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This paper focuses on studying the problem of robust output practical stability of time-varying nonlinear control systems. The main innovation lies in the fact that the proposed approach for stability analysis allows for the computation of bounds that characterize the asymptotic convergence of solutions to a small ball centered at the origin using a Lyapunov method with a definite derivative. Under different conditions on the perturbation, the authors demonstrate that the system can be globally robustly asymptotically output stable by designing a candidate feedback controller. Finally, three examples are given to illustrate the practical implications and significance of the theoretical results. [ABSTRACT FROM AUTHOR]

During worldwide epidemic of COVID-1, people started to depend on social media apps to cure their boredom. It was beneficial to provide information easily to a wide audience. The increased time people spent on their phones led to many, including teenagers and children, becoming addicted to social media, particularly TikTok. As a result, this usage of TikTok became an epidemic itself. Therefore, in this work, a fractional mathematical model is being developed to analyze the influence of TikTok on human population of different ages. In this paper, two equilibrium points for this model will be discussed and the theoretical stability will be proven. For numerical analysis, fractional Euler’s method will be used and the results will be discussed graphically and which group of population will be affected the most by frequent use of TikTok. [ABSTRACT FROM AUTHOR]

This article presents an innovative approach for distinguishing replay attacks from faults in hybrid systems. To develop the idea at first, a novel technique for replay attack detection, which differentiates it from typical system faults, is introduced. This is achieved through a tactical combination of stochastic coding and a window‐based comparison mechanism, setting a new standard in hybrid system security. In the realm of fault detection, robust L2‐L∞ adaptive observers are employed for precise mode detection, allowing for an accurate assessment of the system's operational state. Additionally, robust H∞ Sliding Mode Observers are utilized to identify abnormal behaviours in the system's output, further solidifying the fault detection capabilities. A significant enhancement in the proposed approach is the integration of a Luenberger observer with the Butterworth low‐pass filter. This novel addition not only refines the filtering process but also contributes to the overall reliability and accuracy of the system. The efficiency and versatility of these methods are demonstrated through their application to a four‐tank hybrid system. This practical simulation showcases the adaptability of the approach to real‐world scenarios, highlighting its potential in diverse applications within the field of hybrid systems. [ABSTRACT FROM AUTHOR]

This paper investigates the challenges associated with the velocity consensus and the lane following in heterogeneous automated vehicles considered as agents of a multi agent system. A cloud‐based distributed model predictive control structure is proposed to address the distributed intrinsic of such systems. Moreover, for situations that the cloud‐based control signal is not reachable, backup controllers based on explicit distributed model predictive control are developed. The proposed cloud‐based controller is implemented in the Amazon cloud servers to show the practicality of the proposed approach. Simulation results are presented to demonstrate the effectiveness of our methodology in terms of handling practical limitations of connected and automated vehicles. [ABSTRACT FROM AUTHOR]

In the present study, we introduce the two-dimensional Chlodovsky-type Bernstein operators based on the (p , q) -integer. By leveraging the inherent symmetry properties of (p , q) -integers, we examine the approximation properties of our new operator with the help of a Korovkin-type theorem. Further, we present the local approximation properties and establish the rates of convergence utilizing the modulus of continuity and the Lipschitz-type maximal function. Additionally, a Voronovskaja-type theorem is provided for these operators. We also investigate the weighted approximation properties and estimate the rate of convergence in the same space. Finally, illustrative graphics generated with Maple demonstrate the convergence rate of these operators to certain functions. The optimization of approximation speeds by these symmetric operators during system control provides significant improvements in stability and performance. Consequently, the control and modeling of dynamic systems become more efficient and effective through these symmetry-oriented innovative methods. These advancements in the fields of modeling fractional differential equations and control theory offer substantial benefits to both modeling and optimization processes, expanding the range of applications within these areas. [ABSTRACT FROM AUTHOR]

The article discusses the application of fuzzy control theory in non-linear networked systems to enhance security against cyber-attacks. It focuses on various control strategies, such as resilient fuzzy control synthesis and adaptive control, to address security issues like denial-of-service attacks and deception attacks. The research presented in the article covers a wide range of topics related to control and security of information physical systems, resilient fuzzy control, and adaptive control in non-linear multi-agent systems and energy security management. The special issue includes 17 peer-reviewed papers that explore innovative approaches to address security challenges in non-linear networked systems. [Extracted from the article]

