Control Theory Research Articles

Mathematical Modeling of Infectious Disease and Prey-Predator Interaction with Optimal Control

In this paper, the impact of viral illnesses on the predator-prey relationship with an optimal control analysis is studied. An ecoepidemiological model of four compartments, namely, susceptible prey, susceptible predator, infected prey, and infected predator populations, in the interaction of the prey-predator system is formulated. The fundamental tenet of our ecoepidemiology model is that sick predators do not engage in predation. It is confirmed that the system’s solution exists, is positive, and is bounded. The system’s equilibrium points are determined and computed. Lyapunov functions and a linearizing form are used for local and global stability analysis, respectively. The next generation matrix approach is used to calculate the threshold value for diseased predators and prey at the disease-free equilibrium point. Optimal treatment options for vulnerable and infected populations are established by applying optimal control theory to the ecoepidemiology model of a prey-predator system. MATLAB software is utilized to obtain numerical simulations that validate the analytical outcomes. The optimal control problem simulations demonstrate that the number of infected populations in a given prey-predator system can be decreased by implementing control measures.

Control of iron(II)-tris(2,2'-bipyridine) light-induced excited-state trapping via external electromagnetic fields.

Light-induced excited spin-state trapping reactions in iron pyridinic complexes allow the iron's low-to-high spin transition in a sub-picosecond timescale. Employing a recently developed model for [Fe(2,2'-bipyridine)3]2+photochemical spin-crossover reaction in conjunction with quantum wavepacket dynamics, we explore the possibility of controlling the reaction through external electromagnetic fields, aiming at stabilizing the initial metal-to-ligand charge transfer states. We show that simple Gaussian-shaped electromagnetic fields have a minor effect on the population kinetics. However, introducing vibrationally excited initial wavepacket representations allow to maintain trapped the population into the metal-to-ligand charge transfer states. Using optimal control theory, we propose an electromagnetic field shape that increases the lifetime of metal-to-ligand charge transfer states. These results open the route for controlling the iron photochemistry through the action of external electric fields.

Acoustofluidic manipulation for submicron to nanoparticles.

Particles, ranging from submicron to nanometer scale, can be broadly categorized into biological and non-biological types. Submicron-to-nanoscale bioparticles include various bacteria, viruses, liposomes, and exosomes. Non-biological particles cover various inorganic, metallic, and carbon-based particles. The effective manipulation of these submicron to nanoparticles, including their separation, sorting, enrichment, assembly, trapping, and transport, is a fundamental requirement for different applications. Acoustofluidics, owing to their distinct advantages, have emerged as a potent tool for nanoparticle manipulation over the past decade. Although recent literature reviews have encapsulated the evolution of acoustofluidic technology, there is a paucity of reports specifically addressing the acoustical manipulation of submicron to nanoparticles. This article endeavors to provide a comprehensive study of this topic, delving into the principles, apparatus, and merits of acoustofluidic manipulation of submicron to nanoparticles, and discussing the state-of-the-art developments in this technology. The discourse commences with an introduction to the fundamental theory of acoustofluidic control and the forces involved in nanoparticle manipulation. Subsequently, the working mechanism of acoustofluidic manipulation of submicron to nanoparticles is dissected into two parts, dominated by the acoustic wave field and the acoustic streaming field. A critical analysis of the advantages and limitations of different acoustofluidic platforms in nanoparticles control is presented. The article concludes with a summary of the challenges acoustofluidics face in the realm of nanoparticle manipulation and analysis, and a forecast of future development prospects.

Editorial Conclusion for the Special Issue “New Theory and Applications of Nonlinear Analysis, Fractional Calculus and Optimization”

Nonlinear analysis has widespread and significant applications in many areas at the core of many branches of pure and applied mathematics and modern science, including nonlinear ordinary and partial differential equations, critical point theory, functional analysis, fixed point theory, nonlinear optimization, fractional calculus, variational analysis, convex analysis, dynamical system theory, mathematical economics, data mining, signal processing, control theory, and many more [...]

