<?xml version="1.0" encoding="utf-8"?>
<rss version="2.0">
    <channel>
        <title>Control and Cybernetics Feed</title>
        <link>https://sciendo.com/journal/CANDC</link>
        <description>Sciendo RSS Feed for Control and Cybernetics</description>
        <lastBuildDate>Sun, 10 May 2026 13:18:13 GMT</lastBuildDate>
        <docs>https://validator.w3.org/feed/docs/rss2.html</docs>
        <generator>https://github.com/jpmonette/feed</generator>
        <image>
            <title>Control and Cybernetics Feed</title>
            <url>https://sciendo-parsed.s3.eu-central-1.amazonaws.com/64711fb02b88470fbea1577a/cover-image.jpg</url>
            <link>https://sciendo.com/journal/CANDC</link>
        </image>
        <copyright>All rights reserved 2026, Systems Research Institute Polish Academy of Sciences</copyright>
        <item>
            <title><![CDATA[Radial basis function neural network based higher order sliding mode control]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2025-0013</link>
            <guid>https://sciendo.com/article/10.2478/candc-2025-0013</guid>
            <pubDate>Mon, 09 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[

This paper presents a Radial Basis Function neural network based higher-order Sliding Mode Control for robust control of a dynamical system. A conventional sliding mode controller is suffering from a chattering problem, and the Super Twisting Algorithm is a special kind of higher-order Sliding Mode Control that has the capability of minimizing the chattering problem. There are also unknown model parameters and external disturbances that exert a negative influence on the control performance. To address the issues of model uncertainties and chattering, a Radial Basis Function (RBF) neural network based Super Twisting Algorithm is designed. The RBF neural network evaluates the model parameters and uncertainties, while the Super Twisting approach mitigates chattering, hence improving the controller’s overall performance. Lyapunov stability based adaptive laws are derived for online updating of the parameters of the neural network. The proposed control algorithm was tested on a 2-degree-of-freedom serial flexible joint robotic arm to investigate its efficacy. The controller has a lower control chattering amplitude, lower control energy consumption, and a good tracking response, when compared to the RBF based conventional Sliding Mode controller and simple STA controller, as shown by the results.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Monitoring of segmented processes using p-box aggregated data and fuzzy control charts]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2025-0015</link>
            <guid>https://sciendo.com/article/10.2478/candc-2025-0015</guid>
            <pubDate>Mon, 09 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[

The monitoring of inhomogeneous and non-stationary processes composed of segments and subsegments is considered. The structure of this segmentation is typical for medical data, describing voice characteristics of Bipolar Disorder (BD) psychiatric patients, calculated from their recorded smartphone calls. Data from subsegments are described by different probability distributions and are represented by histograms. Then, data from subsegments belonging to the same segment are aggregated using probability boxes (p-boxes) methodology and a simple probabilistic method. Finally, the mean value of each of the aggregated segments is described by a fuzzy triangular number. Therefore, the stream of consecutive segments is represented by the stream of fuzzy numbers. Several control charts for such fuzzy data are proposed. Their statistical properties are evaluated using simulated synthetic data. The simulation model is related to the real-life data obtained from the monitoring of BD patients. The results of simulations demonstrate the applicability of the proposed procedure for monitoring of BD patients.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Cluster-wise modelling: some issues considered through the example of migrations at municipality level in Poland]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2025-0017</link>
            <guid>https://sciendo.com/article/10.2478/candc-2025-0017</guid>
            <pubDate>Mon, 09 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[

An experiment is presented of cluster-wise modelling, related to modelling and analysis of internal migration flows in Poland at the municipality level (some 2500 units). The experiment was carried out within a project, in which it was assumed that migration flows linearly depend upon unemployment. This simple dependence was positively verified, and the associated model error maps, corresponding to the consecutive years over two decades (2003-2022) provide a very clear and telling spatial image.
Yet, the models obtained were statistically rather feeble. So, it was decided to experiment, in particular, with a set of analogous models, identified for subsets (clusters) of municipalities, employing a simile of classical k-means clustering procedure. Given the known dependence of the outcome from the k-means-like procedure upon the starting point, various initial configurations were considered. The results are exemplified in this paper, the problems appearing indicated, and some broader conclusions are drawn therefrom.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[A multi-criteria decision problem based on information uncertainty conveyed by intuitionistic interval-valued fuzzy sets]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2025-0016</link>
            <guid>https://sciendo.com/article/10.2478/candc-2025-0016</guid>
            <pubDate>Mon, 09 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[

