Currently, developers of information and control systems for marine underwater equipment are faced with new opportunities for using modern high-performance technologies to improve the quality of control processes and the accuracy of operations. For example, approaches based on predictive models (PM) are actively studied to ensure the synthesis of motion control algorithms. In this case, modern machine learning methods, including artificial neural networks (ANN), can be used to synthesize PM. A method for constructing a PM as part of the algorithmic support of information and control systems of an autonomous unmanned underwater vehicle using a neural network emulator of dynamics is proposed. The main disadvantages of the traditional approach to the synthesis of PM in the form of a system of differential equations are analyzed, a sequential structural and parametric synthesis of the neural emulator is performed. In particular, the issues of initial initialization of the neural network parameters and the formation of a training sample are considered, the structure of input and output data is determined. A feature of the proposed ANN structure is the use of pretraining based on a cascade of autoencoders. Results of pretraining the neural network emulator are presented, justifying the choice of the ANN architecture. Also, to check the adequacy of the PM in the form of a neuroemulator, verification is performed with respect to a known nonlinear dynamic model during statistical simulation modeling.

An algorithm for energy-efficient planning of processes in the computing system of an underwater vehicle is proposed. The algorithm ensures minimization of the power consumed by the computing system and the average time tasks spend in the system. A distinctive feature of the algorithm is that it allows for flow shop planning in systems with many information outputs that may appear as a result of building an energy-efficient system architecture. Using the algorithm when designing a layout of the computing system of an underwater vehicle allows for reducing the power consumption and decreasing the average time of stay of tasks in the system compared to the enumeration method in the original system. The polynomial complexity of the proposed algorithm makes it possible to use it when planning processes in real-time systems.

The features of the dynamic regressor expansion and mixing method and its modifications used in estimating the parameters of a linear regression equation for systems with different properties are analyzed. The objectives of the study are to identify key aspects of the practical application of the dynamic regressor expansion and mixing method, compare the patterns of application of its modifications, and select the most effective solutions. Numerical modeling is used to compare various modifications of the original algorithm aimed at overcoming the following problems: a relatively large number of adjustable parameters, weak excitation of the regressor, the need to select gradient descent coefficients to ensure convergence for each parameter in a comparable time, and the presence of outliers in the estimate for piecewise constant parameters. It is shown that the use of expansion schemes allows to reduce the number of adjustable parameters, adding a regularizing matrix to the expanded regressor provides an estimate for cases with weak excitation, normalization of the excitation of the regressor ensures the agreement of the convergence time of the estimate for different degrees of excitation of the regressor, and an interval integral filter with resetting is effective against outliers in the parameter estimate in the case of their piecewise constant assignment.

The automatic control system for the movements of a hex-rotor unmanned aerial vehicle (hexacopter) during monitoring of the outer surface of an aircraft while parked is investigated. For the previously developed detailed mathematical model of the hexacopter flight, the structure and laws of control of its trajectory motion are proposed in order to minimize its deviations from the specified trajectory. The peculiarity of this structure is the presence of an orientation loop, as well as locomotion and trajectory loops, which allow separating the processes of stabilizing the speed of the unmanned aerial vehicle and its trajectory control. The control laws in the trajectory loop are presented, formed on the basis of using linear regulators and proportional limiters of the required speeds of the unmanned vehicle. The results of the synthesis of control laws in the locomotion loop are presented based on the use of the successive returns method of P. V. Kokotovich. A variant of forming the required values of engine thrust of the unmanned aerial vehicle by imposing additional restrictions on its dynamics is proposed and substantiated. The results of the simulation modeling of the hexacopter flight in different modes are presented: altitude gain, horizontal flight, vertical landing, turn at a constant altitude. The results of the modeling confirme the validity of the technical solutions adopted in the work.

Results of upgrading an unmanned aircraft of the airplane type are presented. For the purposes of upgrading, the units of the unmanned airplane-type aircraft are developed and manufactured, providing vertical takeoff and landing of the aircraft. Presented results of flight tests demonstrate the feasibility of upgrading the finished aircraft to a vertical takeoff and landing aircraft by reducing the takeoff and landing distance, and the ability to switch to hover mode, as well as increasing the cruising speed, which allows for faster completion of flight mission.

For a quantum sensor of rotation, the influence of displacements of the optical system elements on the radiation power imparted to the ensemble of atoms of the working substance located in the gas cell is considered. Based on obtained data, changes in the accuracy characteristics of the quantum rotation sensor are analyzed. Linear and angular displacements of the elements of the optical system of the device are investigated. Cell parameters are identified that allow achieving insensitivity of the accuracy characteristics of the quantum rotation sensor to linear displacements of the elements of the optical system by up to 0.15 mm.

A method for estimating intra-turn instability of the rotation speed of the axes of a simulation stand is proposed. The method is based on cross-calibration of the angular velocity sensor based on the value of the rotation speed of the stand faceplate measured using the fixed angle method. The proposed method allows to increase the reliability of the output data of the angular velocity sensor and, based on the measured values of the instantaneous rotation speed, to estimate the instability of the latter and to isolate the systematic component of the error in the rotation speed of the axes of the simulation stand within a turn. Using a slot sensor (as a zero mark) and a fiber-optic gyroscope (as a meter for the rotation speed of the faceplate around the axes of the rig), the rotation speed of the faceplate around the axes of a twoaxis stand is measured and the errors in their instability are estimated. The obtained data are analyzed, the systematic component of the error in the rotation speed of the faceplate of a two-axis stand around its outer axis is isolated. Using the obtained results in the calibration algorithms of inertial sensors and systems will increase the accuracy and reliability of these calibrations.

A comparison of the effectiveness of three algorithms for fault detection and isolation of SINS sensors when using triple redundancy is made. To increase the efficiency of solving the problem, the authors proposed three modifications of the pairwise difference method. Methods which use weighted sum of pairwise differences, calculate an adaptive response threshold, and transform to principal components are proposed. In most existing approaches, fault detection is done at the system level, namely, the values of the output navigation and dynamic parameters of the SINS are used. In contrast, we propose to solve diagnostic problems based on the inertial sensors’ measurements, which reduces the time elapsed from the occurrence of a fault to its detection. In addition, solving fault detection problems at the level of inertial sensors opens opportunities for reconfiguring the SINS to ensure the accuracy of the redundant system in the event of fault of one or more sensors in its composition. Comparison of algorithms is carried out through seminatural simulation, in which artificial faults are added to the data obtained under conditions of a stationary base. Additive single and repeated faults with random amplitude are considered as an example. Relative to the values measured by the sensors, faults amplitude can be classified as small, medium and large. To compare the effectiveness of the proposed algorithms, a confusion matrix is calculated, from which the precision and recall metrics are then calculated.

The problem of determining the orientation of a spacecraft using instantaneous measurements is investigated. A number of algorithms for determining the spacecraft orientation using one-time measurements of various physical nature are presented: TRIAD, Optimized TRIAD, q-Method, QUEST, ESOQ, ESOQ2, SVD. The following properties of the algorithms implemented in the MatLab mathematical package are analyzed: running time, average value and standard deviation of the orientation determination error measure, and the number of floating-point operations.