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.

Full mode flight dynamics modelling and control of stopped-rotor UAV

The Stopped-Rotor(SR)UAV combines the advantages of vertical take-off and landing of helicopter and high-speed cruise of fixed-wing aircraft.At the same time,it also has a unique aerodynamic layout,which leads to great differences in the control and aerodynamic characteristics of various flight modes,and brings great challenges to the flight dynamics modelling and control in full-mode flight.In this paper,the flight dynamics modelling and control method of SR UAV in full-mode flight is studied.First,based on the typical flight profile of SR UAV when performing missions,using the theory and method of fuzzy mathematics,the T-S flight dynamics model of SR UAV in full-mode flight is established by synthesizing the flight dynamics model of each flight mode.Then,an explicit model tracking and parameter adjusting control system based on fuzzy theory is designed to enhance the stability of the inner loop of SR UAV in full-mode flight,which effectively reduces the coupling between axes and improves the control quality of the system.Finally,the outer loop control system is designed by using classical control method,and the control law of SR UAV in full-mode automatic flight is obtained.The simulation results show that the proposed control system design method is feasible and effective,which lays a solid foundation for the subsequent engineering implementation of the SR UAV.