In a multi-criteria decision problem, represented by intuitionistic interval-valued fuzzy sets that cannot be properly ranked linearly, it is not always possible to adequately estimate the uncertainty of information using a single-degree scalar measure. We analyze four different ways of quantifying information uncertainty, using an overall measure and three types of directional measures. The assessment of different types of information uncertainty is performed in the context of hypothetical and possible changes in the degree of membership and non-membership for a given degree of hesitation of an element in the set (i.e. in terms of permissible changes in the assessment of information uncertainty). This approach allows for taking into account the asymmetric nature of the assessment of changes in information uncertainty, perceived in the real physical world from a human perspective. We propose a ranking method for alternatives/options (separately for each type of information uncertainty), which, additionally, allows for taking into account the level of information certainty in the process of selecting the best alternative. We analyze examples meant to illustrate our approach and show that it allows for the analysis of alternatives from four complementary perspectives based on information uncertainty, something that currently used methods cannot do. We present an example illustrating the comparison of our results with selected results from the literature.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Intelligent maximum likelihood self-adjustable block roots assignment for a class of MIMO stochastic systems]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2025-0014</link>
            <guid>https://sciendo.com/article/10.2478/candc-2025-0014</guid>
            <pubDate>Mon, 09 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[

This paper presents a new self-adjustable block roots assignment control scheme for multivariable stochastic systems, via the use of conventional intelligent MIMO maximum likelihood identification algorithm, handled with an adaptive neural based fuzzy inference system (ANFIS). The proposed state-space self-tuning control methodology can be applied to the multivariable stochastic system without requiring prior knowledge of system parameters and noise properties. Illustrative examples demonstrate the effectiveness of the proposed approach.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Optimality conditions for ε-weak local quasi-efficient solutions of D.C. vector optimization problems]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2025-0010</link>
            <guid>https://sciendo.com/article/10.2478/candc-2025-0010</guid>
            <pubDate>Thu, 22 Jan 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[

In this paper, we address a non-convex vector optimization problem, in which the objective function and constraints are defined as differences of convex vector-valued maps. By employing a separation argument, we derive necessary optimality conditions, expressed in terms of ε-regularized subdifferentials, for a point to be an ε-weak local quasi-efficient solution. To ensure the paper is self-contained, we also present sufficient optimality conditions and provide examples to illustrate the results.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Implementing a combined defuzzification: evaluation of COBYLA and SLSQP]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2025-0011</link>
            <guid>https://sciendo.com/article/10.2478/candc-2025-0011</guid>
            <pubDate>Thu, 22 Jan 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[

The combined defuzzification of multiple fuzzy sets under a shared constraint is an extension of traditional defuzzification. The problem concerns defuzzifying multiple fuzzy sets at once, adhering to a constraint that involves their defuzzified values. This specific problem emerged in an application of fuzzy rulebase systems with the goal of regridding spatial data; the constraint stems from the need to keep the total modelled value over a region consistent. Through the introduction of goal functions, the combined defuzzification problem was translated to an optimization problem; the goal functions allow to use objective criteria to evaluate and rank different solutions. Different goal functions for the combined defuzzifier have been presented in the relevant literature, in this contribution, the aim is to evaluate solvers in order to develop a usable implementation of the novel defuzzifier and to verify their stability and performance for the presented problem.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[The use of Octree in point cloud analysis with application to cultural heritage]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2025-0012</link>
            <guid>https://sciendo.com/article/10.2478/candc-2025-0012</guid>
            <pubDate>Thu, 22 Jan 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[

