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Cluster collaborative deployment method based on distributed optimal energy MPC

A distributed and optimal technology, applied in non-electric variable control, complex mathematical operations, instruments, etc., can solve the problems of low UAV density, poor flight trajectory, large control error, etc., and achieve the density of UAVs. High, short flight path, the effect of reducing the amount of calculation

Active Publication Date: 2021-06-01
BEIJING INSTITUTE OF TECHNOLOGYGY
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AI Technical Summary

Problems solved by technology

[0003] Traditional coordination schemes often start to execute after the UAV approaches the target or the moving speed of the task target is slow. However, when the task is faced with a wide environment, especially for the air-sea mission environment, the range of UAV execution tasks is as high as several Thousands of kilometers, the distance between the UAV and the mission target is relatively long, and when the mission target has high mobility, when the traditional coordination scheme is used, the flight trajectory of the UAV in the process of approaching the mission target is poor, resulting in a large amount of fuel Waste, it may happen that the UAV has run out of fuel before it approaches the mission target and starts to execute the cooperative mission mode, and cannot carry out subsequent missions
[0004] Traditional collaborative solutions mostly use a central server to generate control commands for all drones. The central server communicates wirelessly with the drones in real time, obtains the location of each drone and the target location detected by the drone, and generates commands based on the location information. The control instructions of each UAV, and then transmit the control instruction to each UAV through wireless communication, so as to control the flight status of all UAVs. However, when the target is far away from the server, the central server and the UAV The communication between machines will be affected, resulting in a decrease in the stability of data transmission, which will lead to errors in the control commands generated by the central server, and a decrease in control frequency, which will seriously affect the execution effect of collaborative tasks.
[0005] In addition, in the traditional control method, the data is transmitted back to the server by the drone, and then the server generates control commands and transmits them back to the drone, which will cause a large time delay. When the target moves quickly, it will cause the drone to Inability to make accurate contact with the target
[0006] In addition, considering the resource requirements of the target, sometimes it is necessary for multiple aircraft to contact the target at the same time to achieve saturated interaction. The traditional UAV coordination scheme is provided by the central server. Due to the large control error, in order to ensure the safety of the UAV, no one The distance between drones is relatively long, resulting in low density of drones, making it difficult to achieve saturated interaction

Method used

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  • Cluster collaborative deployment method based on distributed optimal energy MPC
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  • Cluster collaborative deployment method based on distributed optimal energy MPC

Examples

Experimental program
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Embodiment 1

[0144] Carry out a simulation experiment. In the experiment, 6 UAVs are set for coordinated deployment. The maximum speed of the UAVs is 1m / s, the initial position of the target is [2.5, 2.0], and the UAVs are initially distributed around the target. The cluster is optimal. Collaborate to gather and track the target.

[0145] Each UAV uses the proportional guidance method to plan the predicted trajectory, and sends the predicted trajectory to the adjacent UAVs.

[0146] After each UAV obtains the predicted trajectory of the adjacent UAV, the optimal trajectory is obtained by solving the distributed energy optimal control model. The distributed energy optimal control model is expressed as:

[0147]

[0148] Among them, the terminal cost φ(x i ,t f )Expressed as:

[0149] φ(x i ,t f )=w vc φ vc (x i ,t f )+w va φ va (x i ,t f )+w a φ a (x i ,t f ) (Four)

[0150] Further, in formula (4), φ vc (x i ,t f ) is the cluster speed control item:

[0151]

[...

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Abstract

The invention discloses a cluster collaborative deployment method based on distributed optimal energy MPC, and the method achieves the collaborative deployment of an unmanned aerial vehicle group through the ad hoc network communication of a plurality of unmanned aerial vehicles and the autonomous generation of a control instruction by each unmanned aerial vehicle. According to the cluster collaborative deployment method based on the distributed optimal energy MPC, an optimal control equation is established based on the distributed model predictive control method, and a suboptimal analytical solution is obtained by solving a Hamiltonian equation, so that the calculation amount of the optimization process is greatly reduced, the generation of a real-time track is realized, the method has the advantages of no need of central server control, large number of cluster unmanned aerial vehicles, high unmanned aerial vehicle density and the like.

Description

technical field [0001] The invention relates to a cluster cooperative deployment method, in particular to a distributed optimal energy MPC-based cluster cooperative deployment method, which belongs to the field of unmanned aerial vehicle control. Background technique [0002] The scheme of multi-UAV cooperative mission execution has been widely used in various tasks. Compared with single UAV mission execution, multi-UAV coordination has higher task execution efficiency. [0003] Traditional coordination schemes often start to execute after the UAV approaches the target or the moving speed of the task target is slow. However, when the task is faced with a wide environment, especially for the air-sea mission environment, the range of UAV execution tasks is as high as several Thousands of kilometers, the distance between the UAV and the mission target is relatively long, and when the mission target has high mobility, when the traditional coordination scheme is used, the flight ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/10G06F17/11
CPCG05D1/104G06F17/11
Inventor 张福彪王亚凯林德福杨希雯周天泽王治霖丁宇郎帅鹏刘明成毛杜芃
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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