Focusing on the differentiated and combat needs of maintenance support capability evaluation for an early warning detection equipment, the process and influencing factors of maintenance support for this type of equipment are analyzed, and the four level maintenance support capability evaluation index is constructed. Based on analytic hierarchy process (AHP) and fuzzy mathematics theory, the construction and solving process of multi-level fuzzy comprehensive evaluation model are studied. Then, the key technical problems such as different indexes normalization are discussed. Finally, the maintenance support capability evaluation system is researched and developed, which can provide decision support for equipment maintenance support improvement.
KEYWORDS: Unmanned aerial vehicles, Receivers, Control systems, Control systems design, 3D modeling, Virtual reality, Numerical simulations, Kinematics, Visual guidance for autonomous vehicles
The rendezvous guidance problem between a receiver unmanned aerial vehicle (UAV) and a virtual tanker was researched. In order to drive elevation angle and azimuth angle of the UAV to satisfy both constraints, two sliding mode guidance laws were designed to control them, respectively. The attitude angle commands were produced by the transformation relationships. The flight control system (FCS) was designed to control the UAV to track the virtual tanker. The FCS was divided into the attitude angles control system and the velocity control system. The attitude angles control system produced the desired fin deflections. The simulation results demonstrate that the designed sliding mode guidance laws and the FCS control the UAV to complete favourably the rendezvous process.
In order to improve the collision avoidance capability of unmanned aerial vehicle (UAV) formation, a new kind of UAV formation control algorithm was developed based on combining the consensus control and the artificial potential field (APF). The formation controller consisted of three sub-controllers. The first was a distributed consensus controller that controlled UAVs to maintain the desired formation shape. The second was an APF controller that avoided collisions among UAVs. The third was an APF controller that avoided collisions between UAVs and obstacles in the task space. Numerical simulation was performed to validate the effectiveness of the designed UAV formation controller. The simulation results show that 5 UAVs can safely avoid collisions with each other and collisions with the obstacles during their formation flight. Moreover, they can recover desired distance and desired shape after they avoid the obstacles.
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