Research on Stability Control of Distributed Drive Vehicle with Four-Wheel Steering

The four-wheel steering distributed drive vehicle is a novel type of vehicle with independent control over the four-wheel angle and wheel torque. A method for jointly controlling the distribution of the wheel angle and torque is proposed based on this characteristic. Firstly, the two-degrees-of-freedom model and ideal reference model of four-wheel steering vehicle are established; then, the four-wheel steering controller and torque distribution controller are designed. The rear wheel angle is controlled by the feedforward controller and the feedback controller. The feedforward controller takes the side slip angle of the center of mass as the control target, and the feedback controller takes the yaw angle as the control target. Torque is controlled by two control layers, the additional yaw moment of the upper layer is calculated by the vehicle motion state and fuzzy control theory, and the lower layer distributes wheel torque through the road adhesion coefficient and wheel load. Finally, a simulation platform is established to verify the effectiveness of the proposed control algorithm.

Anti-swing control of varying rope length tower crane based on adaptive neural network sliding mode

Due to its complex nonlinear, underdriven, and strongly coupled characteristics, the tower crane will cause the load to swing violently when working, which will cause the tower crane to tip over in serious cases, and there exists a huge safety hazard. In this article, a new artificial neural network sliding mode control method is designed for the control problems of tower crane lifting in position and load swing prevention, which has strong robustness to disturbances and unmodeled dynamics, and ensures that the tower crane lifting is accurately tracked in position and suppresses the load swing at the same time. First, a nonlinear dynamics model of a five-degree-of-freedom tower crane considering the actual working conditions is established. Aiming at the problem that it is difficult to effectively control the nonlinear model of the tower crane system, a new neural sliding mode controller and compensation controller are designed based on the sliding mode control theory and using radial basis function neural network. The neural sliding mode controller is used to approximate the sliding mode equivalent controller with uncertainty and strong nonlinearity, and the compensation controller realizes the compensation of the neural sliding mode controller for the difference between the system control inputs and the uncertainty of the system. The convergence and stability of the proposed control system is rigorously demonstrated using the Lyapunov stability theory. Simulation studies have been carried out to verify the correctness of the model established in this article, as well as the excellent control performance of the control system and the ability to deal with system uncertainty, proving its strong robustness.

The Mediating Effect of Optimism and Resourcefulness on the Relationship between Hardiness and Cyber Delinquent Among Adolescent Students

Abstract There is growing interested in the role of psychological resilience in shaping adolescent students’ delinquent behaviours in secondary schools. In this article, using locus of control theory, we examine the role of psychological resilience in terms of hardiness, resourcefulness, and optimism in minimizing the occurrence of cyber delinquent behaviours of secondary school students in Saudi Arabia. We introduce optimism and resourcefulness as protective factors critical for adolescents engaging in cyber delinquent behaviour and conceptualize them as characteristics of individuals with an internal locus of control. This study examines the multiple mediation effects of optimism and resourcefulness in the relationship between hardiness and cyber delinquent behaviours. The structural equation modelling results confirm eight hypotheses and that optimism and resourcefulness mediate the relationship between hardiness and cyber delinquent behaviours. These findings demonstrate the multiple mediation effects of optimism and resourcefulness and the important role of hardiness, resourcefulness, and optimism in predicting cyber delinquent behaviour. We draw implications for theory, secondary schools, and policymakers.

Research on the application of automatic control theory in automatic driving technology

Autonomous driving technology has been widely used and studied in depth in recent years, and has become a hot field in the automotive industry. As one of the core methods to realize automatic driving, automatic control theory provides an important theoretical basis and practical guidance for automatic driving systems. This paper aims to explore the application of automatic control theory to autonomous driving and analyze its potential impact on improving driving safety, comfort and efficiency. This paper first introduces the basic principles and components of the autonomous driving system. Among them, automatic control theory plays an important role in decision-making and execution. Secondly, this paper discusses the specific application of automatic control theory in automatic driving. These include PID controller-based vehicle stability control, model predictive control (MPC) for path planning and trajectory tracking. These applications enable autonomous driving systems to respond in real time to environmental changes and maintain vehicle stability and safety. Finally, the paper discusses the challenges and future directions of automatic control theory in autonomous driving. Future research should focus on further improving the robustness and adaptability of automatic control algorithms to cope with complex driving scenarios and uncertainties. To sum up, automatic control theory plays an important role in automatic driving and has broad application prospects. Through continuous improvement and innovation, automatic control theory will make an important contribution to the realization of safer, more efficient, and more intelligent autonomous driving technology.

