Control Theory Research Articles

Exposure to Food Temptations Reduces Subsequent Consumption Through Goal Activation

ABSTRACTThe enduring availability of high‐caloric, tempting food in consumer environments has been identified as a major cause driving the obesity epidemic. The severity of the problem tends to hide the important fact that many consumers often resist food temptations. This article aims at designing consumption reduction strategies that build on the spontaneous capacity of consumers to resist food temptations. Across a series of three experiments, of which two laboratory studies and one field study, we find that physical exposure to food temptations reduces subsequent free consumption of similar foods. Building on cognitive control theory, we extend this finding and identify boundary conditions. We show that the reduction of consumption works in challenging populations (e.g., men and children) with pictures of food temptations and that it survives a delay. We also show that the effect is suppressed with explicit prohibition during pre‐exposure and with combined exposure (i.e., the combination of physical and picture temptations) in children. The findings are discussed concerning their potential as a social marketing tool.

Functional Evaluation and Neuro-Muscle-Skeletal Recovery in Lyme Disease: A Study on Motor Control Theories and Rehabilitation

Background: Patients with Lyme disease often suffer from neuro-motor sequelae following Borrelia infection, leading to impairments in balance, posture, and spinal musculoskeletal functionality. This study aims to evaluate the effectiveness of physical therapy in addressing these impairments. Methods: Initial assessments were conducted using the GPS Posture-test System to evaluate static bipodal balance and posture, and BACKFIX Technology for unipedal balance and spinal functionality. Disturbances in balance, postural asymmetries, and a forward-shifted center of gravity were observed. The patient then underwent two 10 sessions (each during two weeks) of computer-assisted physical therapy focused on neuro-muscular movement and balance control. Results: Post-therapy evaluations showed significant improvements in both bipodal and unipedal static balance, segmental and overall body posture, and spinal musculoskeletal functionality. The therapy effectively addressed the postural asymmetries and shifted the center of gravity closer to the optimal vertical posture point. Conclusion: Computer-assisted physical therapy with visual feedback significantly improved posture, balance, and spinal functionality in patients with Lyme disease-related neuro-motor sequelae. This approach facilitated the restoration of muscle coordination and enhanced motor control, aligning with findings from the existing literature.

A separation principle for the prescribed-time stabilization of a class of nonlinear systems

Despite the recent development of prescribed-time control theory, the highly desirable separation principle remains unavailable for nonlinear systems with only the output being measurable. In this paper, for the first time we establish such separation principle for a class of nonlinear systems, such that the prescribed-time observer and prescribed-time controller can be designed independently, and the parameter designs do not affect each other. Our method makes use of two parametric Lyapunov equations (PLEs) to generate two symmetric positive-definite matrices, aiming to avoid conservative treatments of nonlinear functions commonly associated with high-gain methods during the design process. Our work provides a stronger version of the matrix pencil formulation that is applicable when nonlinearities satisfy the so-called linear growth condition, even if the growth rate is unknown. In our method the selection of design parameters is straightforward as it involves only three parameters: one for the prescribed convergence time tf, and the other two are for the controller and the observer respectively, and the choice of the latter two parameters does not affect each other. Once the system order is determined, one can directly obtain reasonable ranges for these two parameters. Numerical simulations verify the effectiveness of the proposed method.

Diagnostic-constrained fault-tolerant control of bi-directional AGV transport systems with fault-prone sensors.

This paper explores a novel challenge regarding bidirectional Automated Guided Vehicles (AGVs): supervisory control amidst potential sensor faults. The proposed approach uses an event-based control architecture, guided by Supervisory Control Theory (SCT), to achieve non-blocking routing of AGVs. Unlike most routing approaches assuming full event observability, this paper investigates scenarios where events might become unobservable due to sensor faults or disturbances, which may affect the supervisor efficiency. The paper addresses two new key issues regarding AGV systems. First, it examines the diagnosis problem of automated transport systems from a discrete-event systems perspective. Secondly, it presents a control architecture enhanced with a diagnostic layer to improve fault tolerance. The theory of automata and languages is used to address control and diagnostic issues. The proposed methodology offers a systematic approach to design specification and diagnostic automata for routes shared by AGVs. The new specification automata integrate information from the diagnostic automata via synchronized transition guards, guaranteeing the synthesis of a robust supervisor that avoids deadlocks even when observability is compromised. The efficiency of the proposed architecture is examined and showcased by simulation. In addition, a modelling framework based on stochastic timed automata is introduced, applying model checking to assess system reliability which is redefined as the probability of deadlock avoidance.

