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The article presents a methodology for the development of information exchange tools based on the apparatus of hierarchical Petri nets, characterized by the possibility of simultaneous events and allows solving the problem of correctness analysis and verification of protocols. Protocol objects and access points are represented on the basis of the Petri nets apparatus: the simplest object of the reception/transmission protocol, the queue object, a single object and a trivial object. It describes the construction of complex structures of objects and operations on them, the management of timeouts based on the timer object, the specification of protocol objects, the specification of protocols and levels of logical structure. The proposed method is relevant for the analysis of complex communication systems of large size, which are mobile ad-hoc networks with a large number of nodes, characterized by topology instability due to unstable communication channel characteristics and high nodes mobility, and especially flying ad-hoc networks of aircraft (FANET) and flying sensor networks (FSN).

The use of a formation consisting of adaptive autonomous mobile agents allows solving a wide range of tasks that are often beyond the capabilities of individual agents. A multi-agent formation is a complex high-order dynamic system, so analyzing the stability of such a system is a complex task. At present, the problem of estimating the stability of a formation formed by substantially nonlinear high-order agents with variable dynamic parameters is not sufficiently considered. This task is particularly important for a formation that is affected by complex unstable environmental conditions, in particular for the formation of unmanned aerial vehicles (UAVs). A method for analyzing the stability of formations of nonlinear agents with different types and orders of transfer function has been developed for studying information exchange in UAV networks. The new approach is based on the use of the Popov frequency criterion and the piecewise linear approximation of the hodograph. A computational experiment was performed to analyze the stability of a formation with transfer functions of various types and orders from the 1st to the 10th. The conducted studies revealed a significant difference in the calculated boundary coefficients of formation stability in the linear and nonlinear modes, which confirms the need to analyze the nonlinear stability under the influence of strong destabilizing influences on the formation.

When modeling the dynamic properties of an unmanned aerial vehicle (UAV) and an automatic control system (ACS), it is often necessary to take into account factors such as the lack of rigidity of the aircraft structure, the influence of control and destabilizing factors, which leads to an increase in the order of the model under study. The known numerical and analytical methods do not allow us to obtain general solutions for the variable parameters of the high-order system under study, and at the same time provide the required amount of error and computational costs. A method is developed to model the dynamic properties of UAVs and high-order control systems based on the spectral method, the linear approximation by parts of the input control actions and the spectrum of the system's output signal. An example of using the technique to model the dynamic properties of different orders ACS UAVs (from 1 to 10) with different types of inertia is considered. Based on the analysis of errors in the calculation of the transition process, the effectiveness of the method for analyzing high-order systems is shown based on the required computational costs.

When modeling Automatic Control Systems (ACS) of an unmanned aerial vehicle (UAV), it is often necessary to take into account the nonlinearity of an aircraft's reaction when the controls drift, as well as the strong influence of various destabilizing factors that make the system go out of linear mode. When known analytical and numerical methods are used to analyze dynamic systems, it is problematic to obtain general solutions that are valid for the variable parameters of the system under study and, at the same time, provide the required error value. A method has been developed to model dynamic processes in automatic non-linear UAV control systems based on linear approximation by parts and crosslinking of partial solutions with consideration of the initial conditions. An example of using the technique to model the transition characteristics of an ACS UAV with a single non-linear link is considered. Based on the analysis of errors in the calculation of the transition process, the effectiveness of the proposed approach is shown.

Creation of reliable and efficient flying ad-hoc networks (FANET) requires detailed development of the model of the physical layer of data transmission, determined by the conditions of operation of the networks. The problems of well-known software simulators of communication networks are the simplified nature of the physical layer, as well as the inability to obtain specific analytical solutions in the process of simulation. The hierarchical model of formation of information signals which allows to represent various types of communication channels and the channel-forming equipment, for providing their analytical description and the further analysis is developed. The model allows to describe communication channels between UAVs and (or) ground control centers taking into account the effects of attenuation, intersymbol interference, multipath propagation of signals; schemes of terminal and intermediate network equipment with linear and nonlinear signal conversion; circuits with forward regulation, backward regulation and combined regulation; circuits with multi-channel signal generation and processing, as well as cross-links between channels. Analytical expressions of the transfer function of the generalized hierarchical model for an arbitrary number of disclosed levels of hierarchy are obtained. An example of the presentation and study of the UAV transmitter circuit on the basis of a hierarchical model of signal formation is considered.