Control Theory Research Articles

ОПРЕДЕЛЕНИЕ УРОВНЯ ЭКСПЛУАТАЦИОННОЙ НАДЕЖНОСТИ ТРАНСПОРТНЫХ СРЕДСТВ

The study objective is to determine the level of operational reliability of the company's vehicles. The problem to which the paper is devoted is to develop an algorithm for calculating the influence of factors on the system of operational reliability of vehicles. Research methods are forecasting, experiment, mathematical modeling, statistical analysis, system analysis, machine aging theory, reliability theory, probability theory, control theory. The novelty of the work. A technique is developed to control operational reliability, which includes an algorithm for calculating the influence of factors on the operational reliability system. Study results. One of the main scientific tasks is to develop a technique for forecasting and assessing the level of operational reliability of vehicles as an indicator characterizing the ability of an enterprise to ensure the readiness of vehicles to fulfill their tasks. Conclusions: Implementation of the developed algorithm in motor transport enterprises makes it possible to determine the level of operational reliability of vehicles, including the provision of restoring the level of operational reliability to its maximum value.

Research on the Application of PID Control Algorithm and Fuzzy Control Theory in the Field of Temperature Control

In the field of industrial automation control systems, PID control algorithms are widely used in various control fields such as temperature, speed and position. Among them, fuzzy PID control can improve control accuracy and stability, can significantly improve product quality and production efficiency, in food processing, chemical engineering, pharmaceutical and textile industries have a wide range of applications. Therefore, this paper comprehensively discusses the theory of fuzzy PID control algorithm and its application in the field of temperature control. The advantages of fuzzy PID control in complex industrial environments are emphasized through a comparative analysis with other temperature control methods as well as traditional linear PID control. The research indicates that by optimizing the control algorithm and hardware design, the fuzzy PID control algorithm can reduce the system overshoot and steady state error, thus improving the control accuracy and response speed. At the same time, the introduction of multivariable PID controllers or the use of advanced multivariable control strategies can achieve coordinated control of multiple variables, thereby improving the performance of the entire system. In addition, the integration of advanced energy management system and data analysis technology can comprehensively monitor and optimize the management of energy consumption, further improving the energy efficiency of the system

Maximum-Power Stirling-like Heat Engine with a Harmonically Confined Brownian Particle

Heat engines transform thermal energy into useful work, operating in a cyclic manner. For centuries, they have played a key role in industrial and technological development. Historically, only gases and liquids have been used as working substances, but the technical advances achieved in recent decades allow for expanding the experimental possibilities and designing engines operating with a single particle. In this case, the system of interest cannot be addressed at a macroscopic level and their study is framed in the field of stochastic thermodynamics. In the present work, we study mesoscopic heat engines built with a Brownian particle submitted to harmonic confinement and immersed in a fluid acting as a thermal bath. We design a Stirling-like heat engine, composed of two isothermal and two isochoric branches, by controlling both the stiffness of the harmonic trap and the temperature of the bath. Specifically, we focus on the irreversible, non-quasi-static case—whose finite duration enables the engine to deliver a non-zero output power. This is a crucial aspect, which enables the optimisation of the thermodynamic cycle by maximising the delivered power—thereby addressing a key goal at the practical level. The optimal driving protocols are obtained by using both variational calculus and optimal control theory tools. Furthermore, we numerically explore the dependence of the maximum output power and the corresponding efficiency on the system parameters.

Machine Learning for Sparse Nonlinear Modeling and Control

Machine learning is rapidly advancing nearly every field of science and engineering, and control theory is no exception. In particular, it has shown incredible promise for handling several of the main challenges facing modern dynamics and control, including complexity, unmodeled dynamics, strong nonlinearity, and hidden variables. However, machine learning models are often expensive to train and deploy, fail to generalize beyond the training data, and suffer from a lack of explainability, interpretability, and guarantees, all of which limit their use in real-world and safety-critical control applications. Sparse nonlinear modeling and control techniques are a powerful class of machine learning that promote parsimony through sparse optimization, providing data-efficient models that are more interpretable and generalizable and have proven effective for control. In this review, we explore the use of sparse optimization in the context of machine learning to develop compact models and controllers that are easy to train, require significantly less data, and make low-latency predictions. In particular, we focus on applications in model predictive control and reinforcement learning, two of the foundational algorithms in control theory.

Experimental evaluation of DC-DC buck converter based on adaptive fuzzy fast terminal synergetic controller

This study suggests an enhanced version of the adaptive fuzzy fast terminal synergetic controller (AF-FTSC) for controlling the uncertain DC/DC buck converter based on the synergetic theory of control (STC) and newly developed terminal attractor technique (TAT). The benefits of the proposed SC algorithm involve the features of finite-time convergence, unaffected by parameter variations, and chattering-free phenomenon. A type-1 fuzzy logic system (T1-FLS) make the considered controller more robust and is utilized to estimate the undefined converter nonlinear dynamics without resorting to the usual linearization and simplifications of the converter model. Taking a switching DC-DC buck converter as a demonstration, the suggested AF-FTSC is thoroughly analyzed and executed on a dSPACE ds1103 controller board. The outcomes of the experiment confirm the competence and applicability of the suggested regulator.