This work addresses the nonfragile sampled‐data control (SDC) of the Takagi‐Sugeno (T‐S) fuzzy model for cyber‐physical systems (CPSs) subject to cyber‐attacks (CAs). The sets of random variables satisfying the Bernoulli distribution are utilized to depict the probability of the data transmitted by the network being subjected to stochastic delay and CAs. Then, the information for the entire sampling interval tk$t_k$ to tk+1$t_{k+1}$ with a relation of the augmented state vectors is considered for designing the SDC of the T‐S fuzzy systems (TSFSs). In this regard, the fractional delayed state looped functional (LF) approach is presented to handle the formulated Lyapunov‐Krasovskii functional. Based on the LF method, new criteria are acquired to design SDC such that the proposed CPS is asymptotically stable in terms of linear matrix inequalities. It is worth noting that the proposed control strategy for the TSFSs guarantees stable performance while dealing with the attacks in the numerical section. Also, for the various attacking strengths of periodic and non‐periodic nature, the TSFSs have been simulated to show the efficiency of the proposed control method. Additionally, three numerical models have been performed to validate the less conservatism and superiority of the derived results over the existing ones. [ABSTRACT FROM AUTHOR]

This paper deals with the event‐triggered dynamic output feedback control for fuzzy systems against denial‐of‐service (DoS) attacks. The DoS attack behavior is modeled using a binary Markov chain to reflect the unreliability of the communication channel. To reduce network communication overhead, a novel probabilistic event‐triggered protocol with uncertain probabilities is introduced. Additionally, the potential mismatches between premise variables and modes in both the fuzzy system and the controller are accounted, leading to the design of a dual‐asynchronous dynamic output feedback controller. Finally, the efficacy of the proposed approach is validated through a comprehensive numerical example, highlighting its effectiveness in handling asynchronous control scenarios and DoS attack resilience in fuzzy systems. [ABSTRACT FROM AUTHOR]

We study the instantaneous inference of an unbounded planar flow from sparse noisy pressure measurements. The true flow field comprises one or more regularized point vortices of various strength and size. We interpret the true flow's measurements with a vortex estimator, also consisting of regularized vortices, and attempt to infer the positions and strengths of this estimator assuming little prior knowledge. The problem often has several possible solutions, many due to a variety of symmetries. To deal with this ill posedness and to quantify the uncertainty, we develop the vortex estimator in a Bayesian setting. We use Markov-chain Monte Carlo and a Gaussian mixture model to sample and categorize the probable vortex states in the posterior distribution, tailoring the prior to avoid spurious solutions. Through experiments with one or more true vortices, we reveal many aspects of the vortex inference problem. With fewer sensors than states, the estimator infers a manifold of equally possible states. Using one more sensor than states ensures that no cases of rank deficiency arise. Uncertainty grows rapidly with distance when a vortex lies outside of the vicinity of the sensors. Vortex size cannot be reliably inferred, but the position and strength of a larger vortex can be estimated with a much smaller one. In estimates of multiple vortices their individual signs are discernible because of the nonlinear coupling in the pressure. When the true vortex state is inferred from an estimator of fewer vortices, the estimate approximately aggregates the true vortices where possible. [ABSTRACT FROM AUTHOR]

The concept of eigenvalues has recently been generalized for nonlinear systems, but the method to find them is missing. Unlike the linear case, now one has to deal with non-commutative polynomials from the Ore ring. In the paper, the Ore determinant of a polynomial matrix, describing generic linearization of the state equations, is used instead of the standard definition of determinant of the polynomial matrix with real coefficients. It is shown how to compute the Ore determinant of a polynomial matrix associated with the nonlinear system and conjectured that the eigenvalues can be found from factorization of the Ore determinants of the corresponding system matrix. Moreover, it is proved that such factorization into the first-order polynomials can always be done. Many examples illustrate the computations and concepts throughout the paper. [ABSTRACT FROM AUTHOR]

