Research On Autonomous Planning And Control Technology Of UAVs Cluster Formation

Considering the increasing complexity of UAV mission,the mode of task execution is changing greatly.The cooperative task execution of unmanned aerial vehicles(UAVs)cluster formation is an important development direction in the future.At present,the situational awareness and autonomous decision-making ability of UAV can not completely replace human thinking and judgment,so it's difficult to satisfy the requirements of high-level intelligence and autonomy for complex tasks.Therefore,the performance of UAV as a single platform to perform tasks is extremely limited,it is still necessary to improve the performance through collaboration and cluster.Therefore,this paper considers the problems of task allocation,formation design,trajectory planning and formation control,and studies the autonomous formation control of UAV cluster.The main contents of the paper are as follows:Firstly,a cooperative execution task architecture and task flow are given.Considering the low precision of task allocation problem for UAVs cluster formation in ground station decision making,discrete particle swarm optimization is combined with genetic algorithm,simulated annealing algorithm,dynamic variable inertia weight and multiple learning object strategy image to improve the accuracy and speed of task allocation.The approximate optimal solution of task allocation is obtained.For the task allocation problem of UAVs formation in leader UAV decision-making,the improved particle swarm optimization algorithm and distributed auction algorithm are used to realize real-time task allocation,respectively,when the number of task targets is less than or more than the number of UAVs.The effectiveness of task allocation results is verified by numerical simulation.Secondly,according to the mission assignment solution,the formation design problem is divided into two parts: formation and internal station determination and relative distance determination.Taking the maximum operational effectiveness as the performance index,the DDPSO-GA algorithm is adopted to obtain the optimal formation and internal station position,and the constraints such as wind speed,rainfall,mission target type,maneuverability and minimum safe distance are considered in the task environment.The performance index is the maximum communication power and the highest safety factor,and the base is adopted.Brain-like intelligent algorithm based on deep Q network can quickly obtain the relative distance of formation,thus completing the formation design of UAVs cluster.The effectiveness of the algorithm is proved by Monte Carlo simulation.For the first time,a new idea of formation design is given through optimization.Thirdly,considering that UAVs formation trajectory planning problem is difficult to be solved quickly because of collision avoidance between UAVs,a hierarchical solution strategy is proposed by combining adaptive pseudospectral method with fast random search tree method.Firstly,based on the rapidly exploring random tree(RRT)method,leader UAV plan the safe path points for each UAV.Thus,the formation trajectory planning problem is transformed into the single aircraft trajectory planning problem.The optimal trajectory is obtained by the adaptive pseudo-spectral method,which can be adaptively selected between the path points.The feasibility of the designed trajectory is verified by numerical simulation.Finally,considering the external disturbances and model uncertainties in the UAVs formation control process,a formation control strategy based on potential energy function and adjacent aircraft States is proposed to realize the UAVs formation trajectory tracking and formation maintenance control.In the process of trajectory tracking controller design,based on the potential energy function,a finite-time terminal sliding mode controller is designed for the outer position loop and the inner attitude loop respectively,which avoids the collision problem during trajectory tracking and realizes the fast and stable tracking of formation trajectory.A finite time loop and an outer loop controller are designed to achieve fast and high precision formation maintenance control under the influence of disturbance and model uncertainty.