Multi-UAV cooperative maneuver decision-making for pursuitevasion using improved MADRL

Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning(MADRL) is proposed. In this method, an improved Comm Net network based on a communication mechanism is introduced into a deep reinforcement learning algorithm to solve the multi-agent problem. A layer of gated recurrent unit(GRU) is added to the actor-network structure to remember historical environmental states. Subsequently,another GRU is designed as a communication channel in the Comm Net core network layer to refine communication information between UAVs. Finally, the simulation results of the algorithm in two sets of scenarios are given, and the results show that the method has good effectiveness and applicability.

A Continuous Time Domain Direct Identication Method for Unsteady Fluid Dynamic Model in Incompressible Flow

To identify the unsteady°uid dynamic model for a solid moving in the°uid,a new identication method that operates in the continuous time domain is proposed.It is illustrated that this new identication method is more accurate and appropriate for identifying unsteady°uid dynamic model in incompressible°ow in comparison with the classical frequency domain or discrete domain method.This new method is applied to identify the unsteady°uid dynamic models of a NACA0012 airfoil and the e®ect of the°uid viscosity on the model parameter is analyzed.The result shows that the added mass in viscous°ow is dependent on the input signal frequency and is exactly the added mass in inviscid°ow when the frequency approaches innity.Based on this discovery,the long controversy on the relationship of added mass between viscous and inviscid°ow is solved.Then,the enlightenment of this discovery on the identication of both linear and nonlinear unsteady aerodynamic model in incompressible°ow is illustrated.At last,the advantage of the new acquired unsteady°uid dynamic model is discussed in comparison with the classical quasi-steady aerodynamic model.

Target-enclosing affine formation control of two-layer networked spacecraft with collision avoidance

This paper addresses a target-enclosing problem for multiple spacecraft systems by proposing a two-layer affine formation control strategy. Compared with the existing methods,the adopted two-layer network structure in this paper is generally directed, which is suitable for practical space missions. Firstly, distributed finite-time sliding-mode estimators and formation controllers in both layers are designed separately to improve the flexibility of the formation control system. By introducing the properties of affine transformation into formation control protocol design,the controllers can be used to track different time-varying target formation patterns. Besides, multilayer time-varying encirclements can be achieved with particular shapes to surround the moving target. In the sequel, by integrating adaptive neural networks and specialized artificial potential functions into backstepping controllers, the problems of uncertain Euler-Lagrange models, collision avoidance as well as formation reconfiguration are solved simultaneously. The stability of the proposed controllers is verified by the Lyapunov direct method. Finally, two simulation examples of triangle formation and more complex hexagon formation are presented to illustrate the feasibility of the theoretical results.

A monocular visual measurement system for UAV probe-and-drogue autonomous aerial refueling

Purpose–The purpose of this paper is to develop a monocular visual measurement system for autonomous aerial refueling(AAR)for unmanned aerial vehicle,which can process images from an infrared camera to estimate the pose of the drogue in the tanker with high accuracy and real-time performance.Design/methodology/approach–Methods and techniques for marker detection,feature matching and pose estimation have been designed and implemented in the visual measurement system.Findings–The simple blob detection(SBD)method is adopted,which outperforms the Laplacian of Gaussian method.And a novel noise-elimination algorithm is proposed for excluding the noise points.Besides,a novel feature matching algorithm based on perspective transformation is proposed.Comparative experimental results indicated the rapidity and effectiveness of the proposed methods.Practical implications–The visual measurement system developed in this paper can be applied to estimate the pose of the drogue with a fast speed and high accuracy and it is a feasible measurement strategy which will considerably increase the autonomy and reliability for AAR.Originality/value–The SBD method is used to detect the features and a novel noise-elimination algorithm is proposed.Besides,a novel feature matching algorithm based on perspective transformation is proposed which is robust and accurate.

Guest editorial

Autonomous control of Unmanned Aerial System(UAS)is that UASs can make decisions in the flight and carry out missions autonomously according to the scheduled tasks and rules through online sensing the surrounding situation.Through cooperation,UASs can share information among individual agents and work together to accomplish complicated mission which would not be possible for a single agent otherwise.With the development of UAS technology,autonomous control has become one of the hot topics and key technologies in UAS research field.

Editorial of Special Issue on UAV Autonomous,Intelligent and Safe Control

The application of unmanned aerial vehicles(UAVs)poses many technique questions to be addressed urgently in the Guidance,Navigation and Control(GNC)-eld.1,2 Complexities and uncertainties with highly nonlinear dynamics of UAVs towards practical use environments to various missions and applications are the main issues and challenges being faced for the development of autonomous,intelligent,reliable,safe and low-cost UAVs.With the unceasing and rapid progress made on the electronics,communication,computation,sensing,actuating,control,arti-cial intelligence and machine learning sciences and technologies,great developments have been achieved for UAV towards autonomous,intelligent and safe control technologies in recent years along with enhancement and new development on UAVs'autonomy,intelligence and safety levels.