The invention discloses a small-size unmanned
rotary wing aircraft dynamic model identification method based on an adaptive
genetic algorithm, which relates to flight status
data acquisition and optimization, dynamic
model building and parameter identification, and parameter optimization validation. Firstly, status data and
control data when a small-size unmanned
rotary wing aircraft executes standard actions are acquired through a
data acquisition system, and
smoothing and
filtration are conducted to eliminate wild values; then aiming at the operating characteristics of the small-size unmanned
rotary wing aircraft at autonomous
takeoff and landing stages, a small-size unmanned rotary wing aircraft dynamic model is built through a
balance point linearization method and
model parameters are identified through the adaptive
genetic algorithm; and finally intelligent parameter evaluation indexes are built and the effectiveness of the
model parameters is evaluated and judged through a one-step predication method. The small-size unmanned rotary wing aircraft dynamic model identification method based on the adaptive
genetic algorithm solves the problem in the dynamic model identificationof the small-size unmanned rotary wing aircraft, can realize the high-accuracy control of the small-size unmanned rotary wing aircraft, and has the advantages of low testing cost,
short cycle, simplecalculation, high dynamic model accuracy and weak dependence on initial values.