Control Theory Research Articles

Measuring the Development Progress of Least Developed Countries: In the Context of World Development

ABSTRACT This article measures the ten-year development progress of 47 least developed countries (LDCs) based on comparing the development performances of 217 countries and economies. A methodology based on the theory of statistical process control is used to evaluate development performances in three dimensions: per capita income, life expectancy, and education. This methodology uses a pair of average and standard deviation charts to measure each LDC’s degree of growth and stability of growth in each dimension of development, and uses the three standard deviation limits in each chart to identify exceptional development performances that are outside the upper or lower limit. This article has three key findings on the exceptional development performances of LDCs in the context of world development over 2010–2019: (1) Seventeen LDCs achieved exceptionally high increases in life expectancy; (2) one LDC achieved exceptionally high growth in per capita income, but two LDCs had exceptionally low performances in the growth of per capita income; and (3) six LDCs experienced exceptionally unstable growth of per capita income. These findings shed some light on the future development priorities for LDCs. The reliability of analysis is also discussed.

Predefined-time control design for tracking chaotic trajectories around a contour with an UAV

The surveillance or monitoring of places is crucial to ensuring security, protecting people and assets, preventing crimes, and detecting emergencies, to mention some. Unmanned Aerial Vehicles (UAVs) play a vital role in these applications, offering versatility, agility, and aerial vision. A crucial step for such tasks is to protect the UAV path ahead. This paper focuses on a methodology harnessing the unpredictable nature of chaotic systems to generate trajectories around a closed area or contour. However, although a vast quantity of research papers mention the use of chaotic path generation, they have yet to learn about the control system and the dynamics affecting the UAV, where developing the control theory is challenging. In this paper, we design controllers based on predetermined-time stability, ensuring the achievement of the desired trajectory before a specified time. Additionally, adjusting control parameters is a crucial step during the control design, impacting the control performance. Hence, we present a method to optimize and adapt controller parameters through evolutionary optimization, demonstrating precision enhancement. We validate the proposed system’s performance and the controllers through numerical simulations, indicating that the UAV effectively and accurately follows some types of chaotic trajectories like a square contour, aiming at the feasibility of this methodology in real UAV surveillance applications.

Changing the functioning of the article in the history of the German language

The subject of the study is the historical changes in the syntactic structure of the German language; the object of the study is articles. The authors consider in detail the use of definite, indefinite and null articles based on the material of a fragment of the Gospel of Luke, which were published in various periods of the German language: the text of Tatiana, translated by Martin Luther, made in 1545; an adapted version of Luther's translation, published in 2017 and a lightweight electronic version created as a result of deviation from the canonical translation. Particular attention is paid to the frequency of use of articles, the reasons for the increase or decrease in their use, as well as other means of determination. It is established that modern electronic text shows a tendency to decrease nominal groups, which, accordingly, leads to a decrease in articles. The tendency to decrease the zero article is manifested by the early Upper German period; in modern German, there is a displacement of the definite article. The methodological basis is based on a systematic approach to the interpretation of the phenomena of linguistic ontology in diachrony. The research methods are comparative historical analysis, the method of distributive analysis, quantitative analysis, methods of N. Chomsky's theory of control and binding. The main conclusions of the proposed study are the revealed historical processes in the system of articles of the German language. It was found that at an early stage of language development, the zero article dominated in texts, which significantly narrowed the manifestation of determination. The main burden during this period fell on possessive nominal groups, which primarily included proper names. The definite article was used much less frequently. In nominal groups that are part of the prepositional group, the zero article was mainly used, which eventually led to the creation of stable prepositional constructions with a zero article. The largest number of all types of articles falls on Luther's translation adapted to the modern grammatical structure of the German language, which is associated with the maximum use of nominal groups. What is new is the establishment in modern German of a tendency to reduce the zero and definite articles.

Distributed Adaptive Forwarding Finite-Time Output Consensus of High-Order Multiagent Systems via Immersion and Invariance-Based Approximator.

A finite-time output consensus control problem is investigated in this article for an uncertain nonlinear high-order multiagent systems (MASs). For this class of MASs, the order of individual follower is reduced gradually by implementing the immersion and invariance (I&I) control theory repeatedly, and a requirement of solving partial differential equations (PDEs) in I&I control theory is obviated. Furthermore, an I&I-based radial basis function neural network (RBFNN) approximator is developed, where an extra cross term is added in the approximation mechanism, and the form of an update law for weights is transformed into a proportional and integral one. This I&I-based RBFNN approximator does not rely on a cancellation of the perturbation term, and these uncertainties are reconstructed by the I&I manifold adaptively, which is for improvement of approximation behaviors of traditional RBFNNs. On this basis, a distributed adaptive forwarding finite-time output consensus control strategy is proposed by combining a sign function, and the convergence time of the MAS can be adjusted with appropriate finite-time parameters. Finally, two illustrative examples verify the effectiveness of the theoretical claims.

