An algorithm is proposed that makes it possible to effectively detect the presence of correlated interference in experimental data used for parametric identification. The main problem leading to such interference is structural inconsistencies between models and real objects. Establishing the presence of correlated interference opens up opportunities for expanding identification tools. In this paper, the identification of correlated interference using the parametric identification method was carried out, while using a modified Newton method to minimize the objective functional. The results obtained from the numerical example demonstrate the effectiveness of the proposed method in detecting correlated interference, and also indicate that the values of the criteria contain information about the magnitude of the estimation errors. The results obtained in this scientific research work can be useful for further developments in the field of parametric identification and signal processing.

In the first part of the article, the authors proposed a generalized algorithm for solving the inverse dynamics problem for multi-link underwater manipulators (UM), which, during their movement in a viscous medium, allows more correctly taking into account not only viscous friction, but also the added masses and moments of UM links. Using this algorithm, equations are obtained that form the external moments on the output shafts of all UM drives and their individual components, which depend not only on unknown added masses and moments of inertia of links, but also on the forces of viscous friction. These components are presented in an analytical form, convenient for identifying specific parameters of the interaction of a viscous medium with UM links, which should allow the implementation of automatic UM control systems for high-precision execution of even force underwater manipulation operations. In the second part of the article, based on the obtained analytical ratios, a method has been developed for identifying the values of unknown added masses and moments of inertia of the UM links, as well as the coefficients of viscous friction of these links during their arbitrary movement in an viscous medium. The specified method includes two stages. At the first stage, on the output shafts of each UM drive, with the help of diagnostic observers, the values of external moments are determined, depending only on the forces of viscous friction, as well as on unknown added masses and moments of inertia. At the second stage, using analytical representations of these moments and a linear Kalman filter, the desired current values of the added masses and moments of inertia, as well as the coefficients of viscous friction, are determined in a specific ocean zone. The simulation results using a complete mathematical model of an autonomous under- water vehicle with a specific UM installed on it confirmed the operability and high efficiency of the proposed method for the accurate identification of all the desired parameters of the interaction of UM links with a viscous medium.

This paper considers the multi-traveling salesman problem (MTSP), where traveling salesmen must visit a certain number of cities exactly once and return to the starting point with minimal travel costs. There are three methods for solving this problem: optimization-based, Cluster First-Order Second-based, and Route First-Cluster Second. Although the latter was used to solve the vehicle routing problem, this paper proposes a modification of it for solving the MTSP. The main objective of the study is to develop an effective method for solving this problem that will reduce the task execution time and optimize resource utilization. To evaluate the effectiveness of the developed method, a comparative analysis of the methods for solving the MTSP was conducted. It was revealed that the proposed method based on the Route First-Cluster Second concept allows for more efficient load and resource management, which helps to minimize the overall task execution time. This approach provides a wider coverage and allows us to evaluate the applicability of the method in various contexts, which is an important advantage of this study. The evaluation of the results was based on three key criteria: the computational time for obtaining a solution to the MTSP, the total length of the routes traveled by the traveling salesmen, and the maximum route length. The analysis of the experimental data showed that the developed method outperforms the classical approach based on meta-heuristics, and in all considered criteria in most experiments and in some situations it outperforms the approach based on clustering and meta-heuristics.

The article presents an algorithm for controlling a group of land-based autonomous vehicles. Research into the field of controlling autonomous ground vehicles is actively progressing. А separate challenging scientific and technical issue is the development of algorithms to manage a group of vehicles while maintaining optimal solutions. Despite the abundance of works on using unmanned vehicles in urban settings, algorithms are also under study that operate on rough terrain while solving, for instance, cargo delivery missions in hard-to-access areas. In this paper, the issue of driving an autonomous vehicle in a deterministic setting is addressed. To resolve the two-point issue arising from the maximum principle, the algorithm developed by Krylov and Chernousko has been employed. The difficulty in applying it for real-time control has been demonstrated. An algorithm incorporating a predictive model has been used to implement the concept of " flexible trajectories". The results of the numerical simulation are presented, demonstrating the benefits of this algorithm. Group movement of unmanned ground vehicles was implemented using a "master-slave" approach, where the movement of a slave control object follows a trajectory aligned with the movement of the master. The results of the numerical modeling show the potential for using the proposed algorithm to manage a group of autonomous vehicles under various starting and ending conditions. The algorithm proved successful in the presence of a restricted area for the controlled vehicle. Possibility of simultaneous operation of multiple guided autonomous vehicles is demonstrated.

