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Correction to the articles "Stability of Nonlinear 2D Systems Described by the Continuous-Time Roesser Model" and "Stabilization of Differential Repetitive Processes" published in the previous issue of the journal is presented.

Machine translation is a natural language text processing task that aims to automatically translate input text from one language into another language. The currently known machine translation models show a fairly high quality of translation between large languages, but for smaller language areas, represented by less data, the problem is still not solved. Different methods are used to deal with various errors in automatic translation systems. This paper discusses approaches that use translation models of reverse language directions and improve consistency between translations of the same text using direct and reverse translation models. The paper presents a general theoretical justification for such methods in terms of solving the likelihood maximization problem and also proposes a method for stable training of modern models using cyclic translations. [ABSTRACT FROM AUTHOR]

The article presents information on European Control Conferences that took place during 1991-2001. First European Control Conference was held in Grenoble, France in July 1991. Noteworthy is a high percentage of screened papers, which seems to be due to the fact that this was the first conference and a substantial part of papers were preplanned to some extent because they were presented at the 28 so-called invited sessions. The Seventh European Control Conference took place in Cambridge, Great Britain on September 14, 2003. The European Union recently pays much attention to increasing the standing of the European researchers in the international science and engineering. This was the main goal of organizing in 1990 the European Union Control Association.

The paper is devoted to the problem of deriving synchronizing and homing experiments for nondeterministic Input/Output automata; corresponding input sequences are widely used in testing (non-initialized) discrete event systems. In active testing, there is an opportunity to set a system under test into a known initial state; in passive testing, a known current state allows to reduce the number of properties to be checked. In the paper, we note that such experiments for Input/Output automata are different from so-called "gedanken" experiments with classical Finite State Machines; the existence check conditions of such experiments are established for a predefined discipline of applying inputs and a method for its derivation is proposed when such an experiment exists. The obtained results allow to reduce the problem of deriving synchronizing and homing experiments for Input/Output automata to the well developed problem of deriving such experiments for appropriate classes of Finite State Machines. [ABSTRACT FROM AUTHOR]

This paper considers subsonic turbojet aircraft fuel consumption minimization problem during cruise phase, assuming fixed time of arrival. The problem takes into account real atmosphere data. We utilize tailwind/headwind component values at various flight levels, as well as air temperatures and atmospheric pressures at various altitudes. The solution to the altitude and speed flight profile optimization problem is through constrained coordinate descent method. The paper considers optimizing the fuel consumption of a medium-haul aircraft during the cruise phase using sample data set on temperature, pressure, and wind speed. The proposed approach achieves a decrease in fuel consumption of 1.2% when optimizing with regard to real atmosphere. [ABSTRACT FROM AUTHOR]

This special issue features selected papers from the 14th International Conference "Management of the Development of Large-Scale Systems (MLSD 2021)" held on September 27–29, 2021. [ABSTRACT FROM AUTHOR]

The article provides information on the report on the XVI International Congress of the International Federation of Automatic Control that was held at Prague Palace of Congress in Czech Republic from July 4-8, 2005. The congress had more than 2, 200 attendees who presented 2, 454 papers. Some of the papers focuses on the control of moving objects, actual application of the modern theories to the problems of industrial and transportation, and prehistory and evolution of the system theory.

We propose a new approach to the problem of tuning and optimizing the parameters of a PID controller based on reducing the problem to an optimization problem. In this case, the performance of the controller is evaluated by a quadratic criterion of the system output: the PID controller is tuned against the uncertainty in the initial conditions so that the system output is uniformly small; in this case, a given degree of stability of the closed-loop system is additionally guaranteed. A gradient method for finding the PID controller parameters is given. Numerous examples show that the recurrent procedure proposed is very efficient and leads to PID controllers that are quite satisfactory in terms of engineering performance indices. The article continues a series of papers by the present authors devoted to synthesizing feedback in control problems from the standpoint of optimization. [ABSTRACT FROM AUTHOR]

The present paper is a continuation of the series of articles [1-2] and is devoted to solving the problem of estimating the parameters of hidden Markov models. The hidden state is a homogeneous Markov jump process with a finite set of states. The available observations are indirect and contain Wiener processes whose intensities are different and depend on the hidden state. Both the intensity matrix of Markov state transitions and the drift and diffusion parameters of the observations are subject to estimation. For identification, an iterative algorithm based on smoothing the state of the system based on observations over a fixed time interval is proposed. Then, according to these estimates, the parameters are reconstructed. The paper describes in detail all the numerical schemes for estimating the state and for identifying the parameters. A set of illustrative numerical examples is presented, demonstrating the high quality of the proposed identification estimates. [ABSTRACT FROM AUTHOR]

