Multi-aircraft-multi-target task allocation method with optimal comprehensive energy consumption

Abstract

The invention discloses a multi-aircraft-multi-target task allocation method with optimal comprehensive energy consumption, and the method comprises the following steps: building an optimization target function which is used for representing the target striking synchronism of aircrafts; a guidance law is set, so that the aircraft can accurately hit a target under any air resistance; and performing target allocation by adopting a Hungary algorithm. According to the method disclosed by the invention, the influence of energy loss and resistance on the flight speed is considered, and the selection of the flight path is optimized by comprehensively considering the interaction effect between the target and the aircraft in the multi-aircraft-target distribution problem.

Description

Technical Field

[0001] The invention relates to a multi-aircraft-multi-objective task allocation method with optimal comprehensive energy consumption, belonging to the technical field of flight data control. Background Art

[0002] In a multi-aircraft coordinated strike mission, the energy consumption and air resistance of the aircraft have a significant impact on flight efficiency. If the aircraft consumes too much energy or is significantly affected by air resistance when performing a mission, its flight speed will be slowed down, resulting in a longer strike time or even failure to complete the mission on time.

[0003] Therefore, it is necessary to study the existing target allocation problem to solve the above problems. Summary of the invention

[0004] In order to overcome the above problems, an in-depth study was conducted and a multi-aircraft-multi-objective task allocation method with optimal comprehensive energy consumption was proposed, which includes the following steps:

[0005] S1. Establishing an optimization objective function to characterize the synchronization of the aircraft striking the target;

[0006] S2. Set the guidance law so that the aircraft can accurately hit the target under any air resistance;

[0007] S3. Use Hungarian algorithm for target allocation.

[0008] In a preferred embodiment, in S1, the optimization objective function is set to:

[0009]

[0010] 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 ith aircraft, a Mi represents the normal acceleration command of the ith aircraft, t f,i represents the total flight time of the ith aircraft, C D0 represents the zero-lift drag coefficient, and t represents the time factor.

[0011] In a preferred embodiment, in S2, the guidance law may adopt any known guidance law. Preferably, the guidance law is set as:

[0012]

[0013] Among them, N σ,i 、N γ,i , μ is the navigation parameter, γ drepresents the terminal landing angle of the aircraft, σ i represents the track angle between the i-th aircraft and the target, λ i represents the line of sight angle between the ith aircraft and the target.

[0014] In a preferred embodiment, the navigation parameters are set as:

[0015]

[0016] μ=4kr i

[0017]

[0018] Among them, k is an intermediate parameter, ρ represents the atmospheric density, S ref represents the reference area of ​​the aircraft, and m represents the mass of the aircraft.

[0019] The beneficial effects of the present invention include:

[0020] (1) Taking into account the impact of energy loss and drag on flight speed, in the multi-aircraft-target assignment problem, the flight path selection is optimized by comprehensively considering the interaction between targets and aircraft;

[0021] (2) It can not only accurately calculate the energy consumption required for the aircraft to complete the mission, but also effectively predict the speed loss caused by flight resistance, thereby achieving more efficient task allocation in a multi-target environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic flow chart of a multi-aircraft-multi-objective task allocation method with optimal comprehensive energy consumption according to a preferred embodiment of the present invention is shown;

[0023] Figure 2 The motion trajectory of the aircraft under the optimal allocation in Example 1 is shown;

[0024] Figure 3 The variance results of Comparative Example 1 are shown;

[0025] Figure 4 The motion trajectory of the aircraft under the optimal allocation in Comparative Example 1 is shown. DETAILED DESCRIPTION

[0026] 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.

[0027] 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.

[0028] A multi-aircraft-multi-objective task allocation method with optimal comprehensive energy consumption provided by the present invention comprises the following steps:

[0029] S1. Establishing an optimization objective function to characterize the synchronization of the aircraft striking the target;

[0030] S2. Set the guidance law so that the aircraft can accurately hit the target under any air resistance;

[0031] S3. Use Hungarian algorithm for target allocation.

[0032] In S1, the optimization objective function is set to:

[0033]

[0034] 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 normal acceleration command of the ith aircraft, t f,i represents the total flight time of the ith aircraft, C D0 represents the zero-lift drag coefficient, and t represents the time factor.

[0035] The above-mentioned optimization objective function takes the variance between the strike times of multiple aircraft on different targets as the target. The first half is the integral of the square of the normal acceleration of the aircraft, and the second half is the product of zero-lift drag and the remaining flight time. Using it as the optimization objective function can simultaneously consider the control of energy consumption and the influence of air resistance, thereby improving the accurate measurement of the synchronization of aircraft strikes on targets.