In this article, we propose a method for analysing 3D point clouds. To this aim, we take advantage of the octree data structure to compress and analyse the 3D point data. The motivation for the study undertaken comes from the field of Cultural Heritage. As 3D acquisition methods become more and more ubiquitous in this field, there is an increasing need for methods, which help to efficiently store, display, and share large data sets. There is also a need to segment and classify the recorded data. We show that the octree data structure provides an efficient tool for handling complex 3D data, coming from different applications. Although we tested our method on a data set, coming from the field of archaeology, based on photogrammetric method, we believe that the approach proposed has much wider use. The method proposed can be applied to any 3D data, represented in a given coordinate system.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Optimal block-mode assignment and relocation in matrix polynomials for observer-based static and dynamic multivariable feedback compensators]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2025-0009</link>
            <guid>https://sciendo.com/article/10.2478/candc-2025-0009</guid>
            <pubDate>Thu, 22 Jan 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[

This study introduces an observer-based dual strategy for optimizing block mode placement in matrix polynomial control systems, focusing on improving stability and performance in multivariable feedback applications. It introduces two approaches: a static state feedback compensator for a challenging Bidirectional Inductive Power Transfer (IPT) System, and a dynamic observer-based output feedback compensator for a defensive air-to-surface missile control problem. Both designs exploit the Grey Wolf Optimizer to solve the nonlinear convex optimization, associated with block mode selection. The dynamic plan employs Luenberger observer principles for unmeasured state estimation, ensuring system reliability through strict observability conditions. Simulation results reveal that the proposed optimal placement methods enhance tracking, boost stability margins, and substantially minimize control effort. Overall, this methodology offers an effective frame-work for robust controller design and state estimation across disparate, complex dynamic systems, while reducing computational burden and improving control efficiency.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Nonlinear optimal control for two cable-driven 3-DOF robotic cranes]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2025-0006</link>
            <guid>https://sciendo.com/article/10.2478/candc-2025-0006</guid>
            <pubDate>Sun, 21 Dec 2025 00:00:00 GMT</pubDate>
            <description><![CDATA[

The article is concerned with the nonlinear optimal control problem of two cable-driven 3-DOF robotic cranes. Such cranes can be used in ship maintenance and repair. These are underactuated robotic systems comprising a 2-cable driven cart with a payload suspended from it. Such robotic mechanisms have three degrees of freedom and can move in the entire 2D vertical plane. Using Euler-Lagrange analysis the state-space model of the two cable-driven 3-DOF robotic crane is obtained. It is also proven that this dynamic model is differentially flat. Next, to solve the associated nonlinear optimal control problem, the dynamic model of the two cable-driven 3-DOF robotic crane undergoes approximate linearization around a temporary operating point that is recomputed at each time-step of the control method. The linearization relies on Taylor series expansion and on the associated Jacobian matrices. For the linearized state-space model of the crane an optimal (H-infinity) feedback controller is designed. This controller stands for the solution to the nonlinear optimal control problem under model uncertainty and external perturbations. To compute the controller’s feedback gains an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. The proposed nonlinear optimal control approach achieves fast and accurate tracking of reference setpoints under moderate variations of the control inputs. Besides, the method avoids change of state variables and state-space model transformations and the control inputs it computes are applied directly on the initial nonlinear state-space model of the crane.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[A novel LAPN algorithm based path navigation approach for autonomous agents]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2025-0008</link>
            <guid>https://sciendo.com/article/10.2478/candc-2025-0008</guid>
            <pubDate>Sun, 21 Dec 2025 00:00:00 GMT</pubDate>
            <description><![CDATA[

This paper discusses multi-agent path navigation in unknown environments. It is a challenging task to design an effective communication-based algorithm with minimal error, which would ensure secure navigation of multi-agent paths under complex circumstances, reduce the length of the travelled path, and minimize the runtime. A leader agent path navigation (LAPN) algorithm is proposed in this paper for multi-agent communication. The obstacle avoidance mechanism is used in the first part of the algorithm. The execution time of the algorithm is influenced by the process of leader-to-follower path update, since the updating process determines how quickly the followers receive corrected trajectories. One leader and two follower agents were considered in simulation environments to establish the feasibility of the algorithm. The LAPN algorithm achieves an average percentage deviation, calculated as the relative difference from the ideal straight-line path of 2.82% in travel time and 31% in path length, showing satisfactory navigation efficiency under obstacle-constrained conditions.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[The Two-Constraint Binary Knapsack Problem’s average case analysis for constraints with small, moderate and large coefficients]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2025-0007</link>
            <guid>https://sciendo.com/article/10.2478/candc-2025-0007</guid>
            <pubDate>Sun, 21 Dec 2025 00:00:00 GMT</pubDate>
            <description><![CDATA[

