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

Abstract

The application discloses a multi-aircraft-multi-target task allocation method with optimal comprehensive energy consumption, and comprises the following steps: an optimization target function is established and is used for representing the synchronization of aircrafts attacking targets; a guidance law is set, so that the aircrafts can accurately hit the targets under any air resistance; and the Hungarian algorithm is used for target allocation. The method disclosed by the application considers the influence of energy loss and resistance on flight speed, and optimizes the selection of flight paths by comprehensively considering the interaction effect between targets and aircrafts in the multi-aircraft-target allocation problem.

Description

TECHNICAL FIELD

[0001] The present application relates to a multi-aircraft multi-target task allocation method with optimal comprehensive energy consumption, and belongs to the technical field of flight data control. BACKGROUND

[0002] In a multi-aircraft cooperative attack task, the energy consumption of the aircraft and the air resistance have an important influence on the flight efficiency. If the energy consumption of the aircraft is too large or the influence of the air resistance is more significant when the aircraft is executing the task, the flight speed of the aircraft will be slowed down, thereby causing the attack time to be prolonged, and even the task cannot be completed on time.

[0003] Therefore, it is necessary to carry out research on the existing target allocation problem to solve the above problems. SUMMARY

[0004] In order to overcome the above problems, in-depth research is conducted, and a multi-aircraft multi-target task allocation method with optimal comprehensive energy consumption is proposed, which comprises the following steps:

[0005] S1, an optimization objective function is established, which is used to represent the synchronization of the aircraft attacking the target;

[0006] S2, a guidance law is set, so that the aircraft can accurately hit the target under any air resistance;

[0007] S3, the Hungarian algorithm is used for target allocation.

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

[0009]

[0010] Wherein, minimize represents minimization, subscript i represents the i th aircraft, n represents the total number of aircrafts, t go,i represents the remaining flight time of the i th aircraft, a Mi represents the normal acceleration instruction of the i th aircraft, t f,i represents the total flight time of the i th 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 can adopt any known guidance law, preferably, the guidance law is set as:

[0012]

[0013] Wherein, N σ,i , N γ,i , and μ are navigation parameters, and γ ddenotes the terminal impact angle of the aircraft, σ i denotes the track angle between the ith aircraft and the target, λ i denotes 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] wherein k is an intermediate parameter, ρ denotes the atmospheric density, S ref denotes the reference area of the aircraft, and m denotes the mass of the aircraft.

[0019] The present application has the beneficial effects including:

[0020] (1) The effects of energy loss and resistance on flight speed are taken into account, and in the multi-aircraft-target allocation problem, the selection of flight path is optimized by comprehensively considering the interaction between the target and the aircraft.

[0021] (2) Not only can the energy consumption required for the aircraft to complete the task be accurately calculated, but also the speed loss caused by flight resistance can be effectively predicted, so that more efficient task allocation can be realized in a multi-target environment. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 shows a flowchart of a multi-aircraft-multi-target task allocation method according to a preferred embodiment of the present application, which is optimal in comprehensive energy consumption;

[0023] Figure 2 shows the aircraft motion trajectory under optimal allocation in Example 1;

[0024] Figure 3 shows the variance results of Comparative Example 1;

[0025] Figure 4 shows the aircraft motion trajectory under optimal allocation in Comparative Example 1. DETAILED DESCRIPTION

[0026] The present application will be further described in detail below with reference to the accompanying drawings and examples. Through these descriptions, the features and advantages of the present application will become more apparent.

[0027] The word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any implementation described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other implementations. While the illustrative aspects of the embodiments are described in conjunction with the attached drawings, it is not intended that the application be limited to the aspects described in the attached drawings. Rather, the application is intended to cover all alternatives, modifications, and equivalents of the aspects described in the attached drawings.

[0028] According to the application, a multi-aircraft-multi-target task allocation method with optimal comprehensive energy consumption is provided, comprising the following steps:

[0029] S1, establishing an optimization objective function for representing the synchronization of aircraft attacking targets;

[0030] S2, setting a guidance law so that the aircraft can accurately hit the target under any air resistance;

[0031] S3, using the Hungarian algorithm for target allocation.

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

[0033]

[0034] Wherein, minimize represents minimization, subscript i represents the i-th aircraft, n represents the total number of aircrafts, t go,i represents the remaining flight time of the i-th aircraft, represents the normal acceleration command of the i-th aircraft, t f,i represents the total flight time of the i-th aircraft, C D0 represents the zero-lift drag coefficient, and t represents the time factor.