Equivalent Input Disturbance-Based Control: Analysis, Development, and Applications.

This article reviews the concept, analysis, development, and applications of the equivalent-input-disturbance (EID) approach. First, the definition and existence of the EID are given, and the configuration of the EID estimator is provided. Next, estimation errors in the conventional EID approach are explained, and error-suppression methods are exhibited, which improve the disturbance-rejection performance. Then, this article describes how to apply the EID approach and associate challenges in dealing with nonlinearities, time delays, and uncertainties. Moreover, this article reviews some additional meaningful studies that cannot be categorized into the above-mentioned classes. Finally, some conclusions and future directions are given. The EID approach has been successfully used to suppress the influences of exogenous disturbances, nonlinearities, time delays, and uncertainties in many control systems to improve control performance and robustness. Studies can further rich the theory of the EID approach in the future, such as designing evaluation index, improving disturbance-rejection performance, developing new applications, and combining with other control theory.

A Mathematical Structural Modulation Method for Neutral-Point Potential Balancing of Three-Level Neutral-Point-Clamped Converters

To achieve the fast neutral-point potential balance of three-level neutral-point-clamped (3L-NPC) converters, a mathematical structural modulation method is presented in this paper, featuring fast balance capability, low computation, and easy implementation. In this method, the duty cycle is decomposed into the differential-mode component (DMC) and the common-mode component (CMC). The CMC is used to synthesize the AC output voltage while the DMC is adopted to control the neutral-point potential. Based on the fractional-order sliding-mode control theory, the DMC is constructed. As a result, neutral-point potential balancing in a pre-calculated time is achieved. Finally, the effectiveness of the proposed method is verified by experimental results.

Increased Ability to Perceive Relevant Sensory Information Minimizes Low Back Exposures in Lifting.

We have previously shown evidence that some individuals seem to consistently minimize low back loads when lifting, while others do not. However, it is unknown why. Individual differences in ability to perceive relevant sensory information may explain differences in minimization of low back loads during lifting, consistent with considering load reduction in one's movement objective in an optimal feedback control theory framework. The purpose of this study was to investigate whether individuals' ability to perceive proprioceptive information (both force- and posture-senses) at the low back was associated with peak low back loads when performing generic or occupation-specific lifts. Seventy-two participants were recruited to perform 10 barbell (generic) and backboard (occupation-specific) lifts, while whole-body kinematics and ground reaction forces were collected. Peak low back compression and anteroposterior shear forces normalized to body mass were calculated as dependent variables. Both posture matching ability and force matching ability at the heavier force targets were associated with lower means and variability of peak low-back loads in both lift types, albeit with small effect sizes (R2 ≤ .17). These findings support the utility of an optimal feedback control theory framework to explore factors explaining interindividual differences in low back loads during lifting. Further, this evidence suggests improving proprioceptive ability may be a useful strategy in lift training programs designed for workplace injury prevention.

Control method based on DRFNN sliding mode for multifunctional flexible multistate switch

To address the low accuracy and stability when applying classical control theory in distribution networks with distributed generation, a control method involving flexible multistate switches (FMSs) is proposed in this study. This approach is based on an improved double-loop recursive fuzzy neural network (DRFNN) sliding mode, which is intended to stably achieve multiterminal power interaction and adaptive arc suppression for single-phase ground faults. First, an improved DRFNN sliding mode control (SMC) method is proposed to overcome the chattering and transient overshoot inherent in the classical SMC and reduce the reliance on a precise mathematical model of the control system. To improve the robustness of the system, an adaptive parameter-adjustment strategy for the DRFNN is designed, where its dynamic mapping capabilities are leveraged to improve the transient compensation control. Additionally, a quasi-continuous second- order sliding mode controller with a calculus-driven sliding mode surface is developed to improve the current monitoring accuracy and enhance the system stability. The stability of the proposed method and the convergence of the network parameters are verified using the Lyapunov theorem. A simulation model of the three-port FMS with its control system is constructed in MATLAB/Simulink. The simulation result confirms the feasibility and effectiveness of the proposed control strategy based on a comparative analysis.

