Aircraft task planning calculation method based on improved NSGA-II algorithm

A technology for mission planning and spacecraft, applied in the field of spacecraft on-orbit service research, can solve problems such as slow convergence speed, difficult large-scale tasks, and difficulty in comprehensively considering spacecraft loss, so as to narrow the search area, reduce infeasible solutions, good portability

Active Publication Date: 2015-12-16
DALIAN UNIV
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Problems solved by technology

[0004] However, the existing method of transforming the multi-objective problem of mission planning into a more mature single-objective problem for processing is difficult to comprehensively consider the loss of the spacecraft itself during the entire service process; while using "0-1" integer programming and basic The convergence speed of the NSGA-II algorithm is relatively slow, and it is difficult to apply to large-scale tasks

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  • Aircraft task planning calculation method based on improved NSGA-II algorithm
  • Aircraft task planning calculation method based on improved NSGA-II algorithm
  • Aircraft task planning calculation method based on improved NSGA-II algorithm

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Embodiment 1

[0029] The concrete implementation steps of the present invention:

[0030] Step 1. Randomly initialize a population PO of size N.

[0031] Step 2. Initialize the initial value of each individual, perform non-dominated sorting on P0 based on the initial value of each individual, and generate a non-dominated set P t . The initial value is: first calculate the objective function value of the population individuals that meet the constraint conditions, and then calculate the crowding degree according to the objective function value as the virtual fitness value. The initial value refers to the calculated virtual fitness value.

[0032] Step 3. From P by the binary tournament method t Individuals are selected, and the arithmetic crossover operator and Gaussian mutation genetic operator are used to generate a new generation of population Q t .

[0033] Step 4. Calculate all fitness function values ​​of individuals in the new population.

[0034] Step 5. By merging P t and Q t ...

Embodiment 2

[0066] The specific implementation steps are:

[0067] First set the spacecraft physical parameter data. The main parameters of the spacecraft are set as follows:

[0068] T=5000S;t 0 = 100s; Δt min = 100s; Δv min =3km / s

[0069] The maximum number of iterations is 200.

[0070] The track root number and priority settings are shown in Table 1:

[0071] Table 1 Number of tracks and priority settings

[0072]

[0073] In step 1, it is assumed that there are N serving spacecraft deployed in orbit, and after receiving a service instruction at a certain moment, they serve M target spacecraft with different task priorities, that is, the system consisting of N serving spacecraft The formation serves M different targets. According to the characteristics of the planning problem, the decision variables can be defined as follows:

[0074]

[0075] The present invention adopts natural number coding, and the key to the task planning problem of the service spacecraft is to de...

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Abstract

The invention takes task complete time, fuel consumption and service priority as in-orbit service aircraft task scheduling optimization targets, establishes a mathematic model concerning to a multi-aircraft-task planning problem through designing decision variables and formalized constrain conditions, utilizes the improved NSGA-II algorithm for obtaining a Pareto optimal set of the problem and chooses a compromise scheme in the obtained multiple solution sets according to practical preference. According to the invention, the Pareto optimization set of the scheme can be obtained and then the optimal scheme can be selected when task time constraint, orbital transfer and maximum speed incremental constraint are met, so that the task optimization convergence speed is high and is more suitable for larger task models. The task planning problem is converted to a corresponding optimization problem for solving. The formed multi-target optimization algorithm is adopted and a condition that combined action of multiple targets cannot be taken into consideration comprehensively when the multi-target problem is converted to the single-target problem is relieved.

Description

technical field [0001] The invention belongs to the research field of spacecraft on-orbit service, especially the task planning problem of on-orbit service spacecraft. Background technique [0002] With the development and research of space. With the deepening of applications, human beings have higher and higher requirements for space exploration, the structure and composition of spacecraft are becoming more and more complex, and the performance and technical level are constantly improving, which is also a test for the mission execution ability of spacecraft. This not only makes the time and fuel of the spacecraft in-orbit service very precious, but also the planning and scheduling of the spacecraft is very complicated. Space on-orbit service was originally proposed for the one-time design and closed system of traditional spacecraft, but with the development of aerospace technology, on-orbit service also involves physical evaluation and behaviors that change the current sta...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06G06N3/00
Inventor 张强张建新魏小鹏张青
Owner DALIAN UNIV
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