Attitude Control and Trajectory Tracking of a Shipboard Quadrotor Unmanned Aerial Vehicle Based on Self-Improvement Algorithm

With the rapid development of Industry 4.0 and urbanisation around the world, computer control technology and electromechanical systems, the development of UAVs around the world has entered a period of rapid development. As UAVs enter the rapid development stage, higher requirements are placed on the precise control rate during UAV missions. Multi-rotor UAVs have been used on ships in large scale due to their easy-to-manoeuvre characteristics. However, shipboard UAVs are affected by many factors during flight, such as crosswinds, gusts and wave effects at sea. Therefore, it is necessary to optimise the robustness and safety of the control process of shipborne UAVs through self-immunity algorithms. In the research process of UAV, modern control theory has developed very rapidly, but it is difficult to be applied in practice, and there is a certain gap. The reason for this is that advanced control theory techniques require an accurate mathematical model of the controlled object.In this study, the position and attitude equations of motion of the quadrotor UAV are firstly obtained according to the rigid body mechanics, the UAV dynamics model is deduced, and for the UAV attitude control and trajectory tracking requirements, the inner loop adopts the self-immobilisation control and the outer loop adopts the PID control, and the relevant control structure is designed. Through relevant Simulink simulation and comparison with the effect of traditional PID control, the optimisation effect of the self-immunity algorithm on the control of shipborne quadrotor UAV is verified.

Fixed point results in C*-algebra valued fuzzy metric space with applications to boundary value problem and control theory

In this paper, we derive some new fixed point results in C∗-algebra valued fuzzy metric space with the help of subadditive altering distance function with respect to a t-norm. Our results generalize some existing fixed point results in the literature. A common fixed point result is also derived for a pair of mappings on complete C∗-algebra valued fuzzy metric space. The results are supported by suitable examples along with the graphical demonstration of the used conditions. As application, we establish the solvability of a second order boundary value problem. Moreover, the results are also applied in control theory to study the possibility of optimally controlling the solution of an ordinary differential equation in dynamic programming.

A Study of Two-Wheel Self-Balancing Robot Dynamics for Control Systems Learning

The two-wheeled self-balancing robot (TWSBR), utilizing an inverted pendulum arrangement, exhibits dynamic stability while being statically unstable. Due to this behavior, the TWSBR system has been used to demonstrate fundamental principles of stability, nonlinear dynamics, and control theory. In this work, a computational study of the TWSBR was carried out to develop a mathematical model using Newtonian mechanics that serves as an educational tool in control education for demonstrating the impact of different control algorithms such as Proportional (P), ProportionalDerivative (PD), and Proportional-Integral-Derivative (PID) on system stability. The comparison of P, PD, and PID controllers was undertaken to demonstrate the practical advantages of each controller in improving the robot's stability and responsiveness. The PID controller was designed and optimized using the Root-locus techniques, with a damping ratio equal to 1 which exhibits a fast-settling time with minimum overshoots. When an impulse response was applied to the PID controller in the simulation environment it demonstrated system can reach a dynamic balance within 1.2 seconds demonstrating the effectiveness of the proposed PID controller. The optimal gain parameters K value and Kp, Ki, and Kd parameter values were determined using root-locus analysis, which allowed the system to operate with great stability and minimum settling time. These findings highlight the TWSBR's educational significance as a hands-on tool for teaching practical applications of control theory, as well as providing valuable insights into the design and optimization of control systems for dynamic balance tasks. This study emphasizes the relevance of control algorithms in achieving rapid and stable dynamic behaviors, making it a valuable learning resource for both students and instructors. Keywords: Two-wheeled self-balancing robot, Inverted pendulum, P, PD and PID controllers, Stability analysis, Control Education