Preserving paradata for accountability of semi-autonomous AI agents in dynamic environments: An archival perspective

This paper proposes the category of real-time artificial intelligence (AI) systems as applications of computerized control systems in dynamic, time-constrained contexts normally managed by human intelligence. Noting the accountability challenges which these systems introduce, the paper posits the need for robust documentation and records capacities within these systems. The paper surveys four real-time AI systems with significant records needs: autonomous vehicles, online content targeting systems, mixed-reality tools for surgical contexts, and digital twin systems in airport facilities management. The paper identifies paradata, or the data leading up to an output in a system's operation, as a key data category necessitating preservation for full transparency in the records generated by these systems. Paradata is defined as “information about the procedure(s) and tools used to create and process information resources, along with information about the persons carrying out those procedures.” Paradata uncovers opaque technological processes underlying the production of other datasets and at a granular level must be identified and preserved to delineate the boundaries between human and system agency in semi-autonomous systems. With a basis in control theory, the paper finally offers a framework for assessing the functions of real-time AI systems' operations and their documentation and records needs.

Reducing State Conflicts between Network Motifs Synergistically Enhances Cancer Drug Effects and Overcomes Adaptive Resistance.

Inducing apoptosis in cancer cells is a primary goal in anti-cancer therapy, but curing cancer with a single drug is unattainable due to drug resistance. The complex molecular network in cancer cells causes heterogeneous responses to single-target drugs, thereby inducing an adaptive drug response. Here, we showed that targeted drug perturbations can trigger state conflicts between multi-stable motifs within a molecular regulatory network, resulting in heterogeneous drug responses. However, we revealed that properly regulating an interconnecting molecule between these motifs can synergistically minimize the heterogeneous responses and overcome drug resistance. We extracted the essential cellular response dynamics of the Boolean network driven by the target node perturbation and developed an algorithm to identify a synergistic combinatorial target that can reduce heterogeneous drug responses. We validated the proposed approach using exemplary network models and a gastric cancer model from a previous study by showing that the targets identified with our algorithm can better drive the networks to desired states than those with other control theories. Of note, our approach suggests a new synergistic pair of control targets that can increase cancer drug efficacy to overcome adaptive drug resistance.

Contributions to the Numerical Solutions of a Caputo Fractional Differential and Integro-Differential System

The primary goal of this research is to offer an efficient approach to solve a certain type of fractional integro-differential and differential systems. In the Caputo meaning, the fractional derivative is examined. This system is essential for many scientific disciplines, including physics, astrophysics, electrostatics, control theories, and the natural sciences. An effective approach solves the problem by reducing it to a pair of algebraically separated equations via a successful transformation. The proposed strategy uses first-order shifted Chebyshev polynomials and a projection method. Using the provided technique, the primary system is converted into a set of algebraic equations that can be solved effectively. Some theorems are proved and used to obtain the upper error bound for this method. Furthermore, various examples are provided to demonstrate the efficiency of the proposed algorithm when compared to existing approaches in the literature. Finally, the key conclusions are given.

Physics-Informed Neural Networks for Solving High-Index Differential-Algebraic Equation Systems Based on Radau Methods

As is well known, differential algebraic equations (DAEs), which are able to describe dynamic changes and underlying constraints, have been widely applied in engineering fields such as fluid dynamics, multi-body dynamics, mechanical systems, and control theory. In practical physical modeling within these domains, the systems often generate high-index DAEs. Classical implicit numerical methods typically result in varying order reduction of numerical accuracy when solving high-index systems. Recently, the physics-informed neural networks (PINNs) have gained attention for solving DAE systems. However, it faces challenges like the inability to directly solve high-index systems, lower predictive accuracy, and weaker generalization capabilities. In this paper, we propose a PINN computational framework, combined Radau IIA numerical method with an improved fully connected neural network structure, to directly solve high-index DAEs. Furthermore, we employ a domain decomposition strategy to enhance solution accuracy. We conduct numerical experiments with two classical high-index systems as illustrative examples, investigating how different orders and time-step sizes of the Radau IIA method affect the accuracy of neural network solutions. For different time-step sizes, the experimental results indicate that utilizing a 5th-order Radau IIA method in the PINN achieves a high level of system accuracy and stability. Specifically, the absolute errors for all differential variables remain as low as 10−6, and the absolute errors for algebraic variables are maintained at 10−5. Therefore, our method exhibits excellent computational accuracy and strong generalization capabilities, providing a feasible approach for the high-precision solution of larger-scale DAEs with higher indices or challenging high-dimensional partial differential algebraic equation systems.

