Multi-target control parameter tuning method of unmanned aerial vehicle cluster control system

Abstract

The invention discloses a multi-target control parameter tuning method for an unmanned aerial vehicle cluster control system. The method comprises the steps of: 1, acquiring position information and target position information of an unmanned aerial vehicle cluster, and constructing an unmanned aerial vehicle cluster control model based on an artificial potential field; 2, expanding the unmanned aerial vehicle cluster control model into a rule base control model with a plurality of rules; and 3, performing optimization selection on parameters of the rule base control model by considering four indexes of a death rate, aggregation, isotropy and a scene completion speed of the unmanned aerial vehicle cluster. According to the multi-target control parameter tuning method, multiple targets are synchronously optimized, so that the condition that some parameter combinations which are not prominent in comprehensive performance but have good performance on part of optimization targets are eliminated too early is avoided, and finally, the optimization efficiency of the parameters is improved. Artificial chromosomes generated by an external storage pool are used for replacing chromosomes with small contribution degree to population entropy in a population, and the problem that optimal parameters cannot be obtained due to premature convergence in the solving process is solved.

Description

technical field [0001] The invention belongs to the field of robot intelligent control, and in particular relates to a multi-objective control parameter optimization method of an unmanned aerial vehicle cluster control system. Background technique [0002] Group UAV control is a main research direction of UAV control at present. It mainly studies how to control the behavior of UAV swarms through appropriate rules to ensure that UAV swarms can complete the pre-set swarm behavior. . In the research of many UAV swarm control rules, the control scheme based on virtual physical rules is the most widely used behavior control scheme. The article that first proposed this method is document 1 "Khatib O. Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Robot. Res., 1986, 5(1):90-98.". In this article, the author introduces a concept artificial potential field (APF) for UAV swarm control. UAV swarm controllers designed based on APF usually meet the following ...

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Problems solved by technology

[0006] Although the UAV swarm control model based on virtual physical rules has been extensively studied, the model still needs to solve a crucial problem: how to ensure that the UAV control model can be stable in different environments. good performance of

But the disadvantages of these algorithms are:

However, during the execution of missions of UAV swarms, there is a conflict between the aggregation requirements of UAV swarms and the requirements of avoiding collisions between UAVs. Optimizing it as a single-objective problem cannot really solve the problem of UAVs. Multi-objective problems in the cluster, such as the speed requirements for completing the target, the aggregation requirements of the UAV cluster, and the requirements for the UAV cluster to avoid mutual collision, etc., may have potential conflicts. good control

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