[0036] Preferably, the remaining flight time can be expressed as:

[0037] t go,i =r i / V c,i

[0038] Among them, r i represents the distance between the i-th aircraft and the target, V c,i represents the relative speed between the ith aircraft and the target.

[0039] In S2, the guidance law may adopt any known guidance law. Preferably, the guidance law is set as:

[0040]

[0041] Among them, N σ,i 、N γ,i , μ is the navigation parameter, γ d represents the terminal landing angle of the aircraft, σ i represents the track angle between the i-th aircraft and the target, λ i represents the line of sight angle between the ith aircraft and the target.

[0042] The guidance law takes into account the optimal landing angle control of aerodynamic drag. The guidance law consists of two feedback terms: observation angle and attack angle error, with time-varying gain.

[0043] Preferably, the navigation parameters are set as:

[0044]

[0045] μ=4kr i

[0046]

[0047] Among them, k is an intermediate parameter, ρ represents the atmospheric density, S ref represents the reference area of ​​the aircraft, and m represents the mass of the aircraft.

[0048] In S3, the Hungarian algorithm is used to establish the correspondence between different aircraft and different targets, so that each target corresponds to at least one aircraft, thus achieving target allocation.

[0049] The Hungarian algorithm is a classic exact assignment method that can efficiently handle bipartite graph matching problems and ensure a global optimal solution during the optimization process.

[0050] Example

[0051] Example 1

[0052] Conducting a target task allocation simulation experiment includes the following steps:

[0053] S1. Establishing an optimization objective function to characterize the synchronization of the aircraft striking the target;

[0054] S2. Set the guidance law so that the aircraft can accurately hit the target under any air resistance;

[0055] S3. Use Hungarian algorithm for target allocation.

[0056] In S1, the optimization objective function is set to:

[0057]

[0058] t go,i =r i / V c,i

[0059] In S2, the guidance law is set as:

[0060]

[0061] μ=4kr i

[0062]

[0063] Among them, ρ is set to 1.225kg / m 3 , S ref Set to 0.0491m 2 , m is set to 100 kg, C D0 Set to 0.35.

[0064] In the simulation, the flight trajectory simulation step size is set to a fixed step size of 0.01s, and the terminal landing angle is uniformly set to γ d =-50deg, the initial conditions are set as:

[0065]

[0067] The Hungarian algorithm obtained the minimum comprehensive energy consumption of 5.3384*105m after one iteration. 2 / s 3 The energy consumption value corresponds to the distribution (3,1,4,2).

[0068] Comparative Example 1

[0069] The same experiment as in Example 1 was performed, except that the enumeration method was used. The variance results in the enumeration method are as follows: Figure 3 As shown, the enumeration method is based on the variance results for allocation. As the number of aircraft and targets increases, the number of allocation combinations that need to be enumerated will explode.

[0070] Compared with the traditional enumeration method for allocation, the method in Example 1 will have more obvious advantages in target allocation as the number of aircraft and targets increases, and the amount of calculation in Example 1 will be greatly reduced.

[0071] Under the optimal allocation obtained by simulation in Example 1, the trajectory of the aircraft is as follows: Figure 2As shown, under the optimal allocation obtained by simulation of Example 1, the trajectory of the aircraft is as follows Figure 4 As shown in the figure, it can be seen that the distribution results in Example 1 and Comparative Example 1 are the same, and the movement trajectory of the aircraft is the same. However, the calculation amount in Example 1 is lower and the reaction rate is faster.

[0072] 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 with optimal comprehensive energy consumption, 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. Set the guidance law so that the aircraft can accurately hit the target under any air resistance; S3. Use Hungarian algorithm for target allocation.

2. The multi-aircraft-multi-objective task allocation method with optimal comprehensive energy consumption according to claim 1 is characterized in that: In S1, the optimization objective function is set to: 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 normal acceleration command of the ith aircraft, t f,i represents the total flight time of the ith aircraft, C D0 represents the zero-lift drag coefficient, and t represents the time factor.

3. The multi-aircraft-multi-objective task allocation method with optimal comprehensive energy consumption according to claim 1 is characterized in that: In S2, the guidance law is set as: Among them, N σ,i 、N γ,i , μ is the navigation parameter, γ d represents the terminal landing angle of the aircraft, σ i represents the track angle between the i-th aircraft and the target, λ i represents the line of sight angle between the ith aircraft and the target.

4. The multi-aircraft-multi-objective task allocation method with optimal comprehensive energy consumption according to claim 3 is characterized in that: The navigation parameters are set as: Among them, k is an intermediate parameter, ρ represents the atmospheric density, S ref represents the reference area of ​​the aircraft, and m represents the mass of the aircraft.

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