Comparison the modern controllers’ efficiency for the spray dryer’s control system

Spray drying is one of the widely used drying process in chemical, pharmaceutical, and food industry. This process allows removing water from the product, which is produced in the solution form, and turn the finished product into a flowing powder material. It is obvious that such a process requires a significant amount of thermal energy, which is spent on the water evaporation. In addition, as in any drying process, in spray drying there is a problem with possible material over-drying. Therefore, the task of qualitative regulation of the spray drying process remains relevant. In this article, we attempt to compare the efficiency of two modern controllers for controlling the spray drying process. This is known in the theory of control of dynamic objects using predictive models model predictive controller and a fuzzy controller based on linguistic variables. The regulators choice due to both their popularity and high control quality indicators, which are given in published scientific works. The study was performed on based on spray drying process models, which were obtained in the previous work of the authors. In the research we used the software package MATLAB Simulink for control systems simulation. The research result show, that using the MPС-controller in the spray dryer’s control system allow to reduce the transient processes time. However, in case of a fuzzy controller, the control system partially levelling the mathematical model’s inaccuracy.

IMPROVING THE QUALITY OF REGULATION OF CONTROL SYSTEMS WITH UNCLEAR INITIAL INFORMATION

This article presents a study of the properties of an automatic control system with a fuzzy controller, which includes a fuzzy pseudo-linear correction device with phase advance and a PID controller.One of the promising and effective approaches of the modern theory of control of complex technological systems in conditions of uncertainty caused by the fuzziness of initial information is an approach based on the use of expert assessment methods and the theory of fuzzy sets [1,2]. Technological processes characterized by multi-criteria function mainly in a fuzzy environment. Therefore, for optimal control of the operating modes of such systems, it is necessary to take into account the vectors of criteria and the fuzziness of the initial information. One of the alternative methods of building control and regulation systems for objects that are indistinctly defined from the point of view of classical theory is the use of so-called fuzzy logic controllers.Therefore, the development of fuzzy control controllers based on existing microcontrollers is a very urgent task, since a pseudo-linear fuzzy controller built on the basis of fuzzy sets and fuzzy logical I/O, under the condition of uncertainty of the disturbing effect, is able to provide higher quality indicators of the transient process than a traditional PID controller.As pseudolinear correction devices (PKU) we use: Amplitude-suppressed, phase-ahead, and with separate channels for amplitude and phase, since one of the main disadvantages of the PID controller is the presence of a phase delay and high sensitivity to interference in the measuring channel.

Precision Landing of Unmanned Aerial Vehicle under Wind Disturbance Using Derivative Sliding Mode Nonlinear Disturbance Observer-Based Control Method

Unmanned aerial vehicles (UAVs) are extensively employed in civilian and military applications because of their excellent maneuverability. Achieving fully autonomous quadrotor flight and precision landing on a wireless charging station in the presence of wind disturbance has become a crucial research topic. This paper presents a composite control technique for UAV altitude and attitude tracking in harsh environments, i.e., wind disturbance. A composite controller was developed based on nonlinear disturbance observer (NDOB) control theory to allow the UAV to land in the presence of random external wind disturbances and ground effects. The NDOB estimated the unknown wind disturbance, and the estimation was fed into the derivative sliding mode nonlinear disturbance observer-based control (DSMNDOBC), allowing the UAV to perform autonomous precision landing. Two loop designs were applied: the inner loop for stabilization and the outer loop for altitude tracking. The quadrotor model dynamics and the proposed controller, DSMNDOBC, were simulated employing MATLAB/Simulink®, and the results were compared with the one obtained by the proportional derivative (PD) controller and the sliding mode controller (SMC). The simulation results indicated that the DSMNDOBC has superior altitude and attitude control compared to the PD and SMC controllers and better disturbance estimation and attenuation performance.

On observer compensator design for non-autonomous control semi-linear evolution equations

This paper investigates the Luenberger observer design problem for non-autonomous control semilinear evolution equations with disturbances in Banach spaces. Then, the practicalstabilization problem of the system is solved, yielding a compensator based on the Luenberger observer by using integral inequalities of the Gronwall type. Sufficient conditions of the controller and observer problem are satisfied, we show that the proposed controller with estimatedstate feedback from the proposed practical Luenberger observer will achieve global practical stabilization. We develop novel ideas and techniques, which present the further development of mathematical control theory. Furthermore, an example is given to show the applicability of our theoretical results.

Совершенствование транспортных технологических процессов железнодорожной сети

Purpose: To increase the efficiency of the railway transport network through the mutually beneficial interaction of lines of various categories. Methods: Methods of control theory, system analysis, synthesis and mathematical modeling were used. Results: The tools for determining the optimal location of logistics centers using the center of gravity method have been improved by taking into account the criteria for minimizing transport costs, category, specialization and social significance of the line, as well as determining the weight values of the criteria using the hierarchy analysis method. Practical significance: The proposed tools allow you to determine the location of the logistics center at the intersection of intensive and lowintensity railway lines, taking into account the criteria for minimizing transport costs, the specialization of the line and the social significance of the lines for the regions under consideration.

