Autonomous Maneuver Decision-Making for Unmanned Combat Aerial Vehicle Based on Modified Marine Predator Algorithm and Fuzzy Inference

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.