Multi-objective control parameter optimization method for UAV swarm control system

Abstract

The invention discloses a multi-objective control parameter optimization method for a UAV cluster control system, including step 1, acquiring location information and target location information of the UAV cluster, and constructing a UAV cluster control model based on an artificial potential field ; Step 2, expanding the UAV swarm control model into a rule base control model with multiple rules; Step 3, from the four indicators of the UAV swarm's mortality rate, aggregation, isotropy and scene completion speed Considering, optimize and select the parameters of the rule base control model. The present invention avoids premature elimination of some parameter combinations that are not outstanding in comprehensive performance but have good performance in part of the optimization objectives by synchronously optimizing multiple objectives, and ultimately improve the optimization efficiency of parameters. By using the artificial chromosomes generated by the external storage pool to replace the chromosomes that contribute less to the population entropy, the problem that the optimal parameters cannot be obtained due to premature convergence in the solution 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 tuning method of an unmanned aerial vehicle swarm control system. Background technique [0002] Swarm UAV control is a main research direction of current UAV control. It mainly studies how to control the behavior of UAV swarms through appropriate rules to ensure that UAV swarms can complete preset swarm behaviors. . 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-timeobstacle avoidance for manipulators and mobile robots.Int.J.Robot.Res.,1986,5(1):90-98.". In this article, the authors introduce a concept Artificial Potential Field (APF) for swarm control of UAVs. The UAV swarm controller designed based on APF usually meets the following criteria: any U...

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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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