Басқарудың цифрлық жүйелерін модельдеу

In this work, a mathematical model of digital control of multi-circuit objects is constructed, taking into account the features of the digital controller as an element of the control system. The article shows the theoretical foundations and methods of designing modern digital control systems based on traditional algorithms and new developments in the field of synthesis of continuous and discrete control systems. An example of mathematical modeling of a twocircuit system with digital control is given. To solve the problem, a standard block diagram of complex multi-loop control systems with von Neumann-type digital controllers was developed, taking into account the random nature of the processed data and real-time delays between transactions. The scientific novelty is the flexibility of the software, which significantly expands the capabilities of implementing complex algorithms, which creates the prerequisites for the practical application of the latest methods of modern control theory.

Workshop Development for New Frontier of Mechatronics for Mobility, Energy, and Production Engineering

Mechatronics is a multidisciplinary engineering field that integrates mechanics, electronics, control theory, computer science, communications, power, and production manufacturing, reflecting the trend toward deep cross-disciplinary collaboration. With applications in e-mobility, connected and autonomous vehicles (CAV), robotics, and unmanned aerial vehicles (UAV), the expanding mechatronics industry demands a workforce with broad, multidisciplinary training. This paper details the activities and outcomes of an NSF-funded ECR: PEER (EHR Core Research: Production Engineering Education and Research) project, which organized two workshops at Wayne State University (WSU) and California State University Long Beach (CSULB). These workshops aimed to gather insights from experts across academia, industry, and non-profit sectors to shape the future of mechatronics education and production. Over two days, professionals addressed challenges in workforce development for production engineering in mechatronics, covering educational pathways, advancements in teaching methods, and the social impacts of mechatronics technology. The workshops had distinct focuses: WSU concentrated on ground mobility technologies, while CSULB emphasized aerospace applications. A survey conducted at the end of the workshops evaluated their effectiveness, with the results informing future improvements in mechatronics workforce education.

PROPERTIES OF SOLUTIONS IN THE DUBINS CAR CONTROL PROBLEM

TThis paper addresses the time-optimal control problem of the Dubins car, which is closely related to the problem of constructing the shortest curve with bounded curvature between two points in a plane. This connection allows researchers to apply both geometric methods and control theory techniques during their investigations. It is established that the time-optimal control for the Dubins car is a piecewise constant function with no more than two switchings. This characteristic enables the categorization of all such controls into several types, facilitating the examination of the solutions to the control problem for each type individually. The paper derives explicit formulas for determining the switching times of the control signal. In each case, necessary and sufficient conditions for the existence of solutions are obtained. For certain control types, the uniqueness of optimal solutions is established. Additionally, the dependence of the movement time on the initial and terminal conditions is studied.

A Trustworthy and Responsible Decision-Making Framework for Resource Management in Food–Energy–Water Nexus: A Control-Theoretical Approach

This article introduces a hybrid framework for trustworthy and responsible natural resource management, aimed at building bottom-up trust to enhance cooperation among decision-makers in the food, energy, and water sectors. Cooperation is highly critical for the adoption and application of resource management alternatives (solutions), including those generated by AI-based recommender systems, in communities due to significant impact of these sectors on the environment and the economic productivity of affected communities. While algorithms can recommend solutions, effectively communicating and gaining community acceptance of these solutions is crucial. Our research stands out by emphasizing the collaboration between humans and machines, which is essential for addressing broader challenges related to climate change and the need for expert tradeoff handling in the management of natural resources. To support future decision-making, we propose a successful control-theory model based on previous decision-making and actor behavior. We utilize control theory to depict how community decisions can be affected by how much individuals trust and accept proposed solutions on irrigation water rights and crop operations in an iterative and interactive decision support environment. This model interacts with stakeholders to collect their feedback on the acceptability of solutions, while also examining the influence of consensus levels, trust sensitivities, and the number of decision-making rounds on the acceptance of proposed solutions. Furthermore, we investigate a system of multiple decision-making and explore the impact of learning actors who adjust their trust sensitivities based on solution acceptance and the number of decision-making rounds. Additionally, our approach can be employed to evaluate and refine potential policy modifications. Although we assess potential outcomes using hypothetical actions by individuals, it is essential to emphasize our primary objective of developing a tool that accurately captures real human behavior and fosters improved collaboration in community decision-making. Ultimately, our aim is to enhance the harmony between AI-based recommender systems and human values, promoting a deeper understanding and integration between the two.

