Unmanned aerial vehicle flight control method

Abstract

The invention belongs to the technical field of unmanned aerial vehicle flight control, and particularly relates to an unmanned aerial vehicle flight control method. The method comprises the steps of:setting a maneuvering mode of an unmanned aerial vehicle as a shooting interception maneuvering mode during long-distance engagement; setting the maneuvering mode of the unmanned aerial vehicle as anoffset maneuvering mode after ammunition is shot; setting the maneuvering mode of the unmanned aerial vehicle as a cross-cut maneuvering mode while elude the locking of the enemy; and setting the maneuvering mode of the unmanned aerial vehicle as an escape maneuvering mode, a cocking looping maneuvering mode or a breaking S maneuvering mode when eluding the attack of a guided missile.

Description

technical field [0001] The application belongs to the technical field of unmanned aerial vehicle flight control, and in particular relates to a method for controlling unmanned aerial vehicle flight. Background technique [0002] The introduction of artificial intelligence in UAV control can adapt to and respond to complex and changing environments, and automatically generate control behaviors, so as to get rid of human physiological and psychological limitations, without physical limits, and always maintain rapid and accurate responses. [0003] The key to introducing artificial intelligence into the control of UAVs is to rationalize dimensionality reduction and build complex control models. UAVs in complex environments have hundreds of variables related to high-dynamic flight, reflecting the over-dimensionality of the flight scene control model. If there are too many dimensions, it is difficult to achieve convergence when training it, so that the training cannot be complete...

AI Technical Summary

Problems solved by technology

[0003] The key to introducing artificial intelligence into the control of UAVs is to rationalize dimensionality reduction and build complex control models. UAVs in complex environments have hundreds of variables related to high-dynamic flight, reflecting the over-dimensionality of the flight scene control model. If there are too many dimensions, it is difficult to achieve convergence when training it, so that the training cannot be completed; if the number of dimensions is too small, the information of branch reduction is too much, and it is difficult to achieve the effect of intelligent control with high real-time and high precision

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