Position control of a digital electrohydraulic system with limited sensory data using double deep q-network controller

In this study, we propose a reinforcement learning controller for position control of a digital electrohydraulic system with independent metering valve structure. Our study addresses the inherent challenges of traditional control methods, such as the need for continuous pressure measurement and complex control structures. As a solution, a double deep q-network (DDQN) controller which relies solely on position feedback from D-EHS is suggested. A simulation environment was developed to train the DDQN controller, wherein the artificial neural network structure, training hyperparameters, and reward function were optimized through preliminary studies. The DDQN controller was trained using a sinusoidal position reference signal of a single frequency and evaluated experimentally for its position control performance. Tracking performance was assessed against various position reference signals, including ramp and sinusoidal signal with different frequencies. Robustness against external disturbances was also tested by increasing the nominal supply pressure by 20% and 40%. The results demonstrate that the DDQN controller successfully controls the digital electrohydraulic system and is achieving good tracking performance for the training position reference signal. Furthermore, the controller exhibits satisfactory tracking performance for non-training position reference signals, highlighting its ability to learn the digital electrohydraulic system dynamics with limited training data. Even under varying supply pressure conditions, the DDQN controller maintains an acceptable level of control performance. Our findings emphasize the generalizability and reliability of the DDQN reinforcement learning controller, making it a suitable candidate for application in digital electrohydraulic systems and contributing to the advancement of control theory research.

High-order moment interval stability/stabilization and observability of stochastic impulsive T–S fuzzy systems

This article is concerned with the problems of high-order moment interval stability/stabilization and observability of stochastic impulsive T–S fuzzy systems (SITSFSs). Firstly, the definition of high-order moment interval stability is proposed of SITSFSs in this paper. In consideration of the connection between pole placement and performance of systems, sufficient conditions for the distribution of the eigenvalues of systems within a defined interval are provided, serving as the criterion for the interval stability in high-order moment of SITSFSs. Different from the general concept of stability, besides determining the stability in high-order moment of the system, high-order moment interval stability can also describe the dynamic performance in high-order moment of systems. Then, the proposed approach in combination with fuzzy control theory provides a reliable and effective method for designing an interval state feedback controller that can ensure the stability of systems and regulate convergence rate of systems. Furthermore, moment observability of SITSFSs has also been addressed in this article. Finally, the numerical simulation verifies the validity and suitability of the proposed method.

Fixed-time trajectory tracking control of a quadrotor UAV under time-varying wind disturbances: theory and experimental validation

This paper adopts a fixed-time method to study the trajectory tracking issue of a quadrotor unmanned aerial vehicle (UAV) under time-varying wind disturbances. Firstly, in order to address the impact of time-varying wind disturbances on UAV, a fixed-time disturbance observer is constructed to accurately estimate wind disturbances. Secondly, to improve control accuracy and convergence rate, a robust fixed-time controller is designed for the position and attitude system by combining the sliding mode control theory with fixed-time techniques. Furthermore, it is rigorously analyzed that the tracking error of the observer and controller can converge to zero via Lyapunov criterion, and the convergence time is independent of the initial state. Finally, the effectiveness and robustness of the designed control strategy are verified by numerical simulations and actual flight experiments, providing an effective connection between control theory and practical applications.

A design an optimized fuzzy adaptive proportional-integral-derivative controller for anti-lock braking systems

This paper introduces a new control theory for optimizing anti-lock braking systems (ABS) in automotive applications. Anti-lock systems play a critical role in ensuring vehicle safety during braking by preventing wheel lock-up. However, conventional ABS control algorithms often struggle to adapt to changing road conditions and vehicle dynamics. The error tuning mechanism was not perfect because the occurrence of error is unique based on each application. In response to this challenge, we propose a new approach that combines fuzzy logic and adaptive proportional-integral-derivative (PID) control. The controller more robust to noise and uncertainties in the system. The present research work has concentrated on designing a novel Wavelet Fuzzy adaptive-hybrid Lion-strawberry Proportional-Integral-Derivative (WFA-HLSPID) for the ABS to maximize the control performance while it applied in the vehicle. Simulation results and comparative analysis demonstrate the superior performance of the proposed control theory when compared to traditional ABS systems. The optimized fuzzy adaptive PID control not only enhances vehicle stability during braking but also improves stopping distance. In all cases, the stopping distance, slip rate, and friction have been validated using MATLAB/Simulink.

Enzymatic Metabolic Switches of Astrocyte Response to Lipotoxicity as Potential Therapeutic Targets for Nervous System Diseases.

