Inspired by the collective behaviors observed in bird flocks and fish schools,this paper proposes a novel Decentralized Model Predictive Flocking Control(DMPFC)framework to enable UAV swarms to autonomously track pred...Inspired by the collective behaviors observed in bird flocks and fish schools,this paper proposes a novel Decentralized Model Predictive Flocking Control(DMPFC)framework to enable UAV swarms to autonomously track predefined reference trajectories while avoiding collisions and maintaining a stable quasi[Math Processing Error]-lattice formation.Unlike traditional approaches that rely on switching between predefined swarm formations,this framework utilizes identical local interaction rules for each UAV,allowing them to dynamically adjust their control inputs based on the motion states of neighboring UAVs,external environmental factors,and the desired reference trajectory,thereby enabling the swarm to adapt its formation dynamically.Through iterative state updates,the UAVs achieve consensus,allowing the swarm to follow the reference trajectory while self-organizing into a cohesive and stable group structure.To enhance computational efficiency,the framework integrates a closed-form solution for the optimization process,enabling real-time implementation even on computationally constrained micro-quadrotors.Theoretical analysis demonstrates that the proposed method ensures swarm consensus,maintains desired inter-agent distances,and stabilizes the swarm formation.Extensive simulations and real-world experiments validate the approach’s effectiveness and practicality,demonstrating that the proposed method achieves velocity consensus within approximately 200 ms and forms a stable quasi[Math Processing Error]-lattice structure nearly ten times faster than traditional models,with trajectory tracking errors on the order of millimeters.This underscores its potential for robust and efficient UAV swarm coordination in complex scenarios.展开更多
This paper studies a flocking model in which the interaction between agents is described by a general local nonlinear function depending on the distance between agents. The existing analysis provided sufficient condit...This paper studies a flocking model in which the interaction between agents is described by a general local nonlinear function depending on the distance between agents. The existing analysis provided sufficient conditions for flocking under an assumption imposed on the system’s closed-loop states; however this assumption is hard to verify. To avoid this kind of assumption the authors introduce some new methods including large deviations theory and estimation of spectral radius of random geometric graphs. For uniformly and independently distributed initial states, the authors establish sufficient conditions and necessary conditions for flocking with large population. The results reveal that under some conditions, the critical interaction radius for flocking is almost the same as the critical radius for connectivity of the initial neighbor graph.展开更多
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 in part by the Guangdong Provincial Universities'Characteristic Innovation Project under Grant 2024KTSCX360in part by the Guangdong Educational Science Planning Project under Grant 2023GXJK837.
文摘Inspired by the collective behaviors observed in bird flocks and fish schools,this paper proposes a novel Decentralized Model Predictive Flocking Control(DMPFC)framework to enable UAV swarms to autonomously track predefined reference trajectories while avoiding collisions and maintaining a stable quasi[Math Processing Error]-lattice formation.Unlike traditional approaches that rely on switching between predefined swarm formations,this framework utilizes identical local interaction rules for each UAV,allowing them to dynamically adjust their control inputs based on the motion states of neighboring UAVs,external environmental factors,and the desired reference trajectory,thereby enabling the swarm to adapt its formation dynamically.Through iterative state updates,the UAVs achieve consensus,allowing the swarm to follow the reference trajectory while self-organizing into a cohesive and stable group structure.To enhance computational efficiency,the framework integrates a closed-form solution for the optimization process,enabling real-time implementation even on computationally constrained micro-quadrotors.Theoretical analysis demonstrates that the proposed method ensures swarm consensus,maintains desired inter-agent distances,and stabilizes the swarm formation.Extensive simulations and real-world experiments validate the approach’s effectiveness and practicality,demonstrating that the proposed method achieves velocity consensus within approximately 200 ms and forms a stable quasi[Math Processing Error]-lattice structure nearly ten times faster than traditional models,with trajectory tracking errors on the order of millimeters.This underscores its potential for robust and efficient UAV swarm coordination in complex scenarios.
基金supported by the National Natural Science Foundation of China under Grant No.11688101,91634203,91427304,and 61673373the National Key Basic Research Program of China(973 Program)under Grant No.2016YFB0800404
文摘This paper studies a flocking model in which the interaction between agents is described by a general local nonlinear function depending on the distance between agents. The existing analysis provided sufficient conditions for flocking under an assumption imposed on the system’s closed-loop states; however this assumption is hard to verify. To avoid this kind of assumption the authors introduce some new methods including large deviations theory and estimation of spectral radius of random geometric graphs. For uniformly and independently distributed initial states, the authors establish sufficient conditions and necessary conditions for flocking with large population. The results reveal that under some conditions, the critical interaction radius for flocking is almost the same as the critical radius for connectivity of the initial neighbor graph.
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.
