Research on Distributed Unmanned Aerial Vehicle formation Control Based on Reinforcement Iterative Learning
During the flight of unmanned aerial vehicles(UAVs),the formation information defined by the base station host has a certain impact on the aircraft's turning behavior.To ensure UAV stable flight,a distributed UAV formation control algorithm based on reinforcement iterative learning is researched.The specific numerical values of the reinforcement learning function are com-puted,and the distribution representation of the iterative value probability coefficient is achieved through iterative processing,imple-menting the design of the reinforcement iterative learning model.Based on this foundation,the UAV formation topology structure is defined,and the specific numerical results for the information migration indicators are solved to achieve the UAV information migra-tion based on reinforcement iterative learning.With the cooperation of the UAV formation controller,a distributed formation infor-mation collection is established,and formation data samples are combined to solve the UAV control parameters,enabling the precise control of the distributed UAV formation.Furthermore,the control algorithm execution process is enhanced based on the modeling condition of marching formation,completing the distributed UAV formation control method based on reinforcement iterative learning.Experimental results show that under the influence on the reinforcement iterative learning model,the yaw angle of the UAVs remains within the range of 0°～90°,indicating that the aircrafts fly the formation information defined by the base station host with a relatively stable motion state,which is in line with practical application requirements.
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