Article deals with the consideration of a self-organizing tracking system synthesis for one class of nonlinear dynamic plants operates under constant external disturbances. The control plant consists of several subsystems that are generally unstable and have different relative degrees. The switching between these subsystems occurs at arbitrary points in time according to a given signal, while both the parameters and the structure of the object are subject to parametric and structural uncertainties. Additionally, the mathematical model of the object includes a nonlinear dependence on phase coordinates, which is typical of systems with unmodeled dynamics. The following solution methods are used: cybernetic approach to the development of self-organizing systems, associated with the formation of a strictly minimal phase system using high-speed correction filters; hyperstability criterion, which allows the synthesis a nonlinear combined (adaptive-robust), control law; approach to constructing L-dissipative dynamic systems under the influence of structural disturbances. In the final part of the article, we consider the control problem for plant with switches consisting of two subsystems of the third order. We note that through the specially designed high-speed correction filters and automatic structural self-organization of the regulator, we can achieve targeted improvement in the control system quality. The results obtained can be used to develop highperformance self-organizing control systems for complex dynamic plants. These systems can effectively operate in conditions of changing structure and parameters, as well as when there are disturbances and switching. Additionally, the results are useful for developing control systems for other types of switched plants.

The paper identifies the existing disadvantages of typical control systems for casting units for the production of aluminum ingots. Local control algorithms for individual components of a foundry unit (mixer, metallograft, casting machine) based on previously developed proprietary mathematical models are presented. Based on individual models, a comprehensive control algorithm has been developed that allows for the coordinated functioning of all components of the unit. Special attention is paid to controlling the thickness of the ingot’s cortical zone, which allows for timely response to a critical decrease in thickness and automatic adjustment of control actions to avoid breakthroughs of the ingot wall and subsequent metal explosions. Also, based on the heat distribution obtained by the mathematical model, the Niyama criterion is monitored in real time, which makes it possible to assess the quality of the microstructure of the ingot and the casting speed to ensure the absence of cold and hot cracks in the ingots. This makes it possible to switch from reactive stabilizing control to proactive predictive control, which increases the yield of usable products, process stability and industrial safety. The developed set of models and algorithms is the basis for creating a digital twin of the unit and predictive control systems.

This paper addresses the problem of motion control of a multi-agent robotic formation operating in dynamic environments containing both static and dynamic obstacles. А hierarchical distributed control framework is proposed to coordinate the motion of multiple agents while maintaining a prescribed geometric formation during navigation. The developed architecture consists of four interconnected levels: an agent coordination level, a path planning level, a coordinated path-following level, and an individual agent motion control level. At the coordination level, a distributed mechanism based on the average consensus protocol is employed to generate a consistent estimate of the current position of the formation center using only local information exchange between neighboring agents. The path planning problem for the formation center is formulated as a nonconvex model predictive control (MPC) optimization problem that accounts for dynamic constraints and obstacle avoi dance requirements. To enable efficient numerical implementation, the resulting nonconvex problem is solved using a sequential convex programming (SCP) approach, which approximates the original problem through a sequence of convex optimization subproblems. For trajectory execution, distributed control algorithms are developed to ensure coordinated path following along both straight-line and circular segments of the planned route. These algorithms rely solely on local inter-agent communication and enable the formation to preserve the desired geometric configuration while moving along the path. The motion of individual agents is modeled using nonholonomic dynamics, which provides a sufficiently general representation applicable to a wide range of mobile robotic platforms, including ground, aerial, and marine vehicles. The proposed hierarchical distributed control strategy enables scalable and robust coordination of multi-agent formations in dynamic environments. The effectiveness of the developed algorithms is demonstrated through numerical simulations performed in the MATLAB environment, confirming stable formation motion and successful obstacle avoidance during navigation.

The article presents an extended comparative analysis of four modern methods of nonlinear robust control for a magnetic levitation system: adaptive backstepping, the integral adaptation method of synergetic control theory, the synthesis of sliding mode control based on a sequential set of invariant manifolds within synergetic control theory, and classical sliding mode control. For each method, the procedure for synthesizing the control law is described in detail, considering parametric disturbances caused by changes in the active resistance of the electromagnet, which is a typical problem in real systems. А detailed analysis of the closed-loop system stability is performed, and the dynamics are simulated under parametric disturbance. The simulation and comparison results show that methods based on synergetic control theory provide a simpler and more transparent stability analysis, as well as increased robustness to changes in system parameters. In particular, these methods allow avoiding the effect of high-frequency switching in control signals (chattering), typical for classical sliding mode control, which is a significant advantage for practical implementation in industrial and scientific applications. The adaptive backstepping method with dynamic disturbance parameter estimation demonstrated some sensitivity to parametric changes, requiring additional tuning for optimal operation. The obtained results highlight the practical applicability and effectiveness of synergetic control theory methods over classical approaches, opening new prospects for the development of reliable, stable, and precise control systems in high-tech areas, including transportation magnetic levitation technologies, nanopositioning systems, and vibration isolation. This work contributes to expanding the methodological toolkit in the field of adaptive and robust control of nonlinear electromechanical systems, focusing on improving positioning accuracy while maintaining control quality and system stability under internal disturbances.

The paper examines the issue of feature selection in the construction of classification models for the diagnosis of the aircraft electromechanical actuators (EMA). The widespread use of EMA in aircraft with a high degree of electrification (electric aircraft, unmanned aircraft) and the need to ensure flight safety determines the relevance of the research conducted. This problem of feature selection should be solved to reduce the extent of the analyzed data and increase the efficiency of algorithms for assessing the technical condition of the aircraft EMA. The servo actuator of an unmanned aircraft of an airplane type, which is used to deflect steering surfaces, is considered as an object of research in the work. At the same time, the main attention is focused on internal methods and filtering methods, which are based on simplified models that allow assessing the importance of features and do not require significant computational efforts. This paper presents results of comparing the methods of feature selection based on data obtained as a result of mathematical modeling of the operation of the servo actuator of an unmanned aircraft in various technical conditions. 

This article addresses the critical challenge of developing optimal control algorithms for ballistically linked groups (BLGs) of small satellites within broadband communication constellations. The research is driven by the global shift towards large-scale low Earth orbit (LEO) megaconstellations, where precise, long-term, and fuel-efficient maintenance of the orbital structure is paramount. The core difficulty stems from the severe mass, size, and power constraints of CubeSats, which render traditional high-propellant control strategies impractical. The primary objective is to design, compare, and validate algorithms for accurate formation flying while minimizing propellant consumption, thereby extending mission lifetime and reducing operational costs. The study uses model relative motion in a near-circular orbit. Transfer of a deputy spacecraft to a 100 km along-track separation serves as the test case. The first method applies Pontryagin’s maximum principle, solving the two-point boundary value problem via a Newton metod. The second employs a predictive control framework. The third strategy is based on parametric optimization of a predefined control input structure, with subsequent gradient-based correction. Numerical simulations for different transfer durations confirm the efficacy of all methods. The Newton method provides exceptional accuracy in meeting terminal state constraints at a fixed final time. The predictive controller demonstrates superior fuel economy by incorporating extended passive coasting arcs. The parametric optimization approach offers implementation flexibility, albeit with sensitivity to initial parameter guesses. This comparative analysis confirms the practical viability of these algorithms for the autonomous, fuel-conscious station-keeping and reconfiguration of future small-satellite communication swarms, a vital capability for next-generation global connectivity networks.