To improve the quality of generated speech signals, this paper proposes a method for taking into account time-varying information about the speaker. Using this technique, the system synthesizes more natural speech with a voice similar to the given target voice in both the voice cloning and voice conversion problems. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of video-based face recognition. Nowadays, facial recognition methods have made a big step forward, but video-based recognition with its poor quality, difficult lighting conditions, and real-time requirements is still a difficult and unfinished task. The paper uses the apparatus of convolutional networks for various stages of processing: for capturing and detecting a face, for constructing a feature vector, and finally for recognition. All algorithms are implemented and studied in the Matlab environment to simplify their further export to embedded applications. [ABSTRACT FROM AUTHOR]

The paper investigates the distillation problem for deep learning models. Knowledge distillation is a metaparameter optimization problem in which information from a model of a more complex structure, called a teacher model, is transferred to a model of a simpler structure, called a student model. The paper proposes a generalization of the distillation problem for the case of optimization of metaparameters by gradient methods. Metaparameters are the parameters of the distillation optimization problem. The loss function for such a problem is the sum of the classification term and the cross-entropy between the responses of the student model and the teacher model. Assigning optimal metaparameters to the distillation loss function is a computationally difficult task. The properties of the optimization problem are investigated so as to predict the metaparameter update trajectory. An analysis of the trajectory of the gradient optimization of metaparameters is carried out, and their value is predicted using linear functions. The proposed approach is illustrated using a computational experiment on CIFAR-10 and Fashion-MNIST samples as well as on synthetic data. [ABSTRACT FROM AUTHOR]

This thematic issue presents selected articles of the 20th All-Russian Conference with international participation "Mathematical Pattern Recognition Methods" (MPRM) held on December 7–10, 2021 in Moscow. [ABSTRACT FROM AUTHOR]

The use of convolutional neural networks trained with the margin-based softmax function allows achieving the highest accuracy in the face recognition problem. The development of embedded systems such as smart intercoms has increased interest in lightweight neural networks. Thus, lightweight neural network models, trained using the margin-based softmax function, were proposed for the face identification problem. In the present paper, we propose a distillation method that allows obtaining greater accuracy than other methods for the face recognition problem on LFW, AgeDB-30, and Megaface datasets. The main idea of our approach is to use the class centers of the teacher network to initialize the student network. Then the student network is trained to produce biometric vectors the angles from which to the class centers are equal to the angles in the teacher network. [ABSTRACT FROM AUTHOR]

In this paper, we solve the problem of detecting anomalies in X-ray images obtained by full-body scanners (FBSs). The paper describes the sequence and description of image preprocessing methods used to convert the original images obtained with an FBS to images with visually distinguishable anomalies. Examples of processed images are given. The first (preliminary) results of using a neural network for anomaly detection are shown. [ABSTRACT FROM AUTHOR]

We study the implementation of the optimal control strategy obtained in [1] and supplemented in [2]. The algorithm for optimal stabilization of a linear stochastic differential system in a position determined by a piecewise constant Markov drift has been tested in a substantial number of model experiments. The drift value is observed indirectly; i.e., the control problem is solved in the statement with incomplete information. Practical implementation is complicated by the instability of Euler–Maruyama numerical schemes that implement the Wonham filter, which is a key element of the optimal control strategy. To perform calculations, the Wonham filter is approximated by stable schemes based on the optimal filtering of Markov chains by discretized observations [3]. These schemes have different implementation complexity and orders of accuracy. The paper presents a comparative analysis of the control performance for various stable approximations to the Wonham filter and its typical implementation using the Euler–Maruyama scheme. In addition, three versions of discretized filters are compared and final recommendations are given for their application in the problem of stabilizing a system with hopping drift. [ABSTRACT FROM AUTHOR]

This paper develops a novel unified approach to designing suboptimal robust control laws for uncertain objects with different criteria based on a priori information and experimental data. The guaranteed estimates of the γ0, generalized H2, and H∞ norms of a closed loop system and the corresponding suboptimal robust control laws are expressed in terms of solutions of linear matrix inequalities considering a priori knowledge and object modeling data. A numerical example demonstrates the improved quality of control systems when a priori and experimental data are used together. [ABSTRACT FROM AUTHOR]

