Aiming at the problems of strong nonlinearity, strong coupling, and unknown interference
encountered in the flight control process of unmanned aerial vehicles (UAVs) in a complex …

This study presents an innovative reinforcement-learning-based control algorithm for a
vertical take-off and landing (VTOL) aircraft under wind disturbances. In our approach, the …

Tracking maneuvering target in real time autonomously and accurately in an uncertain
environment is one of the challenging missions for unmanned aerial vehicles (UAVs). In this …

In this article, we propose a novel deep reinforcement learning (DRL) approach for
controlling multiple unmanned aerial vehicles (UAVs) with the ultimate purpose of tracking …

This paper combines deep reinforcement learning (DRL) with meta-learning and proposes a
novel approach, named meta twin delayed deep deterministic policy gradient (Meta-TD3), to …

In this paper, a novel deep reinforcement learning (DRL) method, and robust deep
deterministic policy gradient (Robust-DDPG), is proposed for developing a controller that …

Complex 3D obstacle environments raise high requirements of the rapid reaction capability
and safety aiming at the maneuver control for obstacle avoidance (MCOA) of fixed-wing …

In this paper, we focus on the study of UAV ground target tracking under obstacle
environments using deep reinforcement learning, and an improved deep deterministic policy …

Enclosing a maneuverable target for Unmanned Aerial Vehicles (UAVs) in a constrained
environment is intractable as existing methods fail to coordinate collision safety and tracking …

Past few years have witnessed the widespread adoption of unmanned aerial vehicles
(UAVs) in target tracking for regional monitor and strike. Most existing target tracking …