Astrocytes play a pivotal role in maintaining brain homeostasis. Recent research has highlighted the significance of palmitic acid (PA) in triggering pro-inflammatory pathways contributing to neurotoxicity. Furthermore, Genomic-scale metabolic models and control theory have revealed that metabolic switches (MSs) are metabolic pathway regulators by potentially exacerbating neurotoxicity, thereby offering promising therapeutic targets. Herein, we characterized these enzymatic MSs in silico as potential therapeutic targets, employing protein-protein and drug-protein interaction networks alongside structural characterization techniques. Our findings indicate that five MSs (P00558, P04406, Q08426, P09110, and O76062) were functionally linked to nervous system drug targets and may be indirectly regulated by specific neurological drugs, some of which exhibit polypharmacological potential (e.g., Trifluperidol, Trifluoperazine, Disulfiram, and Haloperidol). Furthermore, four MSs (P00558, P04406, Q08426, and P09110) feature ligand-binding or allosteric cavities with druggable potential. Our results advocate for a focused exploration of P00558 (phosphoglycerate kinase 1), P04406 (glyceraldehyde-3-phosphate dehydrogenase), Q08426 (peroxisomal bifunctional enzyme, enoyl-CoA hydratase, and 3-hydroxyacyl CoA dehydrogenase), P09110 (peroxisomal 3-ketoacyl-CoA thiolase), and O76062 (Delta(14)-sterol reductase) as promising targets for the development or repurposing of pharmacological compounds, which could have the potential to modulate lipotoxic-altered metabolic pathways, offering new avenues for the treatment of related human diseases such as neurological diseases.

Teaching Medical Procedural Skills for Performance.

Procedures are a core element of medical professional practice. Today's training approach was formulated in the mid-twentieth century based on a computer analogue of the brain. Despite minor modifications, the system has remained relatively unchanged for the past 70 years. It delivers competence. However, competence is not reliable performance. The inability to adapt to the variety of patients and variations in the performance environments, such as the operating room, results in patient morbidity and mortality. There is a need for changes in the development and training of medical procedural skills based on current theories of skill acquisition, movement theory, and motor control. Achieving optimal performance necessitates the ability to adapt through training in diverse patient and performance environments rather than merely imitating prescribed movements. We propose a novel model of training, the Constraints-Led Approach, which allows for robust training by altering the factors affecting skill acquisition and lifelong learning.

The state of the theory and methodology of control in the field of public administration

The article analyses the current state of the theory and methodology of control in the public authorities. The main purpose of the study is to identify key aspects and determine ways to optimise control mechanisms to ensure the legitimacy and effectiveness of government structures. The article examines traditional and new approaches to control based on interaction with civil society and the principles of openness and transparency. Particular attention is paid to an integrated approach to reforming control mechanisms and the need to involve the public in the processes of management and control in the public sector. The main part of the article examines various aspects of control, starting with traditional approaches to control, such as state supervision, the authors consider its evolution in the context of the development of civil society and democratic institutions. A detailed analysis of the functional potential of control reveals the need to abandon the traditional view of it as a purely «forceful» influence. The authors examine how control can be used not only to identify shortcomings in the work of state structures, but also to prevent possible violations, optimise management processes and increase overall responsibility. The conclusions of the article indicate that control in the sphere of public authority should be defined as an independent type of public activity with clearly defined directions, forms and institutional mechanisms. This will ensure greater efficiency and focus of control measures. The author emphasises the importance of developing modern control mechanisms to ensure stability, legality and trust in society. The essence of the updated doctrine of the «civic duty to rebel» is to ensure the controlled and responsible exercise of public power. This emphasises the importance of the people’s real ability to influence the processes of governance and establish new mechanisms to ensure justice in case of abuse of power.

Multi‐event‐based distributed cooperative predictive control for the multi‐agent system

AbstractIn this paper, we provide the detailed distributed cooperative predictive control scheme for the multi‐agent system (MAS) affected by the network delays. Firstly, we restate the distributed cooperative predictive control problem by introducing iterative learning‐based MAS, which can guarantee the cooperative predictive control of the MAS. To alleviate the unfavorable network constrains, the multi‐event‐triggered conditions are formulated via considering the predictive and error data sufficiently. With these preparations, the well‐posed Roesser‐type two‐dimensional (2D) system is achieved equivalently to the distributed iterative learning predictive system. On this basis, we employ 2D predictive system analysis and control theory to realize the predictive control for the equivalent 2D system and then obtain the collaborative predictive control for the MAS under multi‐event‐triggered mechanism. The corresponding stability criteria, controller, and observer gains are provided by the executable matrix constraints and algorithms design. To conclude the paper, the numerical example is proposed to illustrate the effectiveness and practicability of the provided methods and algorithms.

