The Denavit-Hartenberg (DH) convention, while foundational in robot kinematics, contains a critical representational flaw in a specific class of anthropomorphic manipulators. This paper identifies and formalizes the "vanishing length problem", a singularity arising in kinematic chains featuring three consecutive joints where the z (yaw), x (roll), and y (pitch) axes form a mutually orthogonal triad. This configuration, characteristic of sequences like Roll-Yaw-Pitch or Pitch-Yaw-Roll, causes the origin of the middle (yaw) joint’s frame to become coincident with the origin of the subsequent joint’s frame. Consequently, the two physical link lengths preceding and following the yaw joint collapse into a single, combined length in the kinematic model, thereby losing a critical degree of freedom in the representation. We introduce the Auxiliary Virtual Joint (AVJ), a passive joint with a fixed constraint, to decouple this collapsed chain. The AVJ reintroduces the missing frame origin, restoring the two distinct link lengths and ensuring kinematic integrity. We provide a complete kinematic and dynamic formulation of the AVJ-augmented system, proving its equivalence to the physical manipulator. Validation on a 6-DoF humanoid arm demonstrates kinematic consistency to machine precision and, through dynamic simulations in MATLAB, confirms the feasibility of computing physically consistent joint torques—a computation rendered impossible for the middle yaw joint in the degenerate model due to the indeterminate inertial properties of the collapsed link.
The problem of ensuring the quality of technical documentation in the development of cyber-physical systems (CFS) was considered. It has been shown that inconsistencies in requirements, specifications and design solutions are one of the main causes of errors in the integration and testing stages, which is critical for the tasks of automation and management of complex technical objects. Disclosed is a method for automated verification of connectivity of technical documentation based on an optical-wave model of semantics. The novelty of the approach lies in the presentation of the document as a sequence of semantic events displayed in Minkowski space with the introduction of "semantic time" and light cones, which makes it possible to formalize causal relationships between requirements and their implementation. To quantify the quality of documentation, the following was introduced: coherence coefficient C, which characterizes structural connectivity; Coverage coverage index, which assesses the completeness of implementation; the proportion of logical breaks R; integrated quality metric Q. The method was tested on a case of 15 educational design works on mechatronics and automated control systems. A high correlation of the proposed metrics with expert assessment is shown: the Q integral metric shows a strong correlation (ρ = 0.94, p <0.001), the coverage index of Coverage requirements is ρ = 0.85 (p <0.001). ROC analysis confirmed the high diagnostic ability of the Q metric (AUC = 1.00). The proposed method can be used as an element of the CSF development quality management system to automatically review project documentation and ensure traceability of requirements.
Springs are an important component of many mechanical products used in mechanical engineering, instrumentation and other industries. Despite the apparent simplicity of manufacturing the springs themselves, modern spring winding machines are complex multi-axis machines capable of winding springs with variable pitch at wire feed speeds up to 500 m/min, as well as performing automatic correction of geometric parameters based on intermediate optical control. Despite the importance of springs for industry, disproportionately fewer research papers have been devoted to control systems for spring-loading machines compared to other types of machine tools. This research attempts to close this gap by developing a universal approach to automating the winding of springs based on the use of electronic cams. The paper proposes a three level software and hardware architecture for spring coiling machine as well as three level hierarchy of electronic cams. It includes the implementation of a lower-level control system based on existing industrial solutions that are not subject to restrictions on dual-use systems, with automatic generation of control programs based on standardized tabular descriptions of springs, with the further possibility of optimizing them. The conducted experimental studies demonstrate that the application of this approach to various types of machine tools allows winding springs up to 5 times faster than on currently used machines based on CNC systems, and the application of the developed algorithm for automatic spring pitch correction has provided up to 1.26 times a reduction in the spread of spring lengths and minimizing the number of defects.