Spatial-situational approach to airport transport security management

The trend in the development of systems for ensuring and managing aviation security of transport facilities in recent years has acquired the character of a transition from classical regulatory management schemes to automated control schemes using procedures based on the theory of optimal control. A number of factors can be identified that reduce the effectiveness of security management in the current system: problems related to time, post-factum processing of situational events, non-systemic collection and processing of information, the problem of hypothetical and potential security threats, the problem of security integrality and security systems integrality, the problem of indivisibility of security and the problem of vulnerability of transport infrastructure and, finally, the problems of forecasting the development of negative events. All this implies a transition to information security management, which is based on a spatial-situational approach. The paper substantiates the emergence of threat space, protection space and security space and shows the possibilities of using these spaces to solve the problems of formalization of security management procedures in the modeling process.

Affect and self-regulation at work: How do we take messy theories to the task?

Within this editorial, we highlight the importance of testing and extending our understanding of core theories associated with affect and self-regulation at work. Specifically, we center our initial discussion on two theories that have garnered substantial attention within the organizational literature—control theory and social cognitive theory—highlighting how these theories offer differential predictions for the benefits of positive and negative affect at work. We then shift our focus on providing three theoretical and/or empirical misses that have significant potential to advance our science forward: (1) arbitrary selection of self-regulation theories within research studies; (2) oversimplifying affective experiences into broad categories of positive and negative affect; and (3) forgetting the complexity of multi-emotion (or ambivalent) events. Overall, we highlight theoretical and empirical contributions, as well as limitations, in this literature, and provide paths forward to enhance both our understanding of affect and self-regulation at work.

Optimal Control of Heat Equation by Coupling FVM and FEM Codes

In this paper, the optimal control theory is applied to a temperature optimization problem by coupling finite element and finite volume codes. The optimality system is split into the state and adjoint system. The direct problem is solved by the widely adopted finite volume OpenFOAM code and the adjoint-control equation using a variational formulation of the problem with the in-house finite element FEMuS code. The variational formulation of the problem is the natural framework for accurately capturing the control correction while OpenFOAM guarantees the accuracy of the state solution. This coupling is facilitated through the open-source MED and MEDCoupling libraries of the SALOME platform. The code coupling is implemented with the MED libraries and additional routines added in the FEMuS and OpenFOAM codes. We demonstrate the accuracy, robustness, and performance of the proposed approach with examples targeting different objectives using distributed and boundary controls in each case.

Transfer of congruency effects between Stroop and multiplication tasks: Evidence that retrieval of multiplication facts requires inhibitory control.

Inhibitory control is classically considered a domain-general process, yet recent findings suggest it may operate in context-specific ways. This has important implications for theories in other cognitive domains, such as mathematics, in which inhibitory control is proposed to play a key role. Inhibitory control has been implicated in resolving interference between competing number facts when retrieving them from memory, yet clear evidence for this is lacking. Here we report two pre-registered experiments with adults that investigated transfer of inhibitory control between interleaved Stroop and multiplication fact retrieval trials. Experiment 1 (n=450) measured the congruency sequence effect, where transfer of inhibitory control between trials leads to a reduced congruency effect following an incongruent trial. Experiment 2 (n=370) measured transfer of the list-wide proportion congruency effect, where the congruency effect is reduced when incongruent trials are more frequent. We found evidence of transfer of the congruency sequence effect between Stroop and multiplication. This did not differ depending on whether the Stroop task used number or animal stimuli. There was no transfer of the list-wide proportion congruency effect. These results suggest that reactive, transient domain-general inhibitory control processes are involved in retrieving multiplication facts from memory. Our findings have implications for theories of cognitive control and mathematical cognition, but caution should be taken in interpreting implications for educational interventions.

ANÁLISE MATEMÁTICA E SIMULAÇÃO DO SISTEMA DE AMORTECIMENTO VEICULAR COM MATLAB

Abstract: In modern control theory, the concept of state variables plays a crucial role in the modeling and analysis of dynamic systems. These variables provide a comprehensive description of the system’s future behavior over time. The organization into a state vector enables a compact and complete representation of the system’s internal state. The state-space function describes the temporal evolution of these variables in response to system inputs, offering an efficient mathematical approach for the design and analysis of control systems. Applications range from industrial process control to autonomous vehicle control and robotics. By understanding and applying these concepts, control engineers develop effective solutions for challenges in complex systems, ensuring performance, stability, and efficiency in dynamic environments. Keywords: state variables; dynamic systems; state-space; control; stability.