A novel approach is proposed to suppress bounded exogenous disturbances in linear control systems using a PI controller. The approach is based on reducing the original problem to a nonconvex matrix optimization problem. A gradient method for finding the controller's parameters is derived and its justification is provided. The corresponding recurrence procedure is rather effective and yields quite satisfactory controllers in terms of engineering performance criteria. This paper continues a series of the author's research works devoted to the design of feedback control laws from an optimization point of view. [ABSTRACT FROM AUTHOR]

This paper considers the use of machine learning for diagnosis of diseases that is based on the analysis of a complete gene expression profile. This distinguishes our study from other approaches that require a preliminary step of finding a limited number of relevant genes (tens or hundreds of genes). We conducted experiments with complete genetic expression profiles (20 531 genes) that we obtained after processing transcriptomes of 801 patients with known oncologic diagnoses (oncology of the lung, kidneys, breast, prostate, and colon). Using the indextron (instant learning index system) for a new purpose, i.e., for complete expression profile processing, provided diagnostic accuracy that is 99.75% in agreement with the results of histological verification. [ABSTRACT FROM AUTHOR]

This paper is devoted to the guaranteeing estimation method with application to the calibration problem of a gyro unit. Mathematical models are constructed to describe the kinematics of the gyro unit on a test bench. The applicability limits and errors of the models are investigated. A numerical solution procedure is developed for guaranteeing estimation problems based on their reduction to l1-approximation problems. [ABSTRACT FROM AUTHOR]

The paper deals with the problem of triclustering in multivalued triadic contexts in terms of one multidimensional extension of formal concept analysis; triclustering can be viewed as a search for dense subtensors in three-dimensional tensors over the field of real numbers. Two methods are proposed for solving this problem, namely, NOAC—a version of the OAC triclustering method for numerical data based on delta operators—and a triadic version of the -means method with an improved metric based on Manhattan distance and proximity predicates in each of the three dimensions. Numerical experiments are carried out both on real and synthetic data and confirm the superiority of the NOAC method in terms of the performance criteria for the resulting triclusters. [ABSTRACT FROM AUTHOR]

The purpose of this note is to correct an inaccuracy in the paper: Agaev, R.P. and Chebotarev, P.Yu., 'On Determining the Eigenprojection and Components of a Matrix,' Autom. Remote Control, 2002, vol. 63, pp. 1537-1545, and to present one of its results in a simplified form. [ABSTRACT FROM AUTHOR]

This article describes the criteria for publishing articles in the journal "Automation and Remote Control." Reviewing is pivotal in scientific activity. It is not a simple matter to write a good article unless the author reads his article through the referee's eyes. It is rather easier to review others' works than one's own work. Therefore, the first hint is not to young scholars, but to their supervisors as frequently call in post-graduates to review scientific articles. Objective evaluation of a paper from such a brief abstract is virtually impossible and papers are approved for inclusion in the proceedings largely on the basis of reputation and affiliation of the author.

The paper presents a constructive description of the set of all efficient (Pareto-optimal) investment portfolios in a new setting, where the risk measure named "shortfall probability" (SP) is understood as the probability of a shortfall of investor's capital below a prescribed level. Under a normality assumption, it is shown that SP has a generalized convexity property, the set efficient portfolios is constructed. Relations between the set of mean-SP and the set of mean-variance efficient portfolios as well as between mean-SP and mean-Value-at-Risk (VaR) sets of efficient portfolios are studied. It turns out that mean-SP efficient set is a proper subset of the mean-variance efficient set; interrelation with the mean-VaR efficient set is more complicated, however, mean-SP efficient set is proved to be a proper subset of mean-VaR efficient set under a sufficiently high confidence level. Besides a normal distribution, elliptic distributions are considered as an alternative for modeling the investor's total return distribution. The obtained results provides the investor with a risk measure, that is more vivid than the variance and Value-at-Risk, and with determination of the corresponding set of effective portfolios. [ABSTRACT FROM AUTHOR]

This paper is devoted to the mean-square optimal stochastic recovery of a square integrable function with respect to the Lebesgue measure defined on a finite-dimensional compact set. We justify an optimal recovery procedure for such a function observed at each point of its compact domain with Gaussian errors. The existence of the optimal stochastic recovery procedure as well as its unbiasedness and consistency are established. In addition, we propose and justify a near-optimal stochastic recovery procedure in order to: (i) estimate the dependence of the standard deviation on the number of orthogonal functions and the number of observations and (ii) find the number of orthogonal functions that minimizes the standard deviation. [ABSTRACT FROM AUTHOR]

