## 1 Introduction
In the actual BVR air combat, to effectively connect the above-mentioned two missions of UCAVs occupying a superior situation and BVR AAMs' guidance, it is necessary to have a crucial process of attack guidance and aiming of the UCAV. The best launching conditions of the BVR AAM can be formed through the process of attack guidance and aiming, which can also give full play to the combat effectiveness of the BVR AAM to the greatest extent. If the aiming error angle of the output of the fire control system [20] is too large, the BVR AAM cannot meet the launching conditions, cannot capture the target, or cannot effectively destroy the target. Despite its crucial position in a modern BVR air combat, this specific problem has received less attention than it should in the open literature. In [21], a novel terminal attack guidance method was proposed for a suicide UCAV in ground attack missions. An attack guidance law based on the virtual pursuit point concept for UCAVs in a dogfight was investigated in [22]. To eliminate the aiming error angle in the BVR air combat, the aiming mode of manned fighters is to maneuver through the manual control of the pilots, which is inefficient and obviously not suitable for autonomous UCAVs. The traditional automatic attack guidance method based on the integrated flight/fire control system [23; 24; 25] relies too much on the set of control parameters by artificial expertise, and it is difficult to adapt to all air combat situations, and the aiming precision also could not meet the requirements when intercepting the highly maneuvering target. Furthermore, in [26; 27], the genetic algorithm and the simulated annealing particle swarm algorithm were respectively used to optimize the control parameters of attack guidance, and good control effects were achieved. However, these intelligent optimization algorithms could not meet the timeliness requirements of the fire control system. When designing the BVR attack guidance method of UCAVs, the classical control theory represented by PID [28] and fuzzy control [29] is time-consuming and difficult to adjust and optimize the controller. Therefore, evolutionary algorithms such as artificial bee colony [30], particle swarm optimization [31], and evolving fuzzy neural system [32] have been used to optimize the design of these controllers. However, the attack guidance system is a highly dynamic complex system with nonlinear factors and no accurate mathematical model. Besides, the system usually has high standards of milliradian level aiming precision and millisecond level timeliness. Hence, it is obvious that the above methods are not sufficient to meet the requirements. To address this problem, this study gives full consideration to the requirements of high timeliness and high precision, and at the same time reduces the dependence of control parameters on artificial expertise. An adaptive fuzzy control (AFC) [33; 34] method based on the model predictive control (MPC) framework [35; 36] is proposed. Firstly, the aiming error angle is calculated based on the motion model of UCAV and the aiming model of BVR attack fire control in this study, then target motion prediction information is introduced based on the designed MPC framework, and the adaptive fuzzy guidance controller is designed to generate control variable. To reduce the predicted aiming error angle, the algorithm iteratively optimizes and updates the actual guidance control variable online. The rest of this paper is organized as follows. Section 2 presents the problem analysis and formulation, which includes the motion model of UCAV and the aiming model of BVR attack fire control. Then, the details of the proposed autonomous attack guidance method are presented in Section 3. The proposed models and algorithm are demonstrated with simulation experiments in Section 4, followed by conclusion and future work in Section 5.