Design of a Finite-Time Bounded Tracking Controller for Time-Delay Fractional-Order Systems Based on Output Feedback

This paper focuses on a class of fractional-order systems with state delays and studies the design problem of the finite-time bounded tracking controller. The error system method in preview control theory is first used. By taking fractional-order derivatives of the state equations and error signals, a fractional-order error system is constructed. This transforms the tracking problem of the original system into an input–output finite=time stability problem of the error system. Based on the output equation of the original system and the error signal, an output equation for the error system is constructed, and a memory-based output feedback controller is designed by means of this equation. Using the input–output finite-time stability theory and linear matrix inequality (LMI) techniques, the output feedback gain matrix of the error system is derived by constructing a fractional-order Lyapunov–Krasovskii function. Then, a fractional-order integral of the input to the error system is performed to derive a finite-time bounded tracking controller for the original system. Finally, numerical simulations demonstrate the effectiveness of the proposed method.

Dynamical analysis of HIV/AIDS and HBV co-infection model with drug-related kidney disease using optimal control theory

Dynamical analysis of HIV/AIDS and HBV co-infection model with drug-related kidney disease using optimal control theory

Performance improvement of manipulator actuated by pneumatic artificial muscles based on synergetic control and social spider optimisation algorithm

The manipulator actuated by pneumatic artificial muscles (PAM) is a widely used type of robotic arm in industrial automation. However, its performance can be limited by non-linear dynamics and uncertainties in the system. To overcome these limitations, this paper proposes a synergetic control strategy (SACT) to improve the performance of the SACT, a social spider optimisation algorithm (SSO) has been suggested for adjusting its parameters. To verify the performance of a PAM-actuated manipulator based on an optimal SACT controller, a computer simulation study was conducted using MATLAB software. Moreover, a comparison study between the optimal synergetic algorithm control theory and the optimal sliding mode controller (SMC) has been made in terms of robustness and transient behaviour characteristics. The provided simulation results have shown that the SACT controller exhibited quicker convergence towards the desired trajectory and maintained a lower steadystate error as compared to the SMC controller. Additionally, the SACT controller demonstrated more resilience to variations in parameters and showed more robust characteristics.

Low-dimensional controllability of brain networks.

Identifying the driver nodes of a network has crucial implications in biological systems from unveiling causal interactions to informing effective intervention strategies. Despite recent advances in network control theory, results remain inaccurate as the number of drivers becomes too small compared to the network size, thus limiting the concrete usability in many real-life applications. To overcome this issue, we introduced a framework that integrates principles from spectral graph theory and output controllability to project the network state into a smaller topological space formed by the Laplacian network structure. Through extensive simulations on synthetic and real networks, we showed that a relatively low number of projected components can significantly improve the control accuracy. By introducing a new low-dimensional controllability metric we experimentally validated our method on N = 6134 human connectomes obtained from the UK-biobank cohort. Results revealed previously unappreciated influential brain regions, enabled to draw directed maps between differently specialized cerebral systems, and yielded new insights into hemispheric lateralization. Taken together, our results offered a theoretically grounded solution to deal with network controllability and provided insights into the causal interactions of the human brain.

Fuzzy Adaptive Event-Triggered Consensus Control for Nonlinear Multiagent Systems With Output Constraints and DoS Attacks.

In this article, the fuzzy adaptive event-triggered consensus control issue is addressed for nonlinear multiagent systems (MASs) under output constraints and Denial of Service (DoS) attacks. First of all, fuzzy logic systems (FLSs) are utilized to approximate the unknown nonlinear functions. Then, a novel switching observer is constructed to observe the leader's state and handle DoS attacks. With the help of the exponent-dependent barrier Lyapunov functions (BLFs), the system output can be constrained within a preset region. Based on dynamic surface control (DSC) technique, the issue of computational complexity can be effectively avoided. Combining the designed switching observer and relative thresholds, a robust fuzzy adaptive event-triggered controller is developed, which ensures that the consensus output tracking errors converge to a small neighborhood of zero, and all signals in the closed-loop system keep bounded. Moreover, Zeno behavior can be avoided. Ultimately, simulation results are given to validate the feasibility and effectiveness of the proposed control strategy and theory.

Fractional-order input-to-state stability and its converse Lyapunov theorem

This paper establishes input-to-state stability (ISS) for fractional-order nonlinear systems, providing a stability constraint framework for fractional-order systems affected by external inputs. The general fractional comparison principle aids in constructing Lyapunov functions, offering criteria for input-to-state stability of fractional-order nonlinear systems. The concept of weak robustness and the converse Lyapunov theorem for fractional-order nonlinear systems further enhance the completeness of input-to-state stability in these systems. By designing systems to maintain fractional-order input-to-state stability (FOISS), the system can remain robust against the effects of bounded external inputs. Serving as a foundational ”toolbox,” this work provides insights into the stability analysis of fractional-order nonlinear systems, benefiting researchers and practitioners in the field of control theory.

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.

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.