Electric propulsion technology has attracted much attention in the aviation industry at present. It has the advantages of environmental protection, safety, low noise, and high design freedom. An important research branch of electric propulsion aircraft is electric vertical takeoff and landing (VTOL) aircraft, which is expected to play an important role in urban traffic in the future. Limited by battery energy density, all electric unmanned aerial vehicles (UAVs) are unable to meet the longer voyage. Series/parallel hybrid-electric propulsion and turboelectric propulsion are considered to be applied to VTOL UAVs to improve performances. In this paper, the potential of these four configurations of electric propulsion systems for small VTOL UAVs are evaluated and compared. The main purpose is to analyze the maximum takeoff mass and fuel consumption of VTOL UAVs with different propulsion systems that meet the same performance requirements and designed mission profiles. The differences and advantages of the four types propulsion VTOL UAV in the maximum takeoff mass and fuel consumption are obtained, which provides a basis for the design and configuration selection of VTOL UAV propulsion system.

With the rapid development of informatization, autonomy and intelligence, unmanned swarm formation intelligent operations will become the main combat mode of future wars. Typical unmanned swarm formations such as ground-based directed energy weapon formations, space-based kinetic energy weapon formations, and sea-based carrier-based formations have become the trump card for winning future wars. In a complex confrontation environment, these sophisticated weapon formation systems can precisely strike mobile threat group targets, making them extreme deterrents in joint combat applications. Based on this, first, this paper provides a comprehensive summary of the outstanding advantages, strategic position and combat style of unmanned clusters in joint warfare to highlight their important position in future warfare. Second, a detailed analysis of the technological breakthroughs in four key areas, situational awareness, heterogeneous coordination, mixed combat, and intelligent assessment of typical unmanned aerial vehicle (UAV) swarms in joint warfare, is presented. An in-depth analysis of the UAV swarm communication networking operating mechanism during joint warfare is provided to lay the theoretical foundation for subsequent cooperative tracking and control. Then, an in-depth analysis of the shut-in technology requirements of UAV clusters in joint warfare is provided to lay a theoretical foundation for subsequent cooperative tracking control. Finally, the technical requirements of UAV clusters in joint warfare are analysed in depth so the key technologies can form a closed-loop kill chain system and provide theoretical references for the study of intelligent command operations.

Optimal periodic control is an effective technique to reduce aerodynamic heating and fuel consumption of hypersonic cruise vehicle. Herein this optimal control problem has been solved by nonlinear programming and passed the posteriori check of optimality. The results indicate that lift coefficient and velocity in dynamical model could be recognized as constants. Accordingly, the original 3D system consists of velocity, flight path angle, and altitude could be decoupled into a 2D subsystem and an 1D one. The 2D subsystem could describe the correlation between flight path angle and altitude. And the period length is mainly dependent on the maximum altitude difference. The 1D subsystem has been proved to be feasible to describe the variation of kinetic energy or velocity. On basis of the subsystems, the heating and fuel performances of periodic cruise have been studied. The heating performance is mainly dependent on maximum altitude difference, and the fuel consumption is mainly dependent on the drag coefficient. The features concluded in this work can support rapid trajectory planning or suboptimal feedback design of hypersonic cruise vehicle.

The design parameters of high-altitude solar-powered aircraft are highly correlative with its flight trajectory. However, it is not an easy work to jointly optimize them in the concept design stage. This paper considers the joint optimization problem of battery mass and flight trajectory for high-altitude solar-powered aircraft. The system model including the aircraft dynamic model, aerodynamic parameters, and thrust model is presented. Then the problem to be optimized is formulated and a new optimization method, which uses the particle swarm optimization and Gauss pseudo-spectral method, is proposed. The Gauss pseudo-spectral method is employed to generate the minimal power consumed by following the flight trajectory in the given configuration of high-altitude solar-powered aircraft, while the particle swarm optimization is used to calculate the optimal battery mass of aircraft. The simulation result shows that the proposed joint optimization method can reduce the battery mass of high-altitude solar-powered aircraft from 16 kg to 13.6 kg, which is equivalent to enhancing its energy density by 19.7%. It can be also seen that the proposed optimization method connects each parameter in a logically clear way and hence provide a perspective for understanding the optimization problem.

Nowadays, unmanned aerial vehicles (UAVs) pose a significant challenge to air defense systems. Unmanned combat aerial vehicles (UCAVs) have been proven to be an effective method to counter the threat of UAVs in application. Therefore, maneuver decision-making has become the crucial technology to achieve autonomous air combat for UCAVs. In order to solve the problem of maneuver decision-making, an autonomous model of UCAVs based on the deep reinforcement learning method was proposed in this paper. Firstly, the six-degree-of-freedom (DoF) dynamic model was built in three-dimensional space, and the continuous actions of tangential overload, normal overload, and roll angle were selected as the maneuver inputs. Secondly, to improve the convergence speed for the deep reinforcement learning method, the idea of “scenario-transfer training” was introduced into the twin delayed deep deterministic (TD3) policy gradient algorithm, the results showing that the improved algorithm could cut off about 60% of the training time. Thirdly, for the “nose-to-nose turns”, which is one of the classical maneuvers for experienced pilots, the optimal maneuver generated by the proposed method was analyzed. The results showed that the maneuver strategy obtained by the proposed method was highly consistent with that made by experienced fighter pilots. This is also the first time in a public article that compared the maneuver decisions made by the deep reinforcement learning method with experienced fighter pilots. This research can provide some meaningful references to generate autonomous decision-making strategies for UCAVs.