Method of horizontal pod scaling in Kubernetes to omit overregulation

This paper describes the method of omitting the over-regulation effect that occurs under certain conditions by horizontal pod autoscaling microservices in the container application orchestration system Kubernetes. The effect was initially observed only for long-term HTTP WebSocket sessions, where it led to excessive use of computing resources, which reduced the efficiency of IT infrastructure management, and caused service failure. It was found that the overregulation effect is reproduced not only for connections with long-term HTTP sessions, such as HTTP WebSocket, but also for shorter-term REST HTTP sessions in case of increased delay in the metric collection cycle used for horizontal pod autoscaling. It is assumed that this effect happens due to the approach of implementing horizontal scaling controllers similar to the principles of proportional regulators in systems with negative feedback from the theory of automation and control. It is proposed to extend one of the methods used for optimizing the proportional controller to the problem consisting of reducing the time delay between scaling metrics collecting and upscale applied by the controller in Kubernetes. The applied method demonstrated its effectiveness, therefore, within the same methodology, an experiment was conducted on using the proportional-integral-differential controller for automatic horizontal scaling of pods. The results obtained showed why the proportional-integral-differential controller is not widespread among the overviewed Kubernetes solutions for horizontal automatic scaling. An assumption was made about the limitations of studying the downscaling process in Kubernetes due to the need to consider the quality of service when stopping pods and the need to collect indicator metrics using quality-of-service object management tools such as ISTIO.

Solving the problem of technological object identification

Abstract. Problem. The identification task is formulated as follows: based on the results of observations of the input and output variables of a system, a model, i.e. a formalized representation of this system, should be built that is optimal in some sense. This shows the connection between the identification task and the above general scheme of establishing regularities based on the results of observations. The identification task is based on modern control theory. To solve it, modern computers are used. The latter, with their high speed and virtually unlimited memory capacity, create the prerequisites for receiving, transmitting and processing large arrays of observations that are necessary for building adequate models of real objects. Goal. Studying the possibilities of solving the problem of identifying technological objects using MATLAB+Simulink. Job tasks: to consider the tasks of identifying technological objects of management; to choose an identification method; identify the electric motor model. Methodology. The research problems should be solved by methods of analysis and synthesis of automatic control systems by the method of least squares. Results. In the work, experimental data on the dependence of the engine idling speed on the excitation current were obtained and solved the identification problem using the MATLAB+SIMULINK package. Originality. Based on the analysis of existing solutions, it was found that the works did not consider the calculation of absolute and relative errors of the results of identifying the object and the model developed using the least squares method. Thus, there is a need to develop a system for identifying a model of a technological object made by the least squares method. To solve the identification problem, an electric motor is chosen as the object of study. Practical value. The paper examines the methods used to identify complex objects. The comparative analysis showed that the most acceptable method is the least squares method, due to simple mathematical calculations in comparison with the other methods. To illustrate the application of this method, two tasks were solved: identification of the model of an electric motor; identification of the model of an alarm system. The studies have shown that model identification can be performed using the MATLAB+Simulink package. The research results obtained on the model differ from the experimental data by no more than 4 %

The Relationship Between Anxiety and Processing Speed of Chinese Children with and Without Dyslexia – from the Perspective of Attentional Control Theory

The Relationship Between Anxiety and Processing Speed of Chinese Children with and Without Dyslexia – from the Perspective of Attentional Control Theory

Even-Order Delay Differential Equations with <i>p</i>-Laplacian Like Operator: New Oscillation Criteria

As a pivotal branch within the realm of differential equations, the theory of oscillation holds a crucial position in the exploration of natural sciences and the construction of modern control theory Frameworks. In this manuscript, we obtain some oscillatory properties for the noncanonical even-order differential equation with a p-Laplacian-like operator by using comparison with first-order differential inequality. Some examples are discussed to illustrate the effectiveness of our main results.