The paper addresses the Two-Constraint Binary Knapsack Problem. It is assumed that some of the problem coefficients are the realizations of mutually independent random variables. Asymptotic probabilistic properties of selected problem characteristics are investigated for special cases of Lagrange multipliers with small, moderate and mixed values.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Optimality conditions for bilevel optimization with variational inequality constraints using approximations]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2025-0005</link>
            <guid>https://sciendo.com/article/10.2478/candc-2025-0005</guid>
            <pubDate>Sun, 21 Dec 2025 00:00:00 GMT</pubDate>
            <description><![CDATA[

We use first- and second-order approximations to establish necessary and suffcient optimality conditions for nonsmooth generalized bilevel optimization problems with variational inequality constraints. To transform the hierarchical problem into a single-level optimization problem, we employ two approaches: the gap function reformulation and the KKT reformulation. We compare these approaches and provide examples to illustrate the applicability of the results.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Insights into U-NET models with special focus on ultrasound and MRI medical image segmentation]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2025-0004</link>
            <guid>https://sciendo.com/article/10.2478/candc-2025-0004</guid>
            <pubDate>Sat, 29 Nov 2025 00:00:00 GMT</pubDate>
            <description><![CDATA[

The advent of deep learning enabled the extraction of complex feature representations from medical imaging data, which was considered impossible to be achieved with standard computer learning. The applications of deep learning in the field of medical image analysis a ord significant results. A key feature of deep learning techniques is their ability to automatically learn task-specific feature representations and extract relevant features without human intervention. Various deep learning models, including CNN, AlexNet, ResNet, DenseNet and U-Net were developed for medical image analysis. Among these models, U-Net is a popular model, used for medical image segmentation. The present article provides a comprehensive review of the deep learning segmentation models, which use U-Net and its variants, applied in the domain of medical image segmentation, specifically tailored to medical imaging modalities, such as ultrasound and MRI, along with respective pros and cons in the field of image segmentation. The analysis reveals that the performance of di erent U-Net variants varies significantly based on imaging modality and segmentation complexity.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[On the existence of proportional-integral observer for the state estimation of linear time-invariant systems]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2025-0002</link>
            <guid>https://sciendo.com/article/10.2478/candc-2025-0002</guid>
            <pubDate>Sat, 29 Nov 2025 00:00:00 GMT</pubDate>
            <description><![CDATA[

In this paper, the explicit necessary and sufficient conditions are established for the existence of proportional-integral observer for the state estimation of linear time-invariant continuous-time systems. In particular, it is proven that for a given linear time-invariant continuous-time system of order n, having m inputs and p linearly independent outputs, a proportional-integral observer of order n can be constructed if and only if the given system is detectable. Furthermore a simple procedure is given for the construction of proportional-integral observer. Our approach is based on properties of real and polynomial matrices.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[On dual problems of second order for (ηξ)-bonvex interval-valued control problems]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2025-0001</link>
            <guid>https://sciendo.com/article/10.2478/candc-2025-0001</guid>
            <pubDate>Sat, 29 Nov 2025 00:00:00 GMT</pubDate>
            <description><![CDATA[

The paper studies the dual problems of second order, associated with a new class of (η, ξ;)-bonvex interval-valued variational control problems. More precisely, by considering the corresponding necessary optimality conditions, we prove the associated duality (weak, strong, strictly converse) results under the new (η, ξ)-bonvexity assumptions of the involved functionals. In addition, illustrative examples are provided in order to highlight the theoretical elements established in the paper.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[An integral feedback linearization applied to an aeropendulum]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2025-0003</link>
            <guid>https://sciendo.com/article/10.2478/candc-2025-0003</guid>
            <pubDate>Sat, 29 Nov 2025 00:00:00 GMT</pubDate>
            <description><![CDATA[

