Extremum seeking control for UAV close formation flight via improved pigeon-inspired optimization

This paper proposes a comprehensive design scheme for the extremum seeking control(ESC)of the unmanned aerial vehicle(UAV)close formation flight.The proposed design scheme combines a Newton-Raphson method with an extended Kalman filter(EKF)to dynamically estimate the optimal position of the following UAV relative to the leading UAV.To reflect the wake vortex effects reliably,the drag coefficient induced by the wake vortex is considered as a performance function.Then,the performance function is parameterized by the first-order and second-order terms of its Taylor series expansion.Given the excellent performance of nonlinear estimation,the EKF is used to estimate the gradient and the Hessian matrix of the parameterized performance function.The output feedback of the proposed scheme is determined by iterative calculation of the Newton-Raphson method.Compared with the traditional ESC and the classic ESC,the proposed design scheme avoids the slow continuous time integration of the gradient.This allows a faster convergence of relative position extremum.Furthermore,the proposed method can provide a smoother command during the seeking process as the second-order term of the performance function is taken into account.The convergence analysis of the proposed design scheme is accomplished by showing that the output feedback is a supermartingale sequence.To improve estimation performance of the EKF,a improved pigeon-inspired optimization(IPIO)is proposed to automatically tune the noise covariance matrix.Monte Carlo simulations for a three-UAV close formation show that the proposed design scheme is robust to the initial position of the following UAV.

Distributed Cooperative Control Algorithm for Multi-UAV Mission Rendezvous

Multiple unmanned aerial vehicles(UAVs)cooperative operation is the main form for UAVs fighting in battlefield,and multi-UAV mission rendezvous is the premise of cooperative reconnaissance and attack missions.We propose a rendezvous control strategy,which divides the rendezvous process into two parts:The loose formation rendezvous and the close formation rendezvous.In the first stage,UAVs are supposed to reach the specific target locations simultaneously and form a loose formation.A distributed control strategy based on first-order consensus algorithm is presented to achieve this goal.Then the second stage is designed based on the second-order consensus algorithm to complete the transition from the loose formation to the close formation.This process needs the speeds and heading angles of UAVs to reach an agreement.Besides,control algorithms with a virtual leader are proposed,by which the formation states can reach a specific value.Finally,simulation results show that the control algorithms are capable of realizing the mission rendezvous of multi-UAV and the consistence of UAVs′final states,which verify the effectiveness and feasibility of the designed control strategy.

Reinforcement learning based UAV formation control in GPS-denied environment

Highly accurate positioning is a crucial prerequisite of multi Unmanned Aerial Vehicle close-formation flight for target tracking,formation keeping,and collision avoidance.Although the position of a UAV can be obtained through the Global Positioning System(GPS),it is difficult for a UAV to obtain highly accurate positioning data in a GPS-denied environment(e.g.,a GPS jamming area,suburb,urban canyon,or mountain area);this may cause it to miss a tracking target or collide with another UAV.In particular,UAV close-formation control in GPS-denied environments faces difficulties owing to the low-accuracy position,close distance between vehicles,and nonholonomic dynamics of a UAV.In this paper,on the one hand,we develop an innovative UAV formation localization method to address the formation localization issues in GPS-denied environments;on the other hand,we design a novel reinforcement learning based algorithm to achieve the high-efficiency and robust performance of the controller.First,a novel Lidar-based localization algorithm is developed to measure the localization of each aircraft in the formation flight.In our solution,each UAV is equipped with Lidar as the position measurement sensor instead of the GPS module.The k-means algorithm is implemented to calculate the center point position of UAV.A novel formation position vector matching method is proposed to match center points with UAVs in the formation and estimate their position information.Second,a reinforcement learning based UAV formation control algorithm is developed by selecting the optimal policy to control UAV swarm to start and keep flying in a close formation of a specific geometry.Third,the innovative collision risk evaluation module is proposed to address the collision-free issues in the formation group.Finally,a novel experience replay method is also provided in this paper to enhance the learning efficiency.Experimental results validate the accuracy,effectiveness,and robustness of the proposed scheme.