This research introduces a fractional high-order sliding mode control (FHOSMC) method that utilises an inverse integral fractional order, 0 < β < 1 , as the high order on the FHOSMC reaching law, exhibiting a novel contribution in the related field of study. The application of the proposed approach into a bioreactor system via diffeomorphism operations demonstrates a notable improvement in the management of the bioreactor dynamics versus classic controllers. The numerical findings highlight an improved precision in tracking reference signals and an enhanced plant stability compared to proportional–integral–derivative (PID) controller implementations within challenging disturbance scenarios. The FHOSMC effectively maintains the biomass concentration at desired levels, reducing the wear of the system as well as implementation expenses. Furthermore, the theoretical analysis of the convergence within time indicates substantial potential for further enhancements. Subsequent studies might focus on extending this control approach to bioreactor systems that integrate sensor technologies and the formulation of adaptive algorithms for real-time adjustments of β -type fractional-orders. [ABSTRACT FROM AUTHOR]

This article considers the problem of output feedback stabilization for a class of stochastic high‐order nonlinear time‐delay systems with unknown output function. For stochastic high‐order nonlinear time‐delay systems, based on the Lyapunov stability theorem, by combining the addition of one power integrator and homogeneous domination method, the maximal open sector Δ$\Delta$ of output function is given. As long as output function belongs to any closed sector included in Δ$\Delta$, an output feedback controller can be developed to guarantee the closed‐loop system globally asymptotically stable in probability. [ABSTRACT FROM AUTHOR]

This paper explains the design and implementation of a novel didactic strategy based on learning styles for teaching automatic control theory in engineering. Traditional research on engineering education has worked to develop teaching methodologies, educational resources, and assessment tasks without considering the learning styles of the students. However, it is important to consider the learning styles of students to increase the number of useful tools that aid students in understanding abstract ideas and difficult concepts. To demonstrate that it is possible to design excellent courses of automatic control by using class activities concerning the learning styles of students. The proposal includes knowing the learning styles of students that enable professors to design appropriate didactic strategies based on the student needs. The proposed didactic strategy is appropriate and enhances the understanding of difficult concepts related to automatic control. [ABSTRACT FROM AUTHOR]

In applied and computational mathematics, quaternions are fundamental in representing three-dimensional rotations. However, specific types of quaternionic linear matrix equations remain few explored. This study introduces new quaternionic linear matrix equations and their necessary and sufficient conditions for solvability. We employ a methodology involving lemmas and ranks of coefficient matrices to develop a novel algorithm. This algorithm is validated through numerical examples, showing its applications in advanced fields. In control theory, these equations are used for analyzing control systems, particularly for spacecraft attitude control in aerospace engineering and for control of arms in robotics. In quantum computing, quaternionic equations model quantum gates and transformations, which are important for algorithms and error correction, contributing to the development of fault-tolerant quantum computers. In signal processing, these equations enhance multidimensional signal filtering and noise reduction, with applications in color image processing and radar signal analysis. We extend our study to include cases of η -Hermitian and i-Hermitian solutions. Our work represents an advancement in applied mathematics, providing computational methods for solving quaternionic matrix equations and expanding their practical applications. [ABSTRACT FROM AUTHOR]

Thanks to the emerging integration of algorithms and simulators, recent Driving Simulators (DS) find enormous potential in applications like advanced driver-assistance devices, analysis of driver's behaviours, research and development of new vehicles and even for entertainment purposes. Driving simulators have been developed to reduce the cost of field studies, allow more flexible control over circumstances and measurements, and safely present hazardous conditions. The major challenge in a driving simulator is to reproduce realistic motions within hardware constraints. Motion Cueing Algorithm (MCA) guarantees a realistic motion perception in the simulator. However, the complex nature of the human perception system makes MCA implementation challenging. The present research aims to improve the performance of driving simulators by proposing and implementing the MCA algorithm as a control problem. The approach is realized using an actual vehicle model integrated with a detailed model of the human vestibular system, which accurately reproduces the driver's perception. These perception motion signals are compared with simulated ones. A 2-DOF stabilized platform model is used to test the results from the two proposed control strategies, Proportional Integrator and Derivative (PID) and Model Predictive Control (MPC). [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the challenges associated with the velocity consensus and the lane following in heterogeneous automated vehicles considered as agents of a multi agent system. A cloud‐based distributed model predictive control structure is proposed to address the distributed intrinsic of such systems. Moreover, for situations that the cloud‐based control signal is not reachable, backup controllers based on explicit distributed model predictive control are developed. The proposed cloud‐based controller is implemented in the Amazon cloud servers to show the practicality of the proposed approach. Simulation results are presented to demonstrate the effectiveness of our methodology in terms of handling practical limitations of connected and automated vehicles.

