The paper considers the problems of synthesizing positional control of dynamic systems (DS) in situations with a high level of uncertainty caused both by disturbances acting on the DS and interference in information channels. Uncertainty results from the action of various external disturbing factors, uncontrolled changes in the object properties, and equipment failures and malfunctions. A peculiar feature of the considered problems is that they are single events. In these information conditions, the synthesis of positional control of dynamic systems is considered based on the minimax approach worst-case design. Therefore, the mathematical model of processes is characterized by disturbances and measurement errors known with a precision up to sets, and the DS state vector is known with a precision up to membership in the information set as a result of solving the estimation problem. The proposed approach combines control concepts under information deficiency proposed by N. N. Krasovsky, A. B. Kurzhansky, and V. M. Kuntsevich with A. A. Krasovsky’s concepts of building selforganizing systems. The "principle of a guaranteed result" was chosen to synthesize DS control. A key distinction between the guaranteed and stochastic approach is the use of uncertainty sets of disturbances, interference, and the system state vector in DS control. The first part of the article solves the problem of estimating the state vector and, as a result, constructs an information set, to which the system state vector is guaranteed to belong. The second part of the article solves the control problem taking into account control restrictions, when the system operation quality is assessed by the belonging of the object’s state vector to a given set, which may depend on time. The tasks of stabilization, tracking, and terminal control can be set here. The control problem is also solved based on the guaranteed approach when specifying the requirements for the system in the form of a quadratic functional. The paper also considers the use of the Lyapunov function for control synthesis. The solution of estimation and control problems is reduced to extremal problems with linear and quadratic objective functions under restrictions in the form of systems of linear inequalities. The paper provides their examples.

It is shown that in order to ensure accident-free operation of production facilities it is necessary to increase the degree of adequacy of the results of the analysis of technological parameters in control and diagnostic systems. However, in these systems, when changing the dynamics of technological parameters, the sampling interval of analyzed signals does not change despite the need for such a procedure. Because of this in practice there are cases when the adequacy of the results of solved problems is not ensured. In rail transport, control and diagnostic systems are used for rolling stock, that diagnose malfunctions in wheel-motor units, bearing defects, lack and insufficiency of lubrication, misalignments of bearing mounting, defects of fastening, imbalance of rotating parts, defects of gearbox. Also diagnosed are looseness of the feed and brake lines, malfunctions of the brake valve, malfunctions of the brake cylinders, malfunctions of the compressor. The inspection results are first of all displayed on the driver’s monitor. In these systems, due to the delay in the monitoring result, sometimes the appropriate measures are delayed. For example, due to the specifics of rail transport, when analyzing the vibration signals received from the same sensors, the control results change when the train speed changes. This causes additional error, which can often lead to the violation of the adequacy of the control results. To eliminate this shortcoming, first of all, it is necessary to proportionally change the sampling interval of the analyzed signals when changing the speed of the rolling stock. In vibration diagnostics, the adequacy of the obtained results depends to a great extent on the accurate determination of the sampling interval. It is shown that the technology and principle of construction of analog-to-digital converters with adaptive determination of the sampling interval by taking into account the dynamics of the operation of controlled objects allows to avoid the control error caused by the error of sampling interval selection.

The expediency of forming and storing in the knowledge representation model of an autonomous robot contradictory information about the laws of transformation of various situations in a problem environment (PE) that occur as a result of the actions performed by the robot is substantiated. This need is due to the fact that a priori it is not possible in practice to construct and assign to an autonomous robot a detailed formal description of a model of a problem environment. The robot is actually forced to function in a priori underdetermined problem environments. This, in turn, leads to the fact that under identical conditions, according to a given model of the problem environment, but taking into account its actual characteristics, various actions performed by the robot can lead to the required result to achieve a given goal. Consequently, in real operating conditions, an autonomous robot may encounter the emergence of "contradictory" information when, under identical conditions, according to a given PS model, a formed plan of goal-directed activity, which was previously effective, requires significant adjustments to achieve a given goal. Such an adjustment to the formed behavior plan is usually associated with the robot studying the patterns of purposeful transformation of situations in the actual problem environment and replenishing procedural knowledge. Thus, the use of contradictory data associated with the incompleteness of a priori specified knowledge provides an autonomous robot with the opportunity to expand information about the patterns of an a priori underdetermined problem environment and, on this basis, increase functionality. To solve this problem, the article proposes a structure of typical elements for representing "contradictory" knowledge, including various elementary acts of behavior, the development of which allows an autonomous robot to obtain a given result by performing various actions in similar operating conditions, taking into account their individual characteristics that are not reflected in the model describing the current problematic environment situations. Cognitive tools have been developed to provide an autonomous robot with the ability to organize an effective combination of procedures for planning goal-directed behavior based on a given model of knowledge representation and self-learning procedures in a priori underdetermined conditions of an unstable problem environment. In general, the considered cognitive tools for planning the expedient activity of an autonomous robot allow to expand its functionality and adapt on this basis to complex a priori underdetermined operating conditions.