Dual therapy of cancer using optimal control supported by swarm intelligence.

The scientific revolution in the treatment of many illnesses has been significantly aided by stem cells. This paper presents an optimal control on a mathematical model of chemotherapy and stem cell therapy for cancer treatment. To develop effective hybrid techniques that combine the optimal control theory (OCT) with the evolutionary algorithm and multi-objective swarm algorithm. The developed technique is aimed to reduce the number of cancerous cells while utilizing the minimum necessary chemotherapy medications and minimizing toxicity to protect patients' health. Two hybrid techniques are proposed in this paper. Both techniques combined OCT with the evolutionary algorithm and multi-objective swarm algorithm which included MOEA/D, MOPSO, SPEA II and PESA II. This study evaluates the performance of two hybrid techniques in terms of reducing cancer cells and drug concentrations, as well as computational time consumption. In both techniques, MOEA/D emerges as the most effective algorithm due to its superior capability in minimizing tumour size and cancer drug concentration. This study highlights the importance of integrating OCT and evolutionary algorithms as a robust approach for optimizing cancer chemotherapy treatment.

Atypical emotion sharing in individuals with mirror sensory synaesthesia

ABSTRACT Being able to empathise with others is a crucial ability in everyday life. However, this does not usually entail feeling the pain of others in our own bodies. For individuals with mirror-sensory synaesthesia (MSS), however, this form of empathic embodiment is a common feature. Our study investigates the empathic ability of adults who experience MSS using a video-based empathy task. We found that MSS participants did not differ from controls on emotion identification and affective empathy; however, they showed higher affect sharing (degree to which their affect matches what they attribute to others) than controls. This finding indicates difficulties with self-other distinction, which our data shows results in fewer signs of prosocial behaviour. Our findings are in line with the self-other control theory of MSS and highlight how the use of appropriate empathy measures can contribute to our understanding of this important socio-affective ability, both in typical and atypical populations.

“Troubled” Meanings: An Affect Control Theory Exploration of the Conflict in Northern Ireland

The Troubles in Northern Ireland ranks among the most violent periods in recent history. While social movements scholars have long sought to understand the conflict, often they do not include micro-level approaches. We use affect control theory and cultural meanings among Catholics gathered at the height of The Troubles to create prototypical group members and then simulate interactions across and between groups. Using cultural meanings gathered at a Catholic high school in Belfast in 1977, we find that Catholic (in-group) identities hold more positive meanings than Protestant identities. This remains true for identities within the paramilitary organizations and non-combatant identities. However, we find that the meanings of combatant identities are much lower in evaluation—a measure of goodness—than non-combatant identities. Our simulations suggest that interactions between groups are expected to be relatively innocuous. However, we do find that, in simulations, combatants—on both sides of the conflict—are expected to interact negatively with others. These findings and the methods we use suggest future avenues for both researchers and policymakers to better understand conflict and peacemaking.

СИНТЕЗ ЭЛЕКТРОПРИВОДА КОМПРЕССОРНОЙ УСТАНОВКИ ЭЛЕКТРОВОЗА

The study objective is to improve the reliability and energy efficiency of the electric locomotive compressor drive. To achieve this, the required power of the electric motor was calculated the performance of the compressor unit, the volume of the main tanks and the selected electric motor were checked to start and adjust the capacity at low supply voltage. The task to which the paper is devoted is to design an electric drive for a compressor motor. A vector model of an asynchronous motor was made. The process of direct start-up and starting an asynchronous electric motor with the use of a scalar control system and IR compensation were modeled. Research methods: systematic approach; provisions of the theory of automatic control and electrical engineering; numerical PC simulation using Matlab software package. The novelty of the work is in the use of a frequency-controlled electric drive to start the asynchronous electric motor of the compressor unit instead of the normally installed starting capacitors. This design solution will significantly reduce the amplitude of the starting currents, which in turn will reduce the wear of the winding insulation. The study results are presented, based on the results of the constructed models, as graphs of the dependencies of speed and current during direct start of the electric motor and with the control system. Conclusions: the scalar control system, thanks to the introduction of an intensity setter, as well as IR compensation diagram, allow extending the transition time and controlling the value of the inrush current in acceptable values. Such an approach to make an electric drive compressor motor is able to significantly increase its reliability and service life, as well as to increase the overall energy efficiency of the system.