This paper studies the cooperative control of multiple unmanned aerial vehicles (UAVs) with sensors and autonomous flight capabilities. In this paper, an architecture is proposed that takes a small quadrotor as a mission UAV and a large six-rotor as a platform UAV to provide an aerial take-off and landing platform and transport carrier for the mission UAV. The design of a tracking controller for an autonomous docking and landing trajectory system is the focus of this research. To examine the system’s overall design, a dual-machine trajectory-tracking control simulation platform is created via MATLAB/Simulink. Then, an autonomous docking and landing trajectory-tracking controller based on radial basis function proportional–integral–derivative control is designed, which fulfills the trajectory-tracking control requirements of the autonomous docking and landing process by efficiently suppressing the external airflow disturbance according to the simulation results. A YOLOv3-based vision pilot system is designed to calibrate the rate of the aerial docking and landing position to eight frames per second. The feasibility of the multi-rotor aerial autonomous docking and landing technology is verified using prototype flight tests during the day and at night. It lays a technical foundation for UAV transportation, autonomous take-off, landing in the air, and collaborative networking. In addition, compared with the existing technologies, our research completes the closed loop of the technical process through modeling, algorithm design and testing, virtual simulation verification, prototype manufacturing, and flight test, which have better realizability.

This study conducted an in-depth investigation of the tracking controller of an aerial autonomous docking and landing trajectory system. To examine the system’s overall design, a dual-machine trajectory-tracking control simulation platform was created via MATLAB Simulink. Then, an autonomous docking and landing trajectory-tracking controller based on backpropagation proportional–integral–derivative control was designed, which fulfilled the trajectory-tracking control requirements in the autonomous docking and landing process by efficiently suppressing the external airflow disturbance according to the simulation results. A YOLOv3-based vision pilot system was designed to calibrate the rate of the aerial docking and landing position to eight frame per second. The feasibility of the multi-rotor aerial autonomous docking and landing system was verified using prototype flight tests during the day and at night.

In the past decade, how improving the fidelity of maneuver gaming and the synergy of target allocation has become key issues in cooperative autonomous air combat researches. To address these two problems, this study proposes a maneuver decision-making algorithm based on an optimized dynamic Bayesian network and a target allocation decision-making algorithm based on an optimized hybrid particle swarm optimization. In maneuvering decision-making, the state transition's reliability and the air combat's autonomy are enhanced through considering the effect of sliding mode control. The Bayesian network is improved through introducing a strategy for dynamic prior probability updating. The computation is reduced and the efficiency is increased through pruning the minimax search tree according to visual prediction. In target allocation decision-making, the algorithm's convergence speed is greatly accelerated and the solution's global optimality is improved through introducing immigrant particles. The algorithm's application scope is expanded through proposing a solution principle about unequal quantity combat situations. Furthermore, the end criterion is specially designed to fit real-world combats through introducing a fire control. The simulation results show that the designed decision-making algorithms are more effective in solving the problem of cooperative autonomous air combat, which shows that the various improvements introduced in this study are reasonable and effective.INDEX TERMS Cooperative autonomous air combat, maneuver decision-making, target allocation decision-making, sliding mode controller, dynamic Bayesian network, hybrid particle swarm optimization.