The problem of fault identification in technical systems described by linear equations under the external disturbances is considered. The problem is solved based on sliding mode observers of two types: on the basis of high-order observers and on the basis of observers with weakened existing conditions. The peculiarity of such observers is that they do not rely on the matching condition, which is necessary for designing conventional sliding mode observers, but require the fulfillment of minimum phase condition for the original system. In contrast to the standard approach, both types of observers are designed not on the basis of the original system, but on the basis of its reduced-order model, which has selective sensitivity to faults and disturbances. The model is constructed in an identification canonical form and allows reducing the complexity of estimation procedure and removing the minimum phase condition. To implement the first type, an ordinary reduced-order Luenberger observer and a high-order sliding mode observer are constructed using information from the first observer; the identification expression uses information from the second observer and data on the coefficients of the characteristic equation of the first observer. To implement the second type, a reduced-order sliding mode observer is designed, which directly generates the required estimate. The disadvantage of high-order observers is that they require the linearity of the original system. In some cases, the disadvantage can be overcome by using virtual sensors, which are nonlinear observers that estimate the unmeasured components of the system state vector. An example is given that demonstrates the capabilities of virtual sensors. Based on the Matlab package, the original system and the constructed observers were simulated both in the absence of measurement noise and in their presence. The simulation confirmed the correctness of the assumptions and theoretical constructions and showed that high-order observers are less sensitive to such noises.
A methodology for intelligent control of the detonation-gas spraying (DGS) process of reactive Ni/Al composite coatings is presented, based on a Decision Support System (DSS) integrated with a digital twin of the technological process. The proposed hierarchical control architecture includes PLC-based sequencing, real-time stabilization of jet parameters using diagnostic data, predictive quality regulation, and a multi-objective optimization loop ensuring adaptive adjustment of process modes. The coating responses were evaluated from SEM image analysis: average pore area, number of unmelted particles, specific length of interphase boundaries, and fraction of pre-formed intermetallic phases. The relationships between technological parameters (barrel filling degree, C2H2/O2 ratio, spray distance, powder feed rate, gas temperature, and pressure) and structural characteristics were described using the Response Surface Methodology (RSM) with second-order regression models. Analysis of variance (ANOVA) confirmed the statistical significance of the factors and the adequacy of the model. Simulation and experimental verification demonstrated that gas temperature and spray distance exert the strongest influence on the formation of interphase boundaries. Optimization using the Harrington desirability function combined with a genetic algorithm enabled minimization of porosity and unmelted particles, while maximizing specific length of interphase boundaries. The mean prediction error across all structural metrics did not exceed 5—8 %. The developed DSS provides adaptive control and automatic optimization of DGS parameters, significantly reducing experimental workload and improving the reproducibility of coating structures. The methodology is suitable for integration into intelligent control systems and digital twin platforms for thermal spraying processes. Future work will focus on applying machine-learning-driven hybrid models combining empirical and physicochemical simulations for enhanced prediction accuracy and autonomy.
Solution of the design problem of nonlinear control systems is accomplished usually using some transformations of mathematical models. In this case, it is convenient to use the mathematical identities of the algebra of polynomials, vectors, and matrices with numerical and functional coefficients, that are proven in this paper. These identities can be used for the transformations of the mathematical models of both the linear systems with constant parameters and studying the nonlinear control systems represented by quasilinear models. These polynomial-matrix identities also have independent significance, as they can be applied to the algebraic transformations of both some vector-matrix expressions and polynomial-matrix expressions with complex arguments. Applying these identities to the state-dependent coefficients models of control systems is problematic, since these models very often describe nonlinear plants and systems approximately. The polynomial-matrix identities presented below are proved by the equivalent transformations of the operator equations in the state variables of the nonlinear feedback control systems represented by the quasilinear models. These models can accurately represent plants and systems defined by nonlinear differential equations in Cauchy form and output equations, it is only important that the nonlinearities of these equations are differentiable with respect to all their arguments. Using some of the proven polynomialmatrix equalities, the following were obtained: the solution of the eigenvalue placement problem for the system matrix of quasilinear models of closed-loop systems; the controllability criterion of the nonlinear plants output; and the controllability criterion of nonlinear closed-loop systems by reference signals. Two examples of nonlinear plants with uncontrollable output are given, as well as numerical examples demonstrating the correctness of the obtained polynomial-matrix identities.
