This paper presents the development of an additional module for the mechatronic vibration stand SM-2M at the Institute of Mechanical Engineering of the Russian Academy of Sciences. The module is designed to conduct experimental studies of various technological processes, including sieving, material mixing, and granulation. To evaluate the dynamic characteristics of the system, a virtual model of the equipment was developed, which includes elements of technological tooling represented by a working table accounting for its own mass. The model was implemented using the ADAMS.View software package, providing capabilities for visualization and dynamic analysis of the system. Testing of the novel mechanical design was carried out via co-simulation methodology, integrating a proportional-integral control algorithm implemented in MATLAB/Simulink with the physical model of the mechanical part realized in ADAMS. Several virtual experiments addressing the control of rotational speed and angular displacement of the drive motors of the vibrational setup equipped with technological tools and bulk materials are presented. Graphical dependencies depicting variations in motor rotation frequencies and trajectories of motion of the center of mass of the working components based on virtual sensor readings are provided. The results facilitate implementation of modern control methods, enhancing technology reliability and efficiency.

This paper considers the problem of simultaneous optimization of the program motion and a bundle of perturbed motions for a nonlinear controlled dynamic system. Such problems arise in the study of optimization of charged particle beam dynamics in accelerating and focusing structures. When solving the problem of stabilizing the program motion, we also encounter the significant dependence of the perturbed motions on the selected program motion and the need to take this into account. The sum of a pair of Bolza type functionals is considered. One is defined on the program motion, the other is defined on a bundle of trajectories. Necessary optimality conditions are given. Moreover, in the stationary case, the function that attains a maximum under optimal control is constant, just as under optimal control of an individual trajectory.

Methods for analyzing the spatial motion of a satellite in geostationary orbit during long-term conservation with episodic activation of control have been developed. The computer simulation results of a geostationary communications satellite motion during the year with quarterly correction are presented.

Improving the performance of technological operations is an urgent task of modern science. Vibrational equipment is one of the common types of industrial equipment used for operations such as sieving, crushing, vibration conveying, etc. An energy-based approach to controlling vibration units allows maintaining a constant level of the total oscillation energy of the vibration unit with a load, which opens up wide opportunities for intellectualizing equipment control under conditions of parameter space uncertainty. This work aims to study the influence of the completeness of information about the vibrational state of the system on the performance of the speed gradient algorithm when controlling multiple synchronization of rotors of a vibration unit. The paper presents results of numerical simulation based on the system dynamics equations and approximate values of the vibration unit parameters. Results of experimental studies on the mechatronic vibration setup SV-2M are presented.

Diagnostics of epileptic activity is usually based on visual and/or automated analysis of brain electromagnetic signals, including EEGs, MEGs, local field potentials and single/multi neuron unit recordings. In primary generalized epilepsies the pathological activity is detected in both hemispheres. However, in some animal models this was not tested properly. Recently, we have found that in pentylenetetrazol rat model of spike-wave discharges (SWDs) in half of subjects a significant part of SWDs was expressed in the cortex of only one hemisphere. The purpose of this paper is to test whether such discharges differ from background activity. In this paper we use both indirected (phase coherence index, mutual information function) and directed (nonlinear adapted Granger causality) nonlinear measures which became popular in neuroscience last decades. Studying connectivity changes accompanying discharge initiation we found increase in interhemispheric coupling similar to that found previously between different cortical regions of the same hemisphere, with no specific preictal connectivity dynamics distinguishable. We also showed that there was no significant difference in interhemispheric connectivity between unilaterally and bilaterally expressed discharges. In pentylenetetrazol rat model of SWDs there is a significant increase in connectivity between hemispheres of somatosensoty cortex during ictal stage. This increase is expressed at the same level for both unilaterally and bilaterally visible discharges.

This study investigates the use of artificial neural network (ANN)-based surrogate models for predicting residual velocities in impact problems under varying conditions. Building upon the combination of FEM simulations and incubation time fracture criteria, this paper extends the methodology by exploring multiple ANN architectures tailored to capture complex impact dynamics, including cases involving composite targets and variable impactor velocities. Special attention is given to identifying failure modes of the FEM model. The results demonstrate that appropriately configured ANN surrogates can effectively reduce computational costs and achieve accurate predictions in the case of high-velocity impacts.

This paper addresses the direct current (DC) motor control problem, ensuring that the motor’s speed remains within a given bounds. To solve this, a coordinate transformation is applied to convert the original problem into a control problem without constraints on the control variable. A feedback controller is proposed, with the control gain determined by solving an optimization problem. An experimental setup is introduced to validate the proposed control algorithm. Two cases are proposed for testing, and the experimental results show that the algorithm ensures the motor’s speed stays within the given set by the designer.

We propose a decentralized method for adaptive prediction of task processing times in dynamic environments. Each controller independently estimates the expected duration of incoming tasks using a gradient-free update rule based on the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. To ensure coherence across the system, controllers synchronize their models through consensus over a time-varying communication graph. This approach enables efficient learning under limited observability and noisy feedback, without requiring access to gradients or global information. We provide theoretical guarantees for convergence under bounded noise, drawing on recent results for distributed SPSA with consensus. Simulations demonstrate the method’s resilience to abrupt changes in task behavior (“drift”) and show that it outperforms baseline methods in terms of prediction accuracy and inter-controller consistency. We further illustrate how the SPSA+Cons approach can be deployed in a modular multi-agent AI platform to align operational parameters across heterogeneous agents under uncertainty. The proposed solution is lightweight, fully decentralized, and suitable for a variety of settings where centralized control is infeasible or costly. Potential applications include collaborative scheduling, sensor coordination, and adaptive task routing in large-scale systems.

This paper presents new algorithms for area coverage by mobile robotic swarms in complex and unknown environments. The robots are silent and not aware of the team’s size, do not discern between each other, lack access to a positioning system, and cannot play distinct roles and so should be driven by a common control rule. On the positive side, they determine the relative positions of the objects, including the boundary of the handled area and obstacles, within a given finite sensing range and have access to a common direction. The proposed algorithms are attributed the physics-inspired virtual force-based (VFB) approach. To highlight their benefits, the paper reports on comparative analysis of seven methods, including the above two ones and an essential group of well-established algorithms that follow VFB approach and are suited to handle the examined scenario. The analysis is carried out using a whole range of various metrics, which capture different aspects of the performance quality. Moreover, a new criterion of coverage uniformity is introduced and justified. Extensive computer simulations in complex scenes have shown that the proposed algorithms demonstrate the best performance in terms of coverage uniformity and percentage.

We study the distributed nonconvex resource allocation with Limited Communication Data Rate (LCDR) over a communication network. Each node in the network has its own private cost function and determines the optimal resource allocation through interactions solely with its neighboring nodes. The nodes need to cooperatively minimize the total cost function to achieve the optimal resource allocation under the constraint of constant total resources. First, we consider exact communication. By Lagrange dual method, we propose a successive convex approximation-based distributed dual gradient tracking algorithm to solve the distributed nonconvex resource allocation problems. Then, we consider the case of digital communication among nodes based on LCDR. The information transmission among nodes is based on the Dynamic Encoding and Decoding (DED) with finite-level uniform quantization. We propose a successive convex approximation-based distributed dual gradient tracking algorithm with LCDR and conduct numerical simulations. The numerical results show that the algorithm converges based on merely one-bit quantizers when appropriate step sizes and scaling functions are chosen.