This paper extends the method originally proposed in [1] to systems with an arbitrary number of inputs and outputs. The method ensures that these signals will be in given domains. Two sequential changes of coordinates are introduced to solve the problem. The first change reduces the plant's output to a new variable of a dimension not exceeding that of the control vector (input). The second change allows passing from the control problem with constraints to the one without them. The effectiveness of this method is illustrated for two problems. The first problem is designing a state-feedback controller for linear systems with constraints imposed on the input and state variables. The second problem is designing an output-feedback controller for linear systems with constraints imposed on the output and input. In both problems, the stability of the closed loop system is verified in terms of linear matrix inequalities. The results are accompanied by simulation examples to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper considers aircraft landing on a carrier. We propose two schemes for calculating, first, the probability of a go-around due to disengaging the arresting gear and, second, the maximum descent of the aircraft's trajectory with respect to the deck level immediately after leaving the deck. The instant to increase the aircraft's thrust before touching the deck is a control parameter affecting these characteristics. The requirements imposed on the probability of a go-around and the maximum descent of the aircraft's trajectory allow determining an admissible range for the thrust increase instant. Numerical results are presented for a real aircraft landing on a real carrier. [ABSTRACT FROM AUTHOR]

This paper considers the idea of reducing the debris of near-Earth space by releasing the spent parts of a spacecraft on orbits touching the conditional boundary of the Earth's atmosphere. We optimize the spacecraft transfer trajectory from a circular reference orbit of an artificial Earth satellite to a target elliptical orbit in a modified pulse problem statement. The convergence of Newton's method is improved by introducing a series of auxiliary coordinate systems at each point of applying an impulse action. The derivatives in the transversality conditions are calculated using a special numerical-analytical differentiation technique. [ABSTRACT FROM AUTHOR]

This thematic issue presents selected articles of the 20th All-Russian Conference with international participation "Mathematical Pattern Recognition Methods" (MPRM) held on December 7–10, 2021 in Moscow. [ABSTRACT FROM AUTHOR]

The article profiles professor Mikhail Vladimirovich Meerov, laboratory head at the Institute of Automation and Telemechanics of the USSR Academy of Sciences. He was born on August 15, 1911 in Ukraine and graduated from Kharkov Electrotechnical Institute. He was awarded with the A.A. Andronov Prize and published 12 monographs and over 200 research papers. In his pioneering works, optimal operation modes methods for boreholes were proposed, which provided for maximal total oil production.

The paper substantiates the concept of autoencoders focused on automatic generation of compressed images. We propose a solution to the problem of synthesizing such autoencoders in the context of machine learning methods, understood here as learning based on the input images themselves (in the bootstrap spirit). For these purposes, a special representation of images has been developed using samples of counts of a controlled size (sampling representations). Based on the specifics of this representation, a generative model of autoencoders is formalized, which is then specified to a probabilistic parametric sampling model in the form of a mixture of components. Based on the concept of receptive fields, a reduction of the general model of a mixture of components to a grid model of finite components of an exponential family is discussed. This allows the synthesis of computationally realistic coding algorithms. [ABSTRACT FROM AUTHOR]

Modern natural language processing models such as transformers operate multimodal data. In the present paper, multimodal data is explored using multimodal topic modeling on transactional data of bank corporate clients. A definition of the importance of modality for the model is proposed on the basis of which improvements are considered for two modeling scenarios: preserving the maximum amount of information by balancing modalities and automatic selection of modality weights to optimize auxiliary criteria based on topic representations of documents. A model is proposed for adding numerical data to topic models in the form of modalities: each topic is assigned a normal distribution with learning parameters. Significant improvements are demonstrated in comparison with standard topic models on the problem of modeling bank corporate clients. Based on the topic representations of the bank's customers, a 90-day delay on the loan is predicted. [ABSTRACT FROM AUTHOR]

