Online learning method and system for unmanned helicopter model

Abstract

The invention discloses an online learning method and system for an unmanned helicopter model, and belongs to the technical field of unmanned aerial vehicles, and the method comprises the steps: building an unmanned helicopter model with uncertain items according to a fixed world reference coordinate system and a body-following coordinate system built through taking the mass center of an unmannedhelicopter as an original point; training the two residual convolution sub-networks by using historical flight data of the unmanned helicopter to obtain two trained sub-networks; constructing a deep continuous learning network model by using the two trained sub-networks and a dynamic Hadamard product layer; and learning uncertain items in the unmanned helicopter model by using the deep continuouslearning network model to realize online dynamic learning of the unmanned helicopter model. According to the method, the historical data flow online dynamic learning model of the unmanned helicopter can be utilized to realize online multi-step prediction of various stunt action states, and the method has good generalization ability and good robustness.

Description

technical field [0001] The invention relates to the technical field of unmanned aerial vehicles, in particular to an online learning method and system for an unmanned helicopter model based on a deep continuous learning network. Background technique [0002] Unmanned aerial vehicles can be divided into fixed-wing, multi-rotor, compound-wing and single-rotor UAVs (also known as unmanned helicopters) with tail rotors according to their structure. By adjusting the variable pitch system, the unmanned helicopter can achieve aerobatics such as flipping, rolling, and fixed-point hovering. Due to its good flight performance, it has a wide range of application scenarios in military and civilian fields. The flexible and highly maneuverable characteristics of unmanned helicopters make them suitable for low-altitude flight over complex terrain in the military field, while in the civilian field, UAVs can be used in agricultural automation, geographic information detection, stunt perform...

AI Technical Summary

Problems solved by technology

[0006] However, none of the above methods take into account that during the actual operation of the unmanned helicopter system, the model has time-varying properties due to external aerodynamic effects, and online dynamic learning is required.

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