One-to-one within-visual-range air combat of unmanned combat aerial vehicles (UCAVs) requires fast, continuous, and accurate decision-making to achieve air combat victory. In order to solve the current problems of insufficient real-time performance of traditional intelligent optimization algorithms for solving decision-making problems and the mismatch between the planning trajectory and the actual flight trajectory caused by the difference between the decision-making model and the actual aircraft model, this paper proposes a hierarchical on-line air combat maneuvering decision-making and control framework. Considering the real-time constraints, the maneuver decision problem is transformed into an expensive optimization problem at the decision planning layer. The surrogate-assisted differential evolution algorithm is proposed on the basis of the original differential evolution algorithm, and the planning trajectory is obtained through the 5 degrees of freedom (DOF) model. In the control execution layer, the planning trajectory is tracked through the nonlinear dynamic inverse tracking control method to realize the high-precision control of the 6DOF model. The simulation is carried out under four different initial situation scenarios, including head-on neutral, dominant, parallel neutral, and disadvantaged situations. The Monte Carlo simulation results show that the Surrogate-assisted differential evolution algorithm (SADE) can achieve a win rate of over 53% in all four initial scenarios. The proposed maneuver decision and control framework in this article achieves smooth flight trajectories and stable aircraft control, with each decision average taking 0.08 s, effectively solving the real-time problem of intelligent optimization algorithms in maneuver decision problems.

The unmanned aerial vehicle (UAV) pursuit–evasion game is the fundamental framework for promoting autonomous decision-making and collaborative control of multi-UAV systems. Faced with the limitations of current deep reinforcement learning methods in terms of transferability and generalization for scalable multi-fixed-wing UAV pursuit–evasion game scenarios, this paper proposes a hierarchical collaborative pursuit–evasion game framework based on target allocation and hierarchical reinforcement learning. The framework comprises three layers: target allocation layer, maneuver decision-making layer, and flight control layer. The target allocation layer employs a dynamic target assignment method based on a dynamic value adjustment mechanism, decomposing the multi-vs.-multi pursuit–evasion game into several one-vs.-one confrontations. The maneuver decision-making layer utilizes a maneuver decision-making method based on trajectory prediction and hierarchical reinforcement learning to generate adversarial maneuver commands. The flight control layer adopts a stable gradient-assisted reinforcement learning flight controller to ensure stable UAV flight. Comparisons with other algorithms across 3V3, 6V6, 9V9, and 12V12 scenarios demonstrate that the proposed method achieves high win rates in diverse game scales. The comparison results also demonstrate the advantages of the framework proposed in this paper in terms of training efficiency and large-scale scalability.