This paper consists of two parts. In the second part, the management mechanisms for implementing complex projects that improve the energy-saving characteristics of products and technologies are further studied. An incentive model for saving energy in the sequence of projects within a multi-project structure is considered, and an optimal mechanism for this model is proposed. This mechanism includes a planning procedure, penalty functions for the nonfulfillment of plans, and an incentive function for project results. The functioning of the system is treated as a game of the Principal and sequentially connected agents who implement projects. The Principal's strategy is the choice of a specific mechanism. The agents' strategies are messages, in which they report to the Principal some information about their parameters, and also the choice of the project results. The information about the agents' parameters is necessary to calculate the plans based on the planning procedure. At the same time, the agents may prefer not to tell the truth, reporting unreliable (distorted) information. It is shown that the optimal mechanism proposed below stimulates the agents to provide reliable information and also to choose the project results coinciding with the plans. The first part of this paper (see [3]) was devoted to the mechanisms of project assessment and resource allocation; the results established therein are used to propose a project management system for implementing complex projects. [ABSTRACT FROM AUTHOR]

Robust stability conditions are established for a family of linear discrete-time systems subjected to uncertainties. The traditional approach, which involves the construction of a common quadratic Lyapunov function for the entire family of systems with uncertainty, often leads to the problem of conservatism. In this connection, constructing parametric quadratic Lyapunov functions seems promising. The main tools of the proposed approach are the apparatus of linear matrix inequalities and a modification, presented here, of the well-known Petersen's lemma. A simple approach to finding the robust quadratic stability radius of the family in question is proposed in the paper as well. The corresponding optimization problems have the form of semidefinite programming and one-dimensional minimization, which can be easily solved numerically. The efficiency of our approach is demonstrated via a numerical example. The results obtained can be generalized to design problems for linear discrete-time systems subjected to uncertainties, to other robust statements, and to the case of exogenous disturbances. [ABSTRACT FROM AUTHOR]

This paper describes a differential game of persons in which the utility functions of the players have a hybrid form; namely, they are changed at a random moment in time. The form of the payoff functional is simplified using integration by parts. For the cooperative scenario, the problem of time-consistency of the optimality principle chosen by the players is studied and a solution is proposed in the form of an adapted imputation procedure. A differential investment game is considered as an example. [ABSTRACT FROM AUTHOR]

The paper presents an image matching method based on heat kernels. The method permits one to single out the most stable features of images at the initial stage using heat kernels on graphs for subsequent comparison. Popular descriptors can be used for this. When using the method, the number of false matches is considerably reduced compared with traditional approaches. [ABSTRACT FROM AUTHOR]

The paper deals with the construction of interpretable machine learning models. The approximation problem is solved for a set of shapes on a contour image. Assumptions that the shapes are second-order curves are introduced. When approximating the shapes, information about the type, location, and shape of curves as well as about the set of their possible transformations is used. Such information is called expert information, and the machine learning method based on expert information is called expert-augmented learning. It is assumed that the set of shapes is approximated by the set of local models. Each local model based on expert information approximates one shape on the contour image. To construct the models, it is proposed to map second-order curves into a feature space in which each local model is linear. Thus, second-order curves are approximated by a set of linear models. In a computational experiment, the problem of approximating an iris on a contour image is considered. [ABSTRACT FROM AUTHOR]

This paper considers a linear discrete-time system operating in a repetitive mode to track a reference trajectory with a given accuracy. The system parameters are incompletely known and are described by the affine uncertainty model. In addition, the system is subjected to random disturbances, and measurements are carried out with noise. During system operation, the reference trajectory changes after a given number of repetitions. The resulting transient error may cause a temporary loss of accuracy. We propose a new iterative learning control design method to compensate the transient error. An example illustrates the effectiveness of this method. [ABSTRACT FROM AUTHOR]

The problem of intercepting a target moving along a prescribed trajectory by a Dubins car is stated and formalized as a time-optimal control problem with an arbitrary car velocity direction at the interception. The conditions available in the literature under which the optimal trajectory is a geodesic line drawn from the initial position of the car to the interception point are refined. Algebraic equations for calculating the optimal interception time are obtained. The optimal control is synthesized based on these equations. A software module is developed for constructing the optimal car trajectories for various target trajectories. [ABSTRACT FROM AUTHOR]

The paper deals with a theoretical study of the coordination of actions of members of a self-managed team using the Stackelberg strategy aimed at increasing their individual gains. It is assumed that the team creates a total income that increases with the growth of efforts made by each agent and obeys the diminishing return law. The unique Nash equilibrium that exists under the conditions of complete autonomy of all agents is Pareto inefficient. It is shown that for the transition to a Pareto-preferential outcome it suffices to form a small group (coalition) in the team whose members trust each other and are not prone to opportunistic behavior. Following a coalition strategy aimed at achieving the maximum coalition gain, the coalition members increase their efforts; this leads to an increase in the total income. Conditions are found under which the coalition can use the Stackelberg leadership strategy. It is shown that the Stackelberg equilibrium outcome dominates in the sense of Pareto over Nash equilibrium outcomes both in noncooperative and coalitional games. [ABSTRACT FROM AUTHOR]

The article focuses on the 15th International Symposium on System Identification (SYSID) 2009, organized by the International Federation of Automatic Control (IFAC). It is stated that the problems of reducing the bulk of computations were discussed at the symposium. The development of an extension to the Matlab identification toolbox was described by researcher L. Ljung.

