DYNAMIC MOTION MODEL OF AN UNMANNED AERIAL VEHICLE FOR CONTROL AND NAVIGATION TASKS
DOI:
https://doi.org/10.18372/2310-5461.68.20451Keywords:
unmanned aerial vehicle, wind disturbances, mathematical model, stability, automatic controlAbstract
Relevance. The use of unmanned aerial vehicles (UAVs) in civil and military applications is rapidly increasing, but their effectiveness significantly decreases under the influence of gusts, wind shear, and turbulence. This problem is especially critical for monitoring, mapping, search and rescue operations, and delivery tasks, where accuracy, reliability, and energy efficiency are crucial. In such conditions, standard control schemes that do not account for atmospheric disturbances are effective only under weak influences, limiting functionality and reducing operational safety. Thus, there is a growing need for adequate mathematical models and modern information technologies capable of compensating the negative effects of wind.
Problem statement. Traditional simplified flight models and standard controllers ensure stability only under weak disturbances, which does not meet the requirements for accuracy and reliability. One of the key prerequisites for developing advanced information technologies for flight control is the construction of a mathematical model of a UAV that correctly accounts for wind effects in its motion equations.
Solution approach. The paper proposes a dynamic model of UAV motion that describes its behavior in six degrees of freedom. The model includes projections of gravity, thrust, and aerodynamic forces onto velocity, normal, and trajectory axes, and incorporates wind disturbances through the relationship between airspeed and ground speed. This approach enables the linearization of equations, decomposition into longitudinal and lateral motion subsystems, and derivation of transfer functions necessary for the analysis and synthesis of automatic control systems.
Results. The developed model makes it possible to study longitudinal and lateral disturbed motion, evaluate stability, and assess the quality of transient processes in control systems. It provides a formalized basis for integrating wind models, testing different flight scenarios, and comparing methods of disturbance compensation.
Conclusions. The proposed model is a scientific and practical foundation for designing information technologies for UAV flight control under wind disturbances. It enables the development of robust and predictive control algorithms that improve accuracy, energy efficiency, and reliability of UAV missions under challenging atmospheric conditions.
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