As one of the tiniest flying objects, unmanned aerial vehicles (UAVs) are often expanded as the ``swarm'' to execute missions. In this article, we investigate the multiquadcopter and target pursuit-evasion game in the obstacles environment. For high-quality simulation of the urban environment, we propose the pursuit-evasion scenario (PES) framework to create the environment with a physics engine, which enables quadcopter agents to take actions and interact with the environment. On this basis, we construct multiagent coronal bidirectionally coordinated with target prediction network (CBC-TP Net) with a vectorized extension of multiagent deep deterministic policy gradient (MADDPG) formulation to ensure the effectiveness of the damaged ``swarm'' system in pursuit-evasion mission. Unlike traditional reinforcement learning, we design a target prediction network (TP Net) innovatively in the common framework to imitate the way of human thinking: situation prediction is always before decision-making. The experiments of the pursuit-evasion game are conducted to verify the state-of-the-art performance of the proposed strategy, both in the normal and antidamaged situations.

Due to the characteristics of the small size and low cost of unmanned aerial vehicles (UAVs), Multi-UAV confrontation will play an important role in future wars. The Multi-UAV confrontation game in the air combat environment is investigated in this paper. To truly deduce the confrontation scene, a physics engine is established based on the Multi-UAV Confrontation Scenario (MCS) framework, enabling the real-time interaction between the agent and environment while making the learned strategies more realistic. To form an effective confrontation strategy, the Graph Attention Multi-agent Soft Actor Critic Reinforcement Learning with Target Predicting Network (GA-MASAC-TP Net) is firstly proposed for Multi-UAV confrontation game. The merits lie in that the Multi-UAV trajectory prediction, considering interactions among targets, is incorporated innovatively into the Multi-agent reinforcement learning (MARL), enabling Multi-UAVs to make decisions more accurately based on situation prediction. Specifically, the Soft Actor Critic (SAC) algorithm is extended to the Multi-agent domain and embed with the graph attention neural network into the Actor, Critic network, so the UAV could aggregate the information of the spatial neighbor teammates based on the attention mechanism for better collaboration. The comparative experiment and ablation study demonstrate the effectiveness of the proposed algorithm and the state-of-art performance in the MCS.

The attitude synchronization problem for multiple spacecraft with angular velocity and input constraints is investigated in this article, under the mild assumptions that the leader’s states can reach each follower through a path and the communications between followers are bidirectional. An adaptive finite-time distributed observer is designed to estimate the leader’s states for each follower spacecraft, which has the superiority in the fully distributed that the communication topology or some other global information is not required, and the finite-time convergence can be guaranteed. Furthermore, the novel auxiliary systems are first proposed to avoid the angular velocity and input constraints being violated, while the finite-time command filtered backstepping controller is designed to track the leader’s attitude motion. The remarkable features of the developed algorithm are the global finite-time attitude consensus of multiple spacecraft, with the constraints satisfied. Moreover, the efficiency of the proposed method is illustrated by numerical simulations and real-time platform verification.