The article discusses the development of a mobile training system for musculoskeletal rehabilitation. Analysis of existing research shows that the use of mobile devices allows for monitoring and evaluating the quality of exercises performed during outpatient musculoskeletal rehabilitation. The main directions for implementing mobile training systems were identified, and the task of organizing musculoskeletal rehabilitation using mobile devices was set. To address this task, an architecture for a mobile training system was developed, and necessary software tools were analyzed. A model for processing information about exercises in musculoskeletal rehabilitation was developed, including formalizing the processes of exercise assessment and transforming raw data from the inertial navigation system of the mobile device to calculate speed and trajectory of movement. An information processing algorithm was presented, including a list of necessary operations to eliminate significant drawbacks of mobile sensors (high error rate, noise, and error accumulation). Experimental studies were conducted to confirm the effectiveness of the algorithm. The functioning algorithm of the mobile training system, including its main stages for organizing the musculoskeletal rehabilitation process, was implemented. The algorithms for processing information from the mobile training system were tested, demonstrating their applicability for monitoring exercises. The scientific novelty of the research lies in the development of architecture, models, and information processing algorithms in the mobile training system for musculoskeletal rehabilitation, taking into account the technical characteristics of mobile devices. The theoretical results obtained (architecture, model, and algorithms) were used for the software implementation of the mobile training system for musculoskeletal rehabilitation on the Android operating system. The practical value of the conducted research lies in organizing the process of outpatient musculoskeletal rehabilitation using mobile devices and developing data processing algorithms, which have ensured sufficient accuracy in measuring actions performed.

The work examines the problem of controlling an amphibious aircraft (AA) in steady-state planing mode under the influence of wind-wave disturbances. An analysis of modern approaches and methods for the synthesis of aircraft control systems was carried out. A brief overview of existing scientific works devoted to the control of AA in various modes of movement is presented. The necessity of developing an autopilot to control the longitudinal movement of an AA is substantiated. A review is made of the characteristics of the state of the water surface and their influence on the operation of an AA on water. An analysis of the influence of external disturbances on the planing stability of AA is presented, on the basis of which the use of the integral adaptation method on invariant manifolds is justified for the synthesis procedure of a vector nonlinear controller of the control system of an AA in planing mode under the influence of wind-wave disturbances. The approach uses the synergetic principle of "expansion-compression" of the phase space, on the basis of which an extended model of synergetic synthesis is first constructed, taking into account estimates of the action of disturbing influences, and then, when applying the synthesis procedure, a phased compression of the phase space is carried out by introducing invariant manifolds, at the intersection of which the fulfillment of a given technological task is guaranteed, and invariance to the action of wind-wave disturbances is also ensured. The results of the study are confirmed by computer modeling of a synthesized closed-loop control system for the aircraft; in particular, it is shown that the closed-loop system guarantees the maintenance of the required flight speed and altitude, as well as maintaining the trim angle in the required range of values.

The work is devoted to solving the problem of finding the minimum composition of a team of specialists and general ground handling facilities (equipment), as well as distribution in the process of preparing the required group of aircrafts for use within a given time. To justify the minimum composition of the team and the necessary equipment, it is necessary to solve the problem of forming a job schedule for a group of aircrafts, a distinctive feature of which is to take into account a number of restrictions, caused by the interaction of specialists and equipment, as well as the order and incompatibility in time of some jobs. This, in turn, requires consideration of a huge number of options for organizing the work performed on each aircraft, and scheduling options for servicing several aircrafts by one specialist. The problem of substantiating the minimum composition of specialists and equipment is based on the use of combinatorial optimization methods, i.e. the construction of possible solutions, the number of which is reduced by using the branch-and-cut method. The article proposes a mixed integer linear programming model with binary variables to find the optimal solution and a software implementation that does not require large computational resources. It is given and analyzed in detail an example of finding the optimal team of specialists who prepare a group of six aircrafts, each of which performs five types of work. The reasonable solution time to find the schedule for a given team made it possible to consider all possible options for the composition of the team (tens of thousands of options) and justify such an option in which the number of specialists in the team would be minimal, but they would ensure the preparation of the aircraft within a given time. When solving a problem, an exact schedule is found for each considered variant of the team composition. Further development of this approach is based on discrete time models; preliminary studies show the possibility of finding the optimal schedule for preparing a group of 30 aircraft for up to 5 seconds.