The output feedback controller for the linear multivariable system, developed in the first part of this work, is analyzed. This controller guarantees specified or achievable performance in terms of control errors, stability margins, and response time. The solution to the synthesis problem is based on a standard H∞-optimization procedure, formulated in a special way. This second part provides a physical The output feedback controller for the linear multivariable system, developed in the first part of this work, is analyzed. This controller guarantees specified or achievable performance in terms of control errors, stability margins, and response time. The solution to the synthesis problem is based on a standard H∞-optimization procedure, formulated in a special way. This second part provides a physical interpretation of the stability margin radii for a multivariable system. The interpretation is given in terms of Nyquist plots with breakpoints at individual plant inputs and constitutes the essence of Theorem1. Namely, the Nyquist plot must not touch or enter a circle of radius r i centered at the critical point (–1, j0), where r i is the stability margin radius guaranteed by the design procedure for the i-th control input of the plant. This theorem has significant practical value for engineers, as it enables the experimental determination of the stability margin radius for each individual control channel at the physical input of the plant. А direct relationship between the absolute stability of a closed loop multivariable system with sector nonlinearities at the plant input and its stability margin radii is established. This result is formalized in Theorem 2. In particular, by applying the circle criterion of absolute stability, the theorem establishes that the closed loop system remains absolutely stable for time-varying nonlinearities introduced at each control input of the plant. In this case, the sector for each nonlinearity physically representing actuator nonlinearities is uniquely determined by the guaranteed stability margin radius in each channel at the physical input of the plant. The proposed approach is illustrated by a controller design example for a load-coupled electric drive, demonstrating its practical relevance.
Identifying dynamic systems from data is a complex problem, where a key requirement of modern research is not only accuracy but also model interpretability. Although highly effective, the symbolic regression method based on genetic programming has inherent limitations, the most important of which is stochasticity, leading to instability of results. In this paper, a new hybrid method, GP-SINDy, is proposed to overcome these shortcomings. Its core idea is to combine two approaches: genetic programming performs a global search for the model structure, while sparse identification fine-tunes the corresponding parameters. The effectiveness of the proposed method was validated through comprehensive computational experiments. On test data, GP-SINDy demonstrated the ability to find models with an optimal balance of accuracy and complexity, outperforming the baseline genetic programming algorithm. Analysis on noisy data confirmed the increased efficiency of the proposed method. Verification on a real system demonstrated the practical applicability of the approach for constructing adequate analytical models. Thus, the GP-SINDy hybrid method represents a powerful and versatile tool for automatically deriving interpretable system dynamics equations, opening up new possibilities in various fields of science and engineering.
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 paper addresses the problem of developing a control system for a group of multirotor unmanned aerial vehicles (UAVs) collaboratively transporting a payload. The importance of this problem arises from the growing interest in cooperative UAV systems for logistics, industrial automation, and rescue operations. The configuration of UAVs under consideration is characterized by the payload being attached to a rigid frame and each UAV connected to it through a spherical joint, forming a mechanically coupled system. Such a configuration is of particular interest for studying complex dynamical systems with mechanical couplings and for developing effective methods of cooperative control. To support analysis and controller design, a mathematical model is proposed that describes the dynamics of both individual UAVs and the coupled system as a whole, including the interaction between the aerial vehicles and the payload. Based on this model, a control algorithm was developed to ensure stable and coordinated motion of the UAV group along prescribed trajectories while maintaining the required payload orientation. The study demonstrates that the proposed algorithm stabilizes the UAV system under external disturbances and during motion along complex flight paths, while also demonstrating scalability to larger UAV groups and various payload configurations. Simulation results validate the effectiveness of the developed control system. The findings can be applied in the design of real-world cooperative transport prototypes and contribute to the advancement of cooperative UAV control methods. The presented results hold practical significance for logistics, delivery of goods to hard-to-reach areas, and the collaborative use of UAVs in various application fields. Moreover, the proposed approaches may serve as a foundation for further research in the field of cooperative unmanned system control.
