Autonomous Air Combat Maneuvering Decision Method of UCAV Based on LSHADE-TSO-MPC under Enemy Trajectory Prediction

Abstract

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.

UCAV autonomous maneuvering decision based on curriculum learning mechanism training

“…Although the algorithm is easy to understand, the real-time performance is poor, so it is difficult to apply to UCAV autonomous air combat. Literature [5] applied the influence diagram method to maneuver decision-making. Although it can effectively guide UCAV combat, the model structure is complex, the calculation is cumbersome and the real-time performance is poor.…”

UAV Air Game Maneuver Decision-Making Using Dueling Double Deep Q Network with Expert Experience Storage Mechanism

“…This method designed a robust multi-objective optimization function grounded in state function and statistical principles and utilized the grey wolf optimizer to solve real-time optimization issues. Tan et al [22] introduced the Model Predictive Control (MPC) theory and introduced the LSHADE-TSO-MPC air game maneuver decision-making method, which predicted target UAV trajectories and accelerated air game victories. Ruan et al [23] combined the bionic intelligent optimization algorithm with air game decision-making problems and designed a transfer-learning pigeon-inspired optimization model that demonstrated good convergence, high precision, and reasonable maneuver decisions in one-on-one dogfights.…”