[0035] The above optimization objective function takes the variance between the attack times of multiple aircrafts 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 the zero-lift drag and the remaining flight time, which is used as the optimization objective function, so that the influence of control energy consumption and air resistance can be considered at the same time, thereby improving the accurate measurement of the synchronization of aircraft attacking targets.

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

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

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

[0039] In S2, the guidance law can adopt any known guidance law, preferably, the guidance law is set as:

[0040]

[0041] wherein N σ,i , N γ,i , μ is a navigation parameter, γ d represents a terminal impact angle of the aircraft, σ i represents a track angle between the i-th aircraft and the target, λ i represents a line-of-sight angle between the i-th aircraft and the target.

[0042] The guidance law considers the optimal impact angle control of aerodynamic drag, and the guidance law is composed of two feedback terms: observation angle and attack angle error, with time-varying gain.

[0043] Preferably, the navigation parameter is set as:

[0044]

[0045] μ = 4kr i

[0046]

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

[0048] In S3, the Hungarian algorithm is used to establish a corresponding relationship between different aircrafts and different targets, so that each target corresponds to at least one aircraft, and target assignment is realized.

[0049] The Hungarian algorithm is a classic exact assignment method, which can efficiently process bipartite graph matching problems and ensure to obtain a globally optimal solution in the optimization process.

[0050] Embodiment

[0051] Embodiment 1

[0052] The target task assignment simulation experiment is carried out, including the following steps:

[0053] S1, an optimal target function is established, which is used to represent the synchronization of the aircraft attacking the target;

[0054] S2, a guidance law is set, so that the aircraft can accurately hit the target under any air resistance;

[0055] S3, the Hungarian algorithm is used for target assignment.

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

[0057]

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

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

[0060]

[0061] μ = 4kr i

[0062]

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

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

[0065]

[0066] The Hungarian algorithm, after one iteration, yielded a comprehensive minimum energy consumption of 5.3384 * 10⁵ m³. 2 / s 3 The energy consumption value corresponds to the allocation (3, 1, 4, 2).

[0067] Comparative Example 1

[0068] The same experiment as in Example 1 was conducted, except that an enumeration method was used. The variance results of 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.

[0069] Compared with the traditional enumeration method, the method in Example 1 has a more obvious advantage in target allocation as the number of aircraft and targets increases, and the amount of computation is greatly reduced in Example 1.

[0070] Under the optimal allocation obtained from the simulation in Example 1, the aircraft's trajectory is as follows: Figure 2 As shown, under the optimal allocation obtained from the simulation of Comparative Example 1, the trajectory of the aircraft is as follows:Figure 4 As shown in the figure, the allocation results in Example 1 are the same as those in Comparative Example 1, and the aircraft trajectory is the same. However, Example 1 has lower computational load and faster response speed.

[0071] The present invention has been described above with reference to preferred embodiments; however, these embodiments are merely exemplary and illustrative. Various substitutions and modifications can be made to the present invention based on these embodiments, all of which fall within the scope of protection of the present invention.

Claims

1. A multi-aircraft, multi-target mission allocation method with optimal overall energy consumption, characterized in that, Includes the following steps: S1. Establish an optimization objective function to characterize the synchronization of the aircraft's attack on the target; S2. Set the guidance law so that the aircraft can accurately hit the target under any air resistance; S3. The Hungarian algorithm is used for target allocation; In S1, the optimization objective function is set as follows: Where minimize means minimize, the subscript i represents the i-th aircraft, n represents the total number of aircraft, and t go,i This represents the remaining flight time of the i-th aircraft. t represents the normal acceleration command for the i-th aircraft. f,i Let C represent the total flight time of the i-th aircraft. D0 This represents the zero-lift drag coefficient, and t represents the time factor; In S2, the guidance law is set as follows: Where, N σ,i N γ,i μ are navigation parameters, γ d σ represents the terminal angle of the aircraft. i Let λ represent the trajectory angle between the i-th aircraft and the target. i This represents the line-of-sight angle between the i-th aircraft and the target.

2. The multi-vehicle-multi-target mission allocation method with optimal overall energy consumption according to claim 1, characterized in that, Navigation parameters are set as follows: μ=4kr i Where k is an intermediate parameter, ρ represents atmospheric density, and S ref The reference area of ​​the aircraft is represented by m, and the mass of the aircraft is represented by m.

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