In the first part of the article, the authors proposed a comprehensive method for solving the problem of synthesizing combined position-force control systems (CS) for electric drives (ED) of multi-link underwater manipulators (MUM) mounted on autonomous underwater vehicles (AUV) operating in mode of landing on a seabed or on work sites, followed by rigid fixation of these AUV using special devices. To do this, the following subtasks were successively solved. First, the synthesis of self-adjusting regulators was performed, ensuring the stabilization of variable dynamic parameters of the ED at a given nominal level. Secondly, a synthesis of observers with a variable structure has been performed, which allows using only measurements from angle sensors of the ED output shafts when creating MUM CS. And thirdly, the synthesis of position-force regulators has been performed, which, by minimizing the selected quadratic cost function, make it possible to ensure accurate working out of the specified movements of the ED output shafts while maintaining the required moments on them. The second part of the paper describes the operation of the MUM position-force CS, which makes it possible to create the required force effects with its work tool on the surface of work objects during its movement along the trajectory. Moreover, this CS ensures the successful performance of force operations in the presence of continuously changing and previously unknown parameters of the interaction of the MUM links with a viscous medium, including the velocity of the liquid flow, viscous friction and the MUM links added masses and moments of inertia. The operability and effectiveness of the synthesized position-force control systems is confirmed by the results of computer modeling, the analysis of which made it possible to determine the conditions under which it is necessary to accurately take into account the various features of the impact of a viscous medium on the MUM links when performing complex technological operations.
The problem of transporting payloads suspended from a quadcopter is gradually acquiring not only theoretical but also practical importance. If the mass and size of the payload are large enough, control algorithms should take into account its motion relative to the copter and aerodynamic forces acting on the payload. Special attention should be paid to preventing large-amplitude payload oscillations, since such oscillations can lead to emergency situations. This paper considers a mechanical system consisting of a quadcopter and a spherical cargo suspended from its center of mass on a weightless rod using a spherical hinge. The system can perform spatial motion in a wind flow, the speed of which is assumed to be constant and directed horizontally. The drag force acting on the payload is taken into account. The controllability of the system in the vicinity of the uniform rectilinear flight is discussed. It is shown that the system is not completely controllable, with the uncontrolled variables corresponding to payload rotation about the axis coinciding with the rod. The remaining variables are completely controllable (at least if the aerodynamic force is small enough). To stabilize the uniform rectilinear flight, a control is constructed, optimal in the sense of the standard quadratic functional. The problem of the motion of the copter along a target sufficiently smooth trajectory with a given cruising speed, while preventing intense oscillations of the payload, is considered. An algorithm is constructed to control the forces generated by the copter’s rotors, which ensures the motion of the system along the target trajectory and prevents the occurrence of high-amplitude payload oscillations.
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.
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.
Quaternion solution of the problem on optimum control of a turn of a spacecraft (as solid body) from an arbitrary initial into an assigned final angular position taking into account the degree of loading of the construction is considered. The solved problem differs in use of new criteria of optimality. Optimization of control process is based on the combined functional of quality that combines in a given proportion time spent on spacecraft rotation and the integral of quadratic form relative to angular velocity (this quadratic form reflects dynamical loads on spacecraft construction). The proposed control method for spacecraft rotation improves conditions of turn in sense of minimum loading of spacecraft construction. Analytical solution of optimal control problem is obtained on the base of maximum principle with use of quaternionic models of the solid body motion controlled. The properties of optimal motion of a spacecraft are revealed in an explicit form, the structure of optimal control is specified. It is shown that degree of spacecraft construction loading during reorientation maneuver does not exceed the required value which is determined by coefficients of the minimized functional; and time of rotation is minimum also (as it is possible under given value of dynamical loading). To construct optimal control program, formalized equations and calculation formulas are written. Analytical equations and relations are presented for finding optimal control. Key relationships determining optimal values of the parameters of control algorithm for spacecraft turning are described. In the case of axially symmetric loading of spacecraft construction, solution to the problem of spatial reorientation is obtained in closed form. A numerical example and results of mathematical modeling that confirm the practical feasibility of the developed method for control of spacecraft reorientation are given. Significance of the investigated problem of spacecraft optimal control is caused that often, allowable loading of spacecraft board and its elements of construction is significant restriction.
This paper presents a novel approach to finite-time stabilization of dynamic systems, differing from classical nonsmooth solutions. The problem of finite-time control for linear dynamic plants is addressed, including the case with external disturbances, under strictly predefined constraints on the output signal trajectories starting from the initial time instant. The solution is presented in an order of increasing complexity, beginning with the scalar unperturbed case and proceeding to the general case of an arbitrary-order linear system with unknown bounded external disturbances. The imposed constraints can be motivated either by technical requirements on the plant behavior or empirically, according to preferred transient performance specifications. It is shown that in the controller synthesis procedure, one can define the form of output constraints so that the finite-time stabilization condition for the controlled variable is satisfied using bounded control input. The plant state is assumed to be known, with initial conditions either exactly known or belonging to a known bounded set; controllability and observability conditions are fulfilled. The proposed method is based on the idea of applying a functional transformation to the output signal, which allows reformulating the original constrained problem into an unconstrained stability problem with respect to a new variable. It is proven that such a transformation exists and that its inverse ensures the solution of the original problem while maintaining bounded control signals. The resulting control algorithm is compared with known results in finite-time control via computer simulations. It is demonstrated that the proposed method provides comparable regulation performance in terms of control effort, while offering greater flexibility in choosing closed-loop system trajectories.
