Multi-aircraft-multi-target task allocation method for minimizing arrival time variance

Abstract

The invention discloses a multi-aircraft-multi-target task allocation method for minimizing an arrival time variance, and the method comprises the following steps: building an optimization target function which is used for representing the target hitting synchronism of aircrafts; determining a guidance strategy, and obtaining the remaining flight time of the aircraft based on the guidance strategy; and carrying out target allocation by adopting a genetic algorithm. The method disclosed by the invention is strong in global search capability, strong in adaptability to complex constraints and good in processing capability to large-scale problems.

Description

Technical Field

[0001] The invention relates to a multi-aircraft-multi-objective task allocation method for minimizing arrival time variance, and belongs to the technical field of flight data control. Background Art

[0002] In the problem of coordinated guidance when multiple aircraft attack a target group at the same time, if the difference in the aircraft's strike time is too large, the targets that have not been hit will have enough reaction time to dodge, which will have a great impact on the coordinated strike effect. In order to control the difference in aircraft strike time, target allocation can be performed based on the variance of aircraft strike time.

[0003] Different from the traditional target allocation problem, the allocation problem with the aircraft strike time variance as the optimization target is not only nonlinear in function form, but also requires global information in each calculation of the optimization function value. Target allocation methods can be divided into exact algorithms and heuristic algorithms. Traditional exact target allocation methods, such as linear programming algorithms and Hungarian algorithms, cannot solve the nonlinear problem of time variance; improved exact algorithms, such as branch-and-bound algorithms, can solve nonlinear allocation problems, but this method can only deal with the situation where only local information is needed to update the objective function.

[0004] Therefore, it is necessary to study the existing target allocation problem to solve the above problems. Summary of the invention

[0005] In order to overcome the above problems, an in-depth study was conducted and a multi-aircraft-multi-objective task allocation method for minimizing the arrival time variance was proposed, which is characterized by comprising the following steps:

[0006] S1. Establishing an optimization objective function to characterize the synchronization of the aircraft striking the target;

[0007] S2. determining a guidance strategy, and obtaining a remaining flight time of the aircraft based on the guidance strategy;

[0008] S3. Use genetic algorithm for target allocation.

[0009] In a preferred embodiment, in S1, the optimization objective function is set to:

[0010]

[0011] Where minimize means minimize, the subscript i represents the i-th aircraft, n represents the total number of aircraft, and t go,i represents the remaining flight time of the i-th aircraft, Represents the average remaining flight time of all aircraft.

[0012] In a preferred embodiment, in S2, the guidance strategy adopts a proportional guidance law, which is expressed as:

[0013]

[0014] in, represents the normal acceleration command of the i-th aircraft, N is the navigation parameter, V c,i is the relative speed between the ith aircraft and the ith target, is the line-of-sight angular velocity between the ith aircraft and the ith target.

[0015] In a preferred embodiment, the remaining flight time is obtained as:

[0016]

[0017] Among them, r i represents the distance between the ith aircraft and the ith target, σ i represents the track angle between the ith aircraft and the ith target.

[0018] The beneficial effects of the present invention include:

[0019] (1) Strong global search capability, strong adaptability to complex constraints, and good ability to handle large-scale problems;

[0020] (2) It has good parallelism, global optimization capability and certain adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic flow chart of a multi-aircraft-multi-objective task allocation method for minimizing arrival time variance according to a preferred embodiment of the present invention is shown;

[0022] Figure 2 The mean square error results of the estimated remaining flight time and the actual flight time in Example 1 are shown;

[0023] Figure 3 The minimum variance iteration curve of the genetic algorithm in Example 1 is shown;

[0024] Figure 4 The motion trajectory of the aircraft under the final optimal allocation in Example 1 is shown. DETAILED DESCRIPTION

[0025] The present invention will be further described in detail below through the accompanying drawings and embodiments. Through these descriptions, the characteristics and advantages of the present invention will become more clear and distinct.

[0026] The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise noted.

[0027] A multi-aircraft-multi-objective task allocation method for minimizing arrival time variance provided by the present invention comprises the following steps:

[0028] S1. Establishing an optimization objective function to characterize the synchronization of the aircraft striking the target;

[0029] S2. determining a guidance strategy, and obtaining a remaining flight time of the aircraft based on the guidance strategy;

[0030] S3. Use genetic algorithm for target allocation.

[0031] In S1, the optimization objective function is set to:

[0032]

[0033] Where minimize means minimize, the subscript i represents the i-th aircraft, n represents the total number of aircraft, and t go,i represents the remaining flight time of the i-th aircraft, Represents the average remaining flight time of all aircraft.

[0034] The above optimization objective function aims to minimize the variance between the strike times of multiple aircraft on different targets, thereby minimizing the differences in the flight times of different aircraft and achieving accurate measurement of the synchronization of aircraft strikes on targets.