Abstract This article presents an innovative approach for distinguishing replay attacks from faults in hybrid systems. To develop the idea at first, a novel technique for replay attack detection, which differentiates it from typical system faults, is introduced. This is achieved through a tactical combination of stochastic coding and a window‐based comparison mechanism, setting a new standard in hybrid system security. In the realm of fault detection, robust L2‐L∞ adaptive observers are employed for precise mode detection, allowing for an accurate assessment of the system's operational state. Additionally, robust H∞ Sliding Mode Observers are utilized to identify abnormal behaviours in the system's output, further solidifying the fault detection capabilities. A significant enhancement in the proposed approach is the integration of a Luenberger observer with the Butterworth low‐pass filter. This novel addition not only refines the filtering process but also contributes to the overall reliability and accuracy of the system. The efficiency and versatility of these methods are demonstrated through their application to a four‐tank hybrid system. This practical simulation showcases the adaptability of the approach to real‐world scenarios, highlighting its potential in diverse applications within the field of hybrid systems.

The concept of eigenvalues has recently been generalized for nonlinear systems, but the method to find them is missing. Unlike the linear case, now one has to deal with non-commutative polynomials from the Ore ring. In the paper, the Ore determinant of a polynomial matrix, describing generic linearization of the state equations, is used instead of the standard definition of determinant of the polynomial matrix with real coefficients. It is shown how to compute the Ore determinant of a polynomial matrix associated with the nonlinear system and conjectured that the eigenvalues can be found from factorization of the Ore determinants of the corresponding system matrix. Moreover, it is proved that such factorization into the first-order polynomials can always be done. Many examples illustrate the computations and concepts throughout the paper.

Abstract This article considers the problem of output feedback stabilization for a class of stochastic high‐order nonlinear time‐delay systems with unknown output function. For stochastic high‐order nonlinear time‐delay systems, based on the Lyapunov stability theorem, by combining the addition of one power integrator and homogeneous domination method, the maximal open sector Δ of output function is given. As long as output function belongs to any closed sector included in Δ, an output feedback controller can be developed to guarantee the closed‐loop system globally asymptotically stable in probability.

This study aimed to analyze methods for modeling and controlling the output of nonlinear systems using feedback, analytical methods, mathematical modeling, and differential equation theory. Key findings include the mathematical characterization of equations and the analysis of system stability and asymptotic behavior. The study explored various methods for addressing problems in nonlinear systems, emphasizing the importance of identifying effective solutions. The research highlights the significance of developing effective approaches to solving complex problems involving nonlinear systems. Feedback is essential for controlling and correcting dynamic processes in systems with nonlinearities. The study's key finding is the mathematical characterization of equations describing nonlinear systems, providing insight into system structure and behavior under different parameters. Analyzing stability and asymptotic behavior allows for assessing system reliability and predicting long-term stability. This study contributes to the scientific understanding and development of methods for modeling and controlling nonlinear systems using feedback. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the control problem of spacecraft rendezvous with obstacle constraint, considering the external disturbance forces caused by orbit perturbation. Firstly, the translational dynamic model of spacecraft rendezvous is given and then rewritten into a second‐order fully‐actuated system form. Then, by employing the prescribed performance control method, the performance function and error transformation are determined, pre‐defining the prescribed performance bounds. Moreover, the fully‐actuated system approach is used to linearize the original nonlinear system, which simplifies the processes of control law design and ensures model accuracy. After that, to ensure that the spacecraft could avoid the dangerous zone during its manoeuvre, the artificial potential function is introduced, based on which a sliding mode surface is designed. Finally, the prescribed performance control–artificial potential function‐based control law is derived, further adopting the neuro‐adaptive method to deal with external interferences. The stability of the close‐loop control system is analysed through the Lyapunov approach and the effectiveness of the proposed control scheme is verified by carrying out a numerical simulation.