The article proposes a solution for developing a steam pressure control system in the common steam main of the Blagoveshchensk Thermal Power Plant with automatic selection of parameters based on a genetic algorithm. A possible steam output of a boiler control option at a power plant is described. It involves the interconnected operation of five key components: the main controller — the upper-level control system; the heat load controller, mill loading controllers, primary air and air mixture controllers — the subordinate control loops. The main controller operates under conditions of significant uncertainty and impacts the performance of the entire heat and power generation system. The mathematical model of the control plant was obtained using a passive experiment based on an annual data on the operation of the BKZ(E)-420-140 boiler. The control plant can be represented by a system with a constant delay, changing parameters and structure in various operating modes, with a relative degree of the transfer function greater than or equal to unity. In this article we implement a procedure for the complete synthesis of the control system using: two equivalent filter-correctors — a setting and an output one, an implicit reference model, an adaptive-robust control algorithm synthesized based on hyperstability criterion (at the structural synthesis stage), and a genetic algorithm (at the parametric synthesis stage). At simulation the performance of two control algorithms was compared: the developed adaptive-robust control algorithm and the classical proportional-integral control algorithm.The results obtained in this article can be used to ensure the efficiency of large thermal power systems.
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 formulates the task of controlling a group of drones to deliver goods to the orders of the population in an urban environment, for example, food and medicines. To solve the problem, it is proposed to create an intelligent drone group resource management system (ISUR-Drones), which should operate autonomously (unpopulated) 24/7 and provide the ability to automatically select and distribute orders for drones, build routes and plan drone operations, optimize (while there is time), monitor and control the execution of plans, as well as adaptive realignment of plans for new orders or other events that occur in real time. To implement an ISUR Drone that performs the functions of an autonomous "smart control room", a model of an ontologically configurable multi-agent network of needs and capabilities and a modification of the adaptive resource planning method for a group of drones are proposed. The functions and architecture of ISUR Drones have been developed and a prototype of the system has been implemented using the example of deliveries in the city of Tolyatti. The high adaptability of the developed system is shown, ensuring the maximum possible satisfaction of consumer wishes and high efficiency in using drone resources.
This paper considers the problem of estimating an unmeasured state vector for a class of nonlinear dynamic systems with parametric uncertainties and output delay. Such systems are widely encountered in control problems involving technical objects operating under uncertain external disturbances, limited availability of measurement information, and bounded data transfer rates. The class of systems under consideration is characterized by unit relative degree and the presence of additive nonlinearities that exhibit a nonlinear dependence on the measured output signal and a linear dependence on the vector of unknown parameters and the state vector. The magnitude of the delay is assumed to be known. The proposed approach to solving the estimation problem is based on a multi-stage observer synthesis procedure, which includes three steps. In the first stage, an unknown input observer for delayed state vector is developed, which ensures the formation of auxiliary estimates. Its application provides the complete elimination of the influence of the unknown input signal on the dynamics of the observation error. At the second stage, an algorithm for estimating the vector of unknown system parameters is constructed based on the obtained estimates. This problem is solved by transforming the original system into a linear regression model, followed by parametric identification based on the gradient descent method. At the final stage, a filtering procedure is introduced, which reduces the problem of estimating the state vector of the original nonlinear system to the problem of identifying the parameters of a linear regression, which is also solved using the gradient descent method. The obtained theoretical results confirm the asymptotic convergence of the estimates of the state vector and unknown parameters to their true values. The performance and effectiveness of the developed method were verified through testing using computer simulation. Obtained unknown parameter estimates and the estimation errors of the state variables of a nonlinear system are presented in the article and confirm the correctness and stability of the estimation process.
