Identification and Stochastic Optimizing the UAV Motion Control in Turbulent Atmosphere
In a class of diffusion Markov processes, we formulate a problem of
identification of nonlinear stochastic dynamic systems with random
parameters, multiplicative and additive noises, control functions, and the state
vector at a final time moment. For such systems, the identifiability conditions
are being studied, and necessary conditions are formulated in terms of the
general theory of extreme problems. The developed engineering methods for
identification and optimizing nonlinear stochastic systems are presented as
well as their application for unmanned aerial vehicles under wind
disturbances caused by atmospheric turbulence, namely, for optimizing the
autopilot parameters during a rotary maneuver of an unmanned aerial vehicle
in translational motion, taking into account the identification of its angular
Unmanned Aerial Vehicle
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