[0035] According to the present invention, by optimizing the objective function, a corresponding relationship between different aircraft and different targets can be established, so that each target corresponds to an aircraft.

[0036] In S2, the guidance strategy can adopt any known guidance strategy, preferably the proportional guidance law. The proportional guidance law only needs to calculate the line-of-sight angular velocity between the target and the missile, which is easy to implement in the actual system and has the advantages of strong adaptability, high hit rate, and mature engineering practice. It is more suitable for the guidance of multiple aircraft.

[0037] The proportional guidance law is expressed as:

[0038]

[0039] in, represents the normal acceleration command of the i-th aircraft, N is the navigation parameter, V c,iis the relative speed between the ith aircraft and the ith target, is the line-of-sight angular velocity between the ith aircraft and the ith target.

[0040] Based on the above guidance strategy, the remaining flight time can be obtained as:

[0041]

[0042] Among them, r i represents the distance between the ith aircraft and the ith target, σ i represents the track angle between the ith aircraft and the ith target.

[0043] In S3, specific targets are matched with target numbers to achieve target allocation.

[0044] The genetic algorithm is an algorithm that simulates the survival of the fittest process in nature. The specific process is not described in detail in the present invention. Its characteristics are that it can directly modify structural objects without being restricted by functions; it has good parallelism, global optimization ability and certain self-adaptation ability. Compared with traditional allocation algorithms, genetic algorithms process multiple individuals at the same time, reducing the possibility of falling into local optimality, and can be applied to objective functions that rely on global variable updates.

[0045] In the present invention, a genetic algorithm is used for target allocation, which can be used for the case where the optimization function is the variance of the aircraft flight time. Global information can be provided in each update of the objective function to complete the calculation of the variance. Compared with the traditional allocation algorithm, it can process multiple solutions at the same time, reducing the risk of falling into a local optimal solution.

[0046] Example

[0047] Example 1

[0048] Conducting a target task allocation simulation experiment includes the following steps:

[0049] S1. Establishing an optimization objective function to characterize the synchronization of the aircraft striking the target;

[0050] S2. determining a guidance strategy, and obtaining a remaining flight time of the aircraft based on the guidance strategy;

[0051] S3. Use genetic algorithm for target allocation.

[0052] In S1, the optimization objective function is set to:

[0053]

[0054] In S2, the guidance strategy adopts the proportional guidance law, which is expressed as:

[0055]

[0056] Based on the above guidance strategy, the remaining flight time can be obtained as:

[0057]

[0058] In S3, a genetic algorithm is used for target allocation.

[0059] In the simulation, the flight trajectory simulation step size is set to a fixed step size of 0.01s, and the initial conditions are set to:

[0060]

[0061] During the simulation, the mean square error between the estimated remaining flight time and the actual flight time is as follows: Figure 2 As shown, the minimum variance iteration curve of the genetic algorithm is as follows Figure 3 As shown, from Figure 3 It can be seen that the genetic algorithm obtained the minimum variance value of the remaining flight time of 0.52s after the early iteration. 2 , the trajectory of the aircraft under the final optimal allocation is as follows Figure 4 shown.

[0062] The present invention has been described above in conjunction with preferred embodiments, but these embodiments are only exemplary and serve only as an illustration. On this basis, the present invention may be subjected to a variety of substitutions and improvements, all of which fall within the scope of protection of the present invention.

Claims

1. A multi-aircraft-multi-objective task allocation method for minimizing arrival time variance, characterized in that: The following steps are involved: S1. Establishing an optimization objective function to characterize the synchronization of the aircraft striking the target; S2. determining a guidance strategy, and obtaining a remaining flight time of the aircraft based on the guidance strategy; S3. Use genetic algorithm for target allocation.

2. The multi-aircraft-multi-objective task allocation method for minimizing arrival time variance according to claim 1, characterized in that: In S1, the optimization objective function is set to: Where minimize means minimization, the subscript i represents the i-th aircraft, n represents the total number of aircraft, and t go,i represents the remaining flight time of the i-th aircraft, Represents the average remaining flight time of all aircraft.

3. The multi-aircraft-multi-objective task allocation method for minimizing arrival time variance according to claim 1, characterized in that: In S2, the guidance strategy adopts the proportional guidance law, which is expressed as: in, represents the normal acceleration command of the i-th aircraft, N is the navigation parameter, V c,i is the relative speed between the ith aircraft and the ith target, is the line-of-sight angular velocity between the ith aircraft and the ith target.

4. The multi-aircraft-multi-objective task allocation method for minimizing arrival time variance according to claim 3 is characterized in that: The remaining flight time is: Among them, r i represents the distance between the ith aircraft and the ith target, σ i represents the track angle between the ith aircraft and the ith target.

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