The Role of Family Structure in Juvenile Delinquency

Purpose: The general purpose of the study was to explore the role of family structure and juvenile diplomacy. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to family structure in juvenile diplomacy. Preliminary empirical review revealed that that family structure significantly influences adolescent engagement in delinquent behaviors, with adolescents from single-parent households or non-traditional family arrangements exhibiting higher rates of delinquency. Research highlighted the mediating role of factors such as parental supervision and positive peer relationships, along with cultural variations in this relationship. Qualitative insights underscored the unique challenges faced by adolescents from non-traditional family structures, emphasizing the importance of tailored support. Overall, the findings underscored the need for context-specific interventions and policies to address juvenile delinquency, informed by a comprehensive understanding of family dynamics and socio-cultural factors. Unique Contribution to Theory, Practice and Policy: Social Learning theory, Strain theory and Social Control theory may be used to anchor future studies on the role of family structure in juvenile delinquency. The study provided recommendations with significant contributions to theory, practice, and policy. It advanced theoretical understanding by highlighting the complex interplay between family dynamics and delinquent behavior, contributing empirical support for existing theoretical models. The study recommended the development of family-centered interventions to strengthen parental supervision and support networks, promote positive peer relationships, and address systemic factors contributing to family stressors. Policy implications included initiatives to support family well-being, invest in evidence-based programs, and integrate findings into broader social policies. Collaboration across sectors was emphasized to implement comprehensive approaches, and the need for continued research to deepen understanding and inform targeted interventions was underscored.

On the application of the calculus of positively constructed formulas for the study of controlled discrete-event systems

The article is devoted to the development of an approach to solving the main problems of the theory of supervisory control of logical discrete-event systems (DES), based on their representation in the form of positively constructed formulas (PCF). We consider logical DESs in automata form, understood as generators of some regular languages. The PCF language is a complete first-order language, the formulas of which have a regular structure of alternating type quantifiers and do not contain a negation operator in the syntax. It was previously proven that any formula of the classical first-order predicate calculus can be represented as a PCF. PCFs have a visual tree representation and a natural question-and-answer procedure for searching for an inference using a single inference rule. It is shown how the PCF calculus, developed in the 1990s to solve some problems of control of dynamic systems, makes it possible to solve basic problems of the theory of supervisory control, such as checking the criteria for the existence of supervisory control, automatically modifying restrictions on the behavior of the controlled system, and implementing a supervisor. Due to some features of the PCF calculus, it is possible to use a non-monotonic inference. It is demonstrated how the presented PCF-based method allows for additional event processing during inference. The Bootfrost software system, or the so-called prover, designed to refute the obtained PCFs is also presented, and the features of its implementation are briefly described. As an illustrative example, we consider the problem of controlling an autonomous mobile robot.

Functional brain network controllability dysfunction in Alzheimer’s disease and its relationship with cognition and gene expression profiling

Objective. In recent studies, network control theory has been applied to clarify transitions between brain states, emphasizing the significance of assessing the controllability of brain networks in facilitating transitions from one state to another. Despite these advancements, the potential alterations in functional network controllability associated with Alzheimer’s disease (AD), along with the underlying genetic mechanisms responsible for these alterations, remain unclear. Approach. We conducted a comparative analysis of functional network controllability measures between patients with AD (n = 64) and matched normal controls (NCs, n = 64). We investigated the association between altered controllability measures and cognitive function in AD. Additionally, we conducted correlation analyses in conjunction with the Allen Human Brain Atlas to identify genes whose expression was correlated with changes in functional network controllability in AD, followed by a set of analyses on the functional features of the identified genes. Main results. In comparison to NCs, patients with AD exhibited a reduction in average controllability, predominantly within the default mode network (DMN) (63% of parcellations), and an increase in average controllability within the limbic (LIM) network (33% of parcellations). Conversely, AD patients displayed a decrease in modal controllability within the LIM network (27% of parcellations) and an increase in modal controllability within the DMN (80% of parcellations). In AD patients, a significant positive correlation was found between the average controllability of the salience network and the mini-mental state examination scores. The changes in controllability measures exhibited spatial correlation with transcriptome profiles. The significant genes identified exhibited enrichment in neurobiologically relevant pathways and demonstrated preferential expression in various tissues, cell types, and developmental periods. Significance. Our findings have the potential to offer new insights into the genetic mechanisms underlying alterations in the controllability of functional networks in AD. Additionally, these results offered perspectives for a deeper understanding of the pathogenesis and the development of therapeutic strategies for AD.