The problem of optimal program control of the spatial motion of a free solid body (in particular, a spacecraft) in an inertial coordinate system using dual quaternions (parabolic Clifford biquaternions) is considered. The dual vector control function (dual composition of angular and linear accelerations of a body), constructed using the Pontryagin maximum principle, and is not limited in the dual module. The integral quadratic functional with respect to angular and linear accelerations, cha- racterizing the energy costs of transferring a body from a given initial state to a given final state in a fixed time is minimized. The spatial motion of a body is equivalent to the composition of angular (rotational) and translational (orbital) movements (Chasles’ theorem). The boundary conditions for angular and linear positions, as well as for angular and linear velocities of the body, are arbitrary. The translational (orbital) motion of a body together with the rotation of a body around its center of mass is described using two new biquaternion differential equations. The laws of change of the control force and the control moment are obtained using the constructed optimal laws of change of angular and linear accelerations of a body according to algebraic formulas using the concept of solving inverse problems of dynamics. After applying the maximum principle (to the construction of optimal program accelerations), the control problem under study was reduced to a twenty-eighth order nonlinear differential boundary value problem with a movable right end of the trajectory, which was solved numerically using the Levenberg-Marquardt method. The case of a large deviation in the angular measure between the initial and final orientations of the spacecraft in the presence of a small linear translational displacement of the spacecraft (the problem of optimal spatial maneuvering of the spacecraft) is considered. In this case, the mass distribution of the spacecraft corresponds to a spherically symmetric solid body or the International Space Station (ISS), or the Space Shuttle spacecraft. Graphs of changes in the components of the dual quaternion (biquaternion) describing the orientation of the spacecraft and its location in the inertial coordinate system, the components of angular and linear velocity vectors, the component of angular and linear accelerations vectors (optimal controls), the component of the control moment and the control force vectors are constructed. The obtained numerical solutions are analyzed; the features and patterns of the process of optimal spatial motion of spacecraft are established. А table of values of the components of the control moment vector in the coordinate system associated with the body at the beginning, middle and end of motion for all three bodies in the presence of translational displacement is obtained.

Increasing production productivity through automation is an urgent task of modern science. The introduction of hybrid additive-subtractive production complexes will become only one of the stages of this automation for modern times. Cur- rently, many tasks arising as a result of attempts at industrial application of such complexes are unresolved. Such tasks include the creation of a module control system for machining, which works out the loss of stability because of uncertainty in the mechanical properties of the material caused by the deposit of layers of the latter. The present work is aimed at investigating the possibility of using machine learning methods to adapt control contours during drilling to the uncertainty of the properties of the workpiece material, due to the difficulty of using traditional control methods, due to the complexity of nonlinear laws in the contact zone of the tool with the workpiece. The paper presents a mathematical model of the drilling process; using a series of numerical experiments, the possibility of the model to qualitatively describe the processes in the contact zone is confirmed. The description of the data set process for training machine learning models is given, and the effectiveness of their use for predicting the internal parameters of the system is confirmed. As a result of the performed con- structions, the paper presents a control system that meets the task, the effectiveness of which has been proven by numerical experiment. The presented control system identifies the mode of loss of stability of the object according to the signal from the force-moment sensor between the carrier and the tool and returns to the system the cutting parameters adjusted relative to the data predicted by a bunch of machine learning models for which the stability of the control object is maintained. The practical significance of the obtained results is determined by the effectiveness shown in the work of using machine learning methods in the development of control systems for machining. Further development of such systems can be aimed at solving related problems related to increasing the response time of the control circuit to the loss of stability of the machining process.