In view of the expensive and time-consuming shortcomings of traditional gain scheduling technology in many UAV applications,through the six-degree-of-freedom nonlinear model of a fixed-wing UAV,under the condition of known the aerodynamic coefficient,aircraft moment of inertia and thrust coefficient,the linear parameter varying (LPV) aircraft model is derived from the six-degree-of-freedom nonlinear model by using Jacobian linearization method.The tensor-product (TP) model transformation method is used to transform the longitudinal nonlinear parameter-dependent LPV model of UAV into TP convex polyhedron model.The robust H_∞ controller design method of gain scheduling output feedback is applied to the TP convex polyhedron model,and the robust gain predictive autopilot flight control system of conventional fixed-wing UAV is designed.A full six-degree-of-freedom aircraft simulation test of the control system is carried out in MATLAB SIMULINK environment.The results show that the closed-loop control system has good command followed,interference suppression ability,good stability and robustness in the given flight envelope,which verifies the effectiveness of the proposed flight control system.

Aiming at the problem that the traditional evaluation method of maintenance support personnel’s ability and quality is single,a new method for maintenance support personnel’s ability and quality evaluation model is proposed.The methods and steps of quality evaluation of maintenance support capability of maintenance personnel by principal component analysis method are put forward and the evaluation result is given.The evaluation results obtained through calculation and analysis are objective and reasonable,which verifies that the method has certain credibility and practicability for the evaluation results of the multi-index evaluation system of maintenance support.It provides reference and basis for further strengthening the construction of maintenance personnel team.

Based on the research of the structure and process of unmanned aerial swarm operation,by means of words and figures,four operational concepts that can exist in the future unmanned aerial vehicle (UAV) operations are conceived,namely,to the ground,to the air,to the sea and to the city,as well as possible operational tasks and operational patterns in operations,on which the key technologies of UAV system for completing combat task are analyzed,in order to provide reference for the demonstration and development of UAV equipment.

Firefly algorithm has problems of slow convergence and easy to get into local optimal. To solve the problems, a self-adaptive step firefly algorithm (SASFA) is presented, which replaces fixed search step by self-adaptive step associated with individual density. The algorithm combines self-adaptive step firefly algorithm and the BP neural network, and obtains the initial optimal weights and threshold value of BP neural network by using self-adaptive step firefly algorithm. The initial weights and threshold value are used as the initial parameters of BP neural network to improve the training precision and rate. Taking the penetration effectiveness evaluation of ballistic missile as an example, the penetration effectiveness index system is established, and the penetration effectiveness evaluation model of ballistic missile based on improved BP neural network is built. The example analysis and simulation experiments show that BP neural network optimized by approved firefly algorithm has high accuracy and strong convergence. It can be used in ballistic missile penetration effectiveness evaluation.