Multi-UAV cooperative air combat has attracted wide attention from relative scholars. However, the decision-making problem of UAV swarm confrontation under uncertain conditions makes it more difficult. In this article, a two-layer decision-making method, containing dynamic target assignment and distributed Monte Carlo tree search (MCTS), is proposed to address this issue. Additionally, the possibility degree function method of interval gray number is combined with a genetic algorithm to deal with uncertain information in an air combat environment. Specifically, considering the actual air combat scene, the target value factor is introduced in the target allocation process, and the dynamic target allocation mechanism is established to adjust the cluster combat strategy in real time. The experiments show that the proposed two-level decision-making method can effectively deal with the swarm air combat problem under uncertain environments. First, the improved genetic algorithm can solve the problem of target allocation in an uncertain environment and give the target allocation scheme in the current state. Moreover, the establishment of the dynamic target allocation mechanism makes the cooperative behavior of UAVs emerge in the swarm, which fully reflects the adversarial air combat.

In the process of air combat intention identification, expert experience and traditional algorithm are relied on to analyze enemy aircraft combat intention in a single moment, but the identification time and accuracy are not excellent. In this paper, from the dynamic attributes of an airspace fighter air combat target and the dynamic and time series changing characteristics of the battlefield environment, we introduce the bidirectional long short-term memory neural network (BiLSTM + Attention) intention identification method based on the attention mechanism for air combat intention identification. In this method, five kinds of state parameters, including target maneuver type, distance, flight velocity, altitude and heading angle, were taken as datasets. The BiLSTM + Attention was used to extract enemy aircraft intention features. By introducing attention mechanism, the weight coefficients of characteristic states corresponding to air combat victories were corrected. Finally, it was input into the SoftMax function to obtain the category of the enemy’s intention. Experimental results showed that the proposed method can effectively identify enemy aircraft in the case of high complexity, multidimensional and large amount of data. Compared with bidirectional long short-term memory (BiLSTM), long short-term memory (LSTM), long short-term memory based on attention mechanisms (LSTM + Attention) and support vector machine (SVM) classification, the proposed method had higher accuracy and lower loss value.

In this paper, a model for maneuver decisions in air combat is established based on position situation information, the performance of the fighter, the threat of combat intention, and the multi-fighter collaboration effect. Additionally, a two-layer game decision algorithm based on the double game tree distributed Monte Carlo search strategy is proposed, and the operational rules of interval numbers and possibility degree comparison rules are adopted to solve the designed method. The experiment results show that the model and algorithm are effective in their intended purpose. The two-layer game decision-making and distributed double game tree MCTS can precut the huge game tree strategy space and quickly identify the optimal air combat game decision scheme, which improves the efficiency of strategy searches. By compared experiments, it was found that the proposed algorithm can improve the performance of air combat.

To address the control of uncertain multi-agent systems (MAS) with completely unknown system nonlinearities and unknown control coefficients, a global consensus method is proposed by constructing novel filters and barrier function-based distributed controllers. The main contributions are as follows. Firstly, a novel two-order filter is designed for each agent to produce informational estimates from the leader, such that a connectivity matrix is not used in the controller’s design, solving the difficultly caused by the time-varying control coefficients in a MAS with a directed graph. Secondly, combined with the novel filters, barrier functions are used to construct the distributed controller to deal with the completely unknown system nonlinearities, resulting in the global consensus of the MAS. Finally, it is rigorously proved that the consensus of the MAS is achieved while guaranteeing the prescribed tracking-error performance. Two examples are given to verify the effectiveness of the proposed method, in which the simulation results demonstrate the claims.