Aeropendulum systems are nonlinear systems, in which a motor-propeller assembly drives a rod. They are used for educational purposes and testing control laws in real systems. Feedback linearization is a nonlinear control technique that algebraically linearizes a plant’s dynamics via a feedback law and has been applied to various systems. This paper designs a feedback linearization control law incorporating an integrator into the control loop. The integrator enhances robustness in respect to constant disturbances, but alters the closed-loop dynamics, preventing it from following exactly the dynamics, associated with the desired characteristic roots under constant input. To address this, the integrator’s initial condition is treated as an additional variable, selected to ensure the expected closed-loop response. Finally, simulations and bench experiments on an Aeropendulum system validate the approach, demonstrating the integrator’s e ectiveness in handling constant disturbances and the impact of selecting an appropriate initial condition.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Set-valued fractional-type optimization problems with κ-cone arcwise connectedness of higher-order]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2024-0021</link>
            <guid>https://sciendo.com/article/10.2478/candc-2024-0021</guid>
            <pubDate>Tue, 26 Aug 2025 00:00:00 GMT</pubDate>
            <description><![CDATA[

The aim of this study is to establish the sufficient higher-order KKT (Karush Kuhn Tucker) criteria of optimality for a set-valued fractional-type optimization problem (SFP) (FP). Under the presumptions of higher-order contingent epi-derivative and higher-order κ-arcwisely connectedness, these requirements are derived. We also look into the effects of these constraints on the higher-order duality of Mond-Weir (MWD), Wolfe (WD), and mixed (MD) kinds.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Artificial intelligence-based smart cloud computing schema model]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2024-0025</link>
            <guid>https://sciendo.com/article/10.2478/candc-2024-0025</guid>
            <pubDate>Tue, 26 Aug 2025 00:00:00 GMT</pubDate>
            <description><![CDATA[

In the contemporary digital era, cloud computing offers an ideal platform for artificial intelligence (AI) applications by providing the necessary computational power, memory, and scalability to handle the massive volumes of data required by intelligent algorithms. AI systems enable computing devices to make expert-level decisions by effectively leveraging information. However, challenges, related to adaptability, efficiency, privacy preservation, and the latent requirement for minimal user intervention remain critical. Notably, error detection efficiency can be improved by distributing data across multiple cloud storage services, akin to spreading data across physical disk drives. Nevertheless, continuously optimizing the performance and cost-efficiency of multiple cloud providers remains a complex task, due to varying pricing models and service quality levels. This paper aims to clarify how rule enforcement for distributed systems can be improved through the use of diverse cloud hosting services guided by authorization patterns. We propose an Effective AI Architecture for File Distribution Enhancement (EAIFDE), which aims to minimize costs and waiting times across various cloud platforms. The proposed architecture is validated using a cloud storage system simulator to evaluate the operational complexity and performance differences among multiple providers.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[An LMI based chaotic passivity analysis on memristive neural networks for memductance function]]></title>
            <link>https://sciendo.com/article/10.2478/candc-2024-0022</link>
            <guid>https://sciendo.com/article/10.2478/candc-2024-0022</guid>
            <pubDate>Tue, 26 Aug 2025 00:00:00 GMT</pubDate>
            <description><![CDATA[

This paper is devoted to passivity analysis for a class of chaotic memristive neural networks with distinct memductance approach, subject to actuator failures. Based on the existence of memristor, actuators and activation function, it was possible for the proposed model to stay in a stable state and reach the critical point by designing a strong reliable state-feedback controller. The qualitative analysis of this model can be developed using differential inclusion theory in the sense of Fillipov’s solution with suitable Lyapunov functional to acquire the results in terms of linear matrix inequalities (LMIs). Considering the known and unknown actuators cases, some sufficient conditions are derived for both state-dependent switched system and state-dependent continuous system based on passivity theory along with its chaotic phenomena. The reliable state-feedback controller is designed to guarantee that the considered closed loop system is internally stable by adopting the stabilizing control law. Finally, numerical examples are presented to demonstrate theoretical results via graphical illustrations.
]]></description>
            <category>ARTICLE</category>
        </item>
    </channel>
</rss>