A functional approach to homeostatic regulation

In this work, we present a novel modeling framework for understanding the dynamics of homeostatic regulation. Inspired by engineering control theory, this framework incorporates unique features of biological systems. First, biological variables often play physiological roles, and taking this functional context into consideration is essential to fully understand the goals and constraints of homeostatic regulation. Second, biological signals are not abstract variables, but rather material molecules that may undergo complex turnover processes of synthesis and degradation. We suggest that the particular nature of biological signals may condition the type of information they can convey, and their potential role in shaping the dynamics and the ultimate purpose of homeostatic systems. We show that the dynamic interplay between regulated variables and control signals is a key determinant of biological homeostasis, challenging the necessity and the convenience of strictly extrapolating concepts from engineering control theory in modeling the dynamics of homeostatic systems. This work provides a simple, unified framework for studying biological regulation and identifies general principles that transcend molecular details of particular homeostatic mechanisms. We show how this approach can be naturally applied to apparently different regulatory systems, contributing to a deeper understanding of homeostasis as a fundamental process in living systems.

Linear Matrix Inequality-Based Design of Structured Sparse Feedback Controllers for Sensor and Actuator Networks

A sensor and actuator network (SAN) is a control system where many sensors and actuators are connected through a communication network. In a SAN with redundant sensors and actuators, it is important to consider choosing sensors and actuators used in control design. Depending on applications, it is also important to consider not only the choice of sensors/actuators but also that of communication channels in which some sensors/actuators are connected. In this paper, based on a linear matrix inequality (LMI) technique, we propose a design method for structured sparse feedback controllers. An LMI technique is one of the fundamental tools in systems and control theory. First, the sparse reconstruction problems for vectors and matrices are summarized. Next, two design problems are formulated, and an LMI-based solution method is proposed. Finally, two numerical examples are presented to show the effectiveness of the proposed method.

Shape Optimization and Experimental Investigation of Glue-Laminated Timber Beams.

This study investigated the optimal shape of glue-laminated timber beams using an analytical model of a slender beam, taking into account the anisotropy of its strength properties as well as boundary conditions at the oblique bottom face of the beam. A control theory problem was formulated in order to optimize the shape of the modeled beam. Two optimization tasks were considered: minimizing material usage (Vmin) for a fixed load-carrying capacity (LCC) of the beam and maximizing load-bearing capacity (Qmax) for a given volume of the beam. The optimal solution was found using Pontryagin's maximum principle (PMP). Optimal shapes were determined using Dircol v. 2.1 software and then adjusted according to a 3D finite element analysis (FEA) performed in Abaqus. The final shapes obtained through this procedure were used in the CNC-based production of three types of nine beams: three reference rectangular beams, three Vmin beams, and three Qmax beams. All specimens were subjected to a four-point bending test. The experimental results were contrasted with theoretical assumptions. Optimization reduced material usage by ca. 12.9% while preserving approximately the same LCC. The maximization of LCC was found to be rather unsuccessful due to the significant dependence of the beams' response on the highly variable mechanical properties of GLT.

Velocity Observer Design for Tether Deployment in Hamiltonian Framework

This paper presents a nonlinear velocity observer of tether deployment using the immersion and invariance technique, and the velocity observer design problem is recast as a problem of designing an attractive and invariant manifold inside the Hamiltonian framework. The passivity-based control theory is used to define an expected Hamiltonian function, and the stability of the designed velocity observer is addressed by using the passivity-based methodology. Finally, a simple tension control law with measurable and unmeasurable states is employed for controlling the tether deployment, where the unmeasurable states use the proposed velocity observer. Numerical simulations demonstrate that the proposed velocity observer is working successfully. Sensitivity analyses are conducted to test the effectiveness and robustness of the proposed velocity observer.

The optimal control theory — a scientific approach to fundamentally solving control problems

The optimal control theory — a scientific approach to fundamentally solving control problems