In applied and computational mathematics, quaternions are fundamental in representing three-dimensional rotations. However, specific types of quaternionic linear matrix equations remain few explored. This study introduces new quaternionic linear matrix equations and their necessary and sufficient conditions for solvability. We employ a methodology involving lemmas and ranks of coefficient matrices to develop a novel algorithm. This algorithm is validated through numerical examples, showing its applications in advanced fields. In control theory, these equations are used for analyzing control systems, particularly for spacecraft attitude control in aerospace engineering and for control of arms in robotics. In quantum computing, quaternionic equations model quantum gates and transformations, which are important for algorithms and error correction, contributing to the development of fault-tolerant quantum computers. In signal processing, these equations enhance multidimensional signal filtering and noise reduction, with applications in color image processing and radar signal analysis. We extend our study to include cases of $ \eta $-Hermitian and i-Hermitian solutions. Our work represents an advancement in applied mathematics, providing computational methods for solving quaternionic matrix equations and expanding their practical applications.

Abstract Due to variable and complex work environments, nonlinearities, uncertainty and disturbances are inevitable in multi‐agent systems. Approximation‐free control can address these issues without involving approximators, such as fuzzy logic systems and neural networks. However, some issues like the singularity problem caused by the signals exceeding the preset boundary in changing work conditions still remain. This paper proposes an adaptive and reliable approximation‐free control, which comprises a novel singularity compensator and a modified transforming function. The proposed control scheme performs better in terms of convergence rate and overshoot, avoids issues relating to singularity, and has added flexibility in terms of parameter choice. The proposed control law adapts to changes in operating conditions and nonlinearities—the efficacy of which is demonstrated using simulations.

Abstract This paper introduces an adaptive control scheme for quadrotor suspended load systems, to track desired trajectory with variable payload and wind disturbances. The dynamic model of the quadrotor suspended load system is developed, taking into account the impact of the wind field described by the Dryden model. To attenuate the effects of payload variation and wind disturbances on the system, an adaptive control method based on disturbance observers is devised. Additionally, the uniform boundedness of all error signals is demonstrated. The effectiveness of the designed control method is verified through simulations, which serves to strengthen its applicability in practical applications.

Abstract This paper proposes a nonlinear control law with a dynamic controller based on high gain observer (HGO) for a co‐axial double rotor magnetic gear (CADRMG) based on Halbach array used in wind turbines. The generalized canonical form (GCF) is used to normalize the nonlinear augmented system to achieve the dynamic control signal with a chain of integrator of system output. In addition, a tracking problem is defined to track high speed rotor (HSR) with respect to GCF. On the other hand, the HGO is used to estimate the error tracking at the expense of nonlinear terms. Furthermore, the nonlinear system observability of the augmented system is evaluated. A dynamic control law is used to solve the tracking problem based on HGO. This controller can eliminate the effect of load torque as a constant and unknown disturbance in the closed‐loop system. Moreover, the closed‐loop stability of the system under dynamic signal control is guaranteed. The system states estimation is calculated by recursive equations from tracking error estimations. Finally, numerical simulations are given to illustrate the theoretical results of proposed system.

Abstract This paper studies the H∞ control problem for polynomial time‐varying systems. The H∞ control problem has been much less investigated for time‐varying systems in comparison to the time‐invariant systems. Approximate dynamic programming (ADP) is an optimal method to solve the control problems. Therefore, it is valuable to solve the polynomial time‐varying H∞ control problem with the ADP approach. Considering the time as an independent variable for sum‐of‐squares (SOS) optimization problems, an SOS‐based ADP method is proposed to solve this problem. A policy iteration algorithm is presented, where in its policy evaluation step it is sufficient to solve an optimization problem. Some constraints are added to this optimization problem to guarantee the closed‐loop exponential stability. The convergence and stability properties of the proposed algorithm are stated and proven. Moreover, in order to design an H∞ controller with a smaller disturbance attenuