Enhancing the adaptability of Unmanned Aerial Vehicle(UAV)swarm control models to cope with different complex working scenarios is an important issue in this research field.To achieve this goal,control model with tuna...Enhancing the adaptability of Unmanned Aerial Vehicle(UAV)swarm control models to cope with different complex working scenarios is an important issue in this research field.To achieve this goal,control model with tunable parameters is a widely adopted approach.In this article,an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking(MO O-Flocking)is proposed.The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters.To achieve multi-objective parameter tuning,a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2(ISPEA2)is designed.Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives.Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.展开更多
基金supported by the Hunan Provincial Natural Science Foundation of China(No.2023JJ40686).
文摘Enhancing the adaptability of Unmanned Aerial Vehicle(UAV)swarm control models to cope with different complex working scenarios is an important issue in this research field.To achieve this goal,control model with tunable parameters is a widely adopted approach.In this article,an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking(MO O-Flocking)is proposed.The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters.To achieve multi-objective parameter tuning,a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2(ISPEA2)is designed.Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives.Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.
