Research On UAV Path Planning And Trajectory Following Control Based On Intelligent Optimization Algorithm

Unmanned Aerial Vehicles(UAV)has been widely used in military and civilian applications because of their simple structure,vertical take-off and landing,flight flexibility and low cost.In practice,quadrotor UAVs perform tasks in an extremely complex environment,and depending on the task a suitable path planning algorithm needs to be selected.In addition,the quadrotor system is highly non-linear and its motion is highly coupled,thus placing high demands on the control system.With the increasing demand,in order to further enhance the intelligence and efficiency of UAVs,path planning and tracking control of UAVs have become a hot research issue in various countries.This paper focuses on the above two hotspot directions,with emphasis on solving the shortcomings of traditional intelligent optimization algorithms in terms of poor optimization-seeking ability when dealing with path planning problems of high complexity,as well as the traditional fuzzy PID parameter optimization problem.The main contents and summary of the paper are as follows:(1)Establish a mathematical model of a quadrotor UAV.The basic knowledge of the basic structure,flight principle,coordinate system and attitude of the quadrotor is described in detail.A mathematical model of the kinematics and dynamics of the UAV is derived based on relevant theories.(2)A hybrid algorithm based on the Arithmetic Optimization Algorithm and the Harris Hawks optimization is proposed,namely Ensemble of Arithmetic Optimization Algorithm and Harris Hawks Optimization(EAOAHHO).The Pinhole Imaging Opposition-Based Learning strategy is introduced to improve the diversity of the original population and the ability to evade local optima.In addition,the introduction of Ensemble/Composite Mutation Strategy enhances the exploitation and exploration of this proposed hybrid algorithm and obtains better convergence accuracy.The performance of the algorithm is verified by 23 benchmark functions and CEC 2017 test suit.(3)A 3D spatial model of UAV operation is established,terrain constraints and threat conditions are introduced,and a proper fitness function is constructed.The designed algorithm is applied to solve the UAV 3D path planning problem,and the simulations indicate that that the proposed EAOAHHO converges faster and with greater convergence accuracy than the two algorithms before improvement.A more suitable path can be searched for when solving the path planning problem.In addition,the feasibility of the proposed algorithm to solve the path planning problem is experimentally verified.(4)The EAOAHHO Fuzzy PID is designed to solve the UAV trajectory tracking problem.The algorithm proposed in this paper is used for parameter tuning of the UAV fuzzy PID to enhance the accuracy and tracking efficiency of the trajectory tracking.It is demonstrated through simulation that the EAOAHHO Fuzzy PID has better control effect than the traditional PID.