In this paper, an autonomous UCAV air combat maneuvering decision method based on LSHADE-TSO optimization in a model predictive control framework is proposed, along with enemy trajectory prediction. First, a sliding window recursive prediction method for multi-step enemy trajectory prediction using a Bi-LSTM network is proposed. Second, Model Predictive Control (MPC) theory is introduced, and when combined with enemy trajectory prediction, a UCAV maneuver decision model based on the MPC framework is proposed. The LSHADE-TSO algorithm is proposed by combining the LSHADE and TSO algorithms, which overcomes the problem of traditional sequential quadratic programming falling into local optimum when solving complex nonlinear models. The LSHADE-TSO-MPC air combat maneuver decision method is then proposed, which combines the LSHADE-TSO algorithm with the MPC framework and employs the LSHADE-TSO algorithm as the optimal control sequence solver. To validate the effectiveness of the maneuvering decision method proposed in this paper, it is tested against the test maneuver and the LSHADE-TSO decision algorithm, respectively, and the experimental results show that the maneuvering decision method proposed in this paper can beat the opponent and win the air combat using the same weapons and flight platform. Finally, to demonstrate that LSHADE-TSO can better exploit the decision-making ability of the MPC model, LSHADE-TSO is compared to various optimization algorithms based on the MPC model, and the results show that LSHADE-TSO-MPC can not only help obtain air combat victory faster but also demonstrates better decision-making ability.

In this paper, a novel approach to solving the formation online collaborative trajectory planning for fixed-wing Unmanned Combat Aerial Vehicles (UCAVs) is proposed. In order to describe the problem, the formation attack process which consists of communication framework and synergy elements is analyzed. The collaborative trajectory planning model which is based on avoiding the threat zones, reducing the execution time, and accomplishing the mission combines kinematics/dynamics model of UCAV with formation relative motion model to establish the optimal control problem. The approach based on hp adaptive pseudospectral method is presented to generate formation trajectory that satisfies the collaborative constraints. When a trigger event is detected, based on the offline planning, the online collaborative trajectory replanning using rolling horizon strategy is carried out. Simulated experiments which are divided into offline scenarios and online scenarios demonstrate that the proposed approach can generate trajectories which can meet the actual flight constraints, and the results verify the feasibility and stability of the proposed approach.

In this paper, we propose IJADE-TSO, a novel hybrid algorithm in which an improved adaptive differential evolution with optional external archive (IJADE) has been combined with the tuna swarm optimization (TSO). The proposed algorithm incorporates the spiral foraging search and parabolic foraging search of TSO into the mutation strategy in IJADE to improve the exploration ability and population diversity. Additionally, to enhance the convergence efficiency, crossover factor (CR) ranking, CR repairing, top α r₁ selection, and population linear reduction strategies have been included in the algorithm. To evaluate the superiority of the proposed algorithm, IJADE-TSO has been benchmarked with its state-of-the-art counterparts using the CEC 2014 test set. Finally, to check the validity of IJADE-TSO, we apply it to photovoltaic (PV) parameter identification and compare its performance with those of other recently developed well-known algorithms. The statistical results reveal that IJADE-TSO outperforms the other compared algorithms.

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.

Targeting the autonomous decision-making problem of unmanned combat aerial vehicles (UCAVs) in a two-versus-one (2v1) within-visual-range (WVR) air combat scenario, this paper proposes a maneuver decision-making method based on tactical coordination. First, a coordinated situation assessment model is designed, which subdivides the air combat situation into optimization-driven and tactical coordinated situations. The former combines missile attack zone calculation and trajectory prediction to optimize the control quantity of a single aircraft, while the latter uses fuzzy logic to analyze the overall situation of the three aircraft to drive tactical selection. Second, a decision-making model based on a hierarchical expert system is constructed, establishing a hierarchical decision-making framework with a UCAV-coordinated combat knowledge base. The coordinated situation assessment results are used to match corresponding tactics and maneuver control quantities. Finally, an improved particle swarm optimization algorithm (I-PSO) is proposed, which enhances the optimization ability and real-time performance through the design of local social factor iterative components and adaptive adjustment of inertia weights. Air combat simulations in four different scenarios verify the effectiveness and superiority of the proposed decision-making method. The results show that the method can achieve autonomous decision making in dynamic air combat. Compared with decision-making methods based on optimization algorithms and differential games, the win rate is increased by about 17% and 18%, respectively, and the single-step decision-making time is less than 0.02 s, demonstrating high real-time performance and win rate. This research provides new ideas and methods for the autonomous decision making of UCAVs in complex air combat scenarios.

In recent years, autonomous maneuver decision-making has emerged as a key technology in autonomous air combat confrontation, garnering widespread attention. A method combining the modified marine predator algorithm (MMPA) and fuzzy inference is proposed to solve the autonomous maneuver decision-making problem of an unmanned combat aerial vehicle (UCAV). By incorporating the missile attack strategy into the process of calculating the maneuver strategy, the air combat decision-making capability of the UCAV is enhanced. First, the weight coefficients determined by the fuzzy inference method are combined with air combat superiority functions that consider the current missile attack zone and then the objective function is obtained, which is to be optimized at the current moment. Second, the MMPA is used to solve the objective function to obtain the missile attack maneuver strategy and the maneuver strategy for defending against missile attacks. A comparative analysis with other classical intelligent optimization algorithms highlights the advantages of the proposed method. Furthermore, the air combat confrontation simulation experiments are conducted under six different initial scenarios, namely, neutral, offensive, oppositional, defensive, parallel, and head-on. The simulation results show that the integrated maneuver and missile attack decision-making capabilities of the UCAV are improved using the proposed autonomous maneuver decision-making method.