Information about several papers discussed at an international conference on computer-aided design, computer-aided manufacturing and product data management is presented. Topics include design in machine building and radio electronics, integrated process design and control systems and product data management systems. The conference featured E. I. Artamonov, V. I. Brezgin and V. M. Rozin.

The paper considers the modification and application of the ant colony method for the problem of directed enumeration of the values of system parameters when performing calculated multiple calculations. Interaction with the user makes it possible to stop the process of exhaustive enumeration of sets of parameter values, and the application of a modification of the ant colony method will allow us to consider rational sets at early iterations. If the user does not terminate the algorithm, then the proposed modifications allow one to enumerate all solutions using the ant colony method. To modify the ant colony method, a new probabilistic formula and various algorithms of the ant colony method are proposed, allowing for each agent to find a new set of parameter values. The optimal algorithm, according to the research results, is the use of repeated endless cyclic search for a new solution. This modification allows you to consider all solutions, and at the same time, find all the optimal solutions among the first 5% of the considered solutions. [ABSTRACT FROM AUTHOR]

We consider the intractable problem of finding several edge-disjoint spanning trees of the minimum total weight with a given diameter in complete undirected graph in current paper. The weights of edges of a graph are random variables from several continuous distributions: uniform, biased truncated exponential, biased truncated normal. The approximation algorithm with time complexity (n2), where n is number of vertices in graph, is proposed for solving this problem. The asymptotic optimality conditions for constructed algorithm is presented for each considered probabilistic distribution. [ABSTRACT FROM AUTHOR]

This paper considers a single-server queueing system with an incoming Markovian Arrival Process (MAP) request flow with two states. Explicit expressions are derived for the stationary probability distribution of the states and several numerical characteristics of the system (the probability of idle time of the server, the expected number of requests in the system, and the mean queue length). The resulting numerical characteristics are presented in tables and plotted in graphical form as well. The recurrent MAP flow with two states as a special case of correlated MAP request flows is studied. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) sensors usually require a calibration procedure. In some cases, scale factor errors depend on the signs of the projections of the vector input signal onto the sensitivity axes of the sensor. To eliminate the ambiguity of scale factor errors, the angular positions of the sensor can be restricted so that the corresponding projections have a definite sign. This paper presents an analytical solution of the optimal calibration problem for a 3D sensor under a constraint on its angular positions. [ABSTRACT FROM AUTHOR]

In modern smart manufacturing, robots are often connected via a network, and their task can change according to a predetermined program. Iterative learning control (ILC) is widely used for robots executing high-precision operations. Under network conditions, the efficiency of ILC algorithms may decrease if the program is restructured. In particular, the learning error may temporarily increase to an unacceptable value when changing the reference trajectory. This paper considers a networked system with the following features: the reference trajectory and parameters change between passes according to a known program, agents are subjected to random disturbances, and measurements are carried out with noise. In addition, the network topology changes due to the disconnection of some agents from the network and the connection of new agents to the network according to a given program. A distributed ILC design method is proposed based on vector Lyapunov functions for repetitive processes in combination with Kalman filtering. This method ensures the convergence of the learning error and reduces its increase caused by changes in the reference trajectory and network topology. The effectiveness of the proposed method is confirmed by an example. [ABSTRACT FROM AUTHOR]

In this paper, a heating process control law with steam supply is designed for a fluid in a heat exchanger. The process is described by a linear hyperbolic equation of the first order with a nonlocal boundary condition with a time-delayed argument. The temperature of the supplied steam is found as a linear dependence on fluid temperature values at measurement points in the heat exchanger. Explicit formulas are obtained for the gradient of the objective functional of the control problem in the space of the feedback coefficients (parameters) of this dependence. A numerical scheme is developed for determining the feedback parameters based on these formulas. Finally, an algorithm is proposed for determining the rational (optimal) number of measurement points. [ABSTRACT FROM AUTHOR]