-brood swarms by a computer, and each individual of the swarms in the model meets three basic action rules, namely (1) Separation: avoiding collisions with individuals within a neighborhood; (2) polymerization (Cohesion): remain compact with individuals in the neighborhood; (3) fitness match (Alignment): consistent with the individual velocities in the neighborhood.
Disclosure of Invention
The invention aims to provide a wave compensation platform control method based on an improved particle swarm optimization algorithm, so as to realize control over a wave compensation platform.
The invention scheme for realizing the purpose is as follows: a wave compensation platform control method based on an improved particle swarm optimization algorithm comprises the following steps:
step 1: the dynamics and kinematics model of the wave compensation platform is obtained according to a mechanism modeling method, and the motion, transformation and mapping can be converted into simple matrix operation on the basis of transforming three-dimensional space coordinates by a fourth-order matrix through a matrix method, so that the displacement, the speed and the acceleration of each part can be represented.
Step 2: initializing a particle swarm size, wherein the particle swarm size is m, each particle has a search area with n dimensions, xi＝(xi1，xi2，xi3，…，xin) For the search position in space, v, of the ith particlei＝(vi1，vi2，vi3，…，vin) Is the velocity of the ith particle.
And step 3: step 3, PiRecording the search optimum position, P, of the ith particlegAnd recording the position of the optimal particle in the current population.
And 4, step 4: and updating the particle swarm according to the improved speed updating formula and the position updating formula. The particle velocity updating method based on the negative gradient idea in the gradient descent algorithm obtains a new particle velocity updating formula:
vij(t+1)＝aR1vij(t)-bR2(fpi(t)-fi(t))(Pij(t)-xij(t))-cR3(fgi(t)-fi(t))(Pgj(t)-xij(t))
in the formula: a. b and c are new learning factors, and the ranges are 0 < a < 0.01,0 < b < 1, and 0 < c < 0.02 respectively. f. ofi(t) is the optimal fitness value of the ith particle in the current iteration, fpi(t) is the fitness value of the ith particle at the t-th iteration, fgiAnd (t) is the optimal fitness value of the population at the t iteration of the population.
In the particle position updating algorithm, the space range of the solution becomes smaller along with the increase of the searching time, and in order to obtain an accurate solution, the searching iteration step length of the particle is required to be correspondingGround is reduced, and adaptive adjustment coefficients based on an arc tangent function are added in the algorithm
Wherein: and t is the iteration number.
The improved particle position updating formula is as follows: x is the number ofij(t+1)＝η×(xij(t)+vij(t))。
And 5: judging whether the number of iterations is reached, and outputting P if the number of iterations is reachedgOtherwise, returning to the step 4.
Compared with the prior art, the invention has the following advantages: compared with a genetic algorithm with mature technology, the method has the advantages of simple parameter design and easy algorithm realization. And the defects that the traditional particle swarm algorithm is low in convergence precision and easy to get early are overcome.
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FIG. 1: the invention discloses a flow chart of an improved particle swarm optimization algorithm
FIG. 2: schematic diagram of control system
Detailed Description
A wave compensation control method based on an improved particle swarm optimization algorithm needs to control three attitude angles, namely a bow angle, a pitch angle and a roll angle, of a wave compensation platform. And measuring the roll value, the pitch value and the heave value of the ship by using a sensor arranged in the middle of the lower table top of the wave compensation platform. The electro-hydraulic servo valve controls the valve core to move according to the obtained analog electric signals of the ship rolling, pitching and heave compensation values so as to output hydraulic oil with corresponding flow to control the driving rod to do telescopic motion, and real-time compensation of the ship rolling, pitching and heave is realized through the method, and a control system is shown in figure 2. The method specifically comprises the following steps:
step 1: the dynamics and kinematics model of the wave compensation platform is obtained according to a mechanism modeling method, and the motion, transformation and mapping can be converted into simple matrix operation on the basis of transforming three-dimensional space coordinates by a fourth-order matrix through a matrix method, so that the displacement, the speed and the acceleration of each part can be represented.
Step 2: initializing a particle swarm size, wherein the particle swarm size is m, each particle has a search area with n dimensions, xi＝(xi1，xi2，xi3，…，xin) For the search position in space, v, of the ith particlei＝(vi1，vi2，vi3，…，vin) Is the velocity of the ith particle.
And step 3: step 3, PiRecording the search optimum position, P, of the ith particlegAnd recording the position of the optimal particle in the current population.
And 4, step 4: and updating the particle swarm according to the improved speed updating formula and the position updating formula. The particle velocity updating method based on the negative gradient idea in the gradient descent algorithm obtains a new particle velocity updating formula:
vij(t+1)＝aR1vij(t)-bR2(fpi(t)-fi(t))(Pij(t)-xij(t))-cR3(fgi(t)-fi(t))(Pgj(t)-xij(t))
in the formula: a. b and c are new learning factors, and the ranges are 0 < a < 0.01,0 < b < 1, and 0 < c < 0.02 respectively. f. ofi(t) is the optimal fitness value of the ith particle in the current iteration, fpi(t) is the fitness value of the ith particle at the t-th iteration, fgiAnd (t) is the optimal fitness value of the population at the t iteration of the population.
In the particle position updating algorithm, the spatial range of the solution becomes smaller along with the increase of the search time, and in order to obtain an accurate solution, the search iteration step length of the particle is correspondingly reduced, and an adaptive adjustment coefficient based on an arctangent function is added in the algorithm
Wherein: t is the number of iterationsAnd (4) counting.
The improved particle position updating formula is as follows: x is the number ofij(t+1)＝η×(xij(t)+vij(t))。
And 5: judging whether the number of iterations is reached, and outputting P if the number of iterations is reachedgOtherwise, returning to the step 4.
The above is a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered as the following claims in the light of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (1)
1. A wave compensation platform control method based on an improved particle swarm optimization algorithm is characterized by comprising the following steps:
step 1: the dynamics and kinematics model of the wave compensation platform is obtained according to a mechanism modeling method, and the motion, transformation and mapping can be converted into simple matrix operation on the basis of transforming three-dimensional space coordinates by a fourth-order matrix through a matrix method, so that the displacement, the speed and the acceleration of each part can be represented;
step 2: initializing a particle swarm size, wherein the particle swarm size is m, each particle has a search area with n dimensions, xi＝(xi1,xi2,xi3,…,xin) For the search position in space, v, of the ith particlei＝(vi1,vi2,vi3,…,vin) Is the velocity of the ith particle;
and step 3: step 3, PiRecording the search optimum position, P, of the ith particlegRecording the position of the optimal particle in the current population;
and 4, step 4: updating the particle swarm according to an improved speed updating formula and a position updating formula, wherein a new particle speed updating formula is obtained based on a particle speed updating method of a negative gradient thought in a gradient descent algorithm:
vij(t+1)＝aR1vij(t)-bR2(fpi(t)-fi(t))(Pij(t)-xij(t))-cR3(fgi(t)-fi(t))(Pgj(t)-xij(t))
in the formula: a. b and c are new learning factors, and their ranges are 0 & lt a & lt 0.01,0 & lt b & lt 1,0 & lt c & lt 0.02, fi(t) is the optimal fitness value of the ith particle in the current iteration, fpi(t) is the fitness value of the ith particle at the t-th iteration, fgi(t) is the population optimal fitness value at the t iteration of the population,
in the particle position updating algorithm, the spatial range of the solution becomes smaller along with the increase of the search time, and in order to obtain an accurate solution, the search iteration step length of the particle is correspondingly reduced, and an adaptive adjustment coefficient based on an arctangent function is added in the algorithm
Wherein: t is the number of iterations,
the improved particle position updating formula is as follows: x is the number ofij(t+1)＝η×(xij(t)+vij(t))；
And 5: judging whether the number of iterations is reached, and outputting P if the number of iterations is reachedgOtherwise, returning to the step 4.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111649457A (en) * | 2020-05-13 | 2020-09-11 | 中国科学院广州能源研究所 | Dynamic predictive machine learning type air conditioner energy-saving control method |
| CN112214031A (en) * | 2020-09-25 | 2021-01-12 | 北京理工大学 | Multi-node collaborative landing position planning method based on genetic particle swarm optimization |
| CN113879982A (en) * | 2020-07-02 | 2022-01-04 | 云南力神重工机械有限公司 | Multi-hoisting-point trolley |
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2019
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111649457A (en) * | 2020-05-13 | 2020-09-11 | 中国科学院广州能源研究所 | Dynamic predictive machine learning type air conditioner energy-saving control method |
| CN113879982A (en) * | 2020-07-02 | 2022-01-04 | 云南力神重工机械有限公司 | Multi-hoisting-point trolley |
| CN112214031A (en) * | 2020-09-25 | 2021-01-12 | 北京理工大学 | Multi-node collaborative landing position planning method based on genetic particle swarm optimization |
| CN112214031B (en) * | 2020-09-25 | 2021-08-20 | 北京理工大学 | Multi-node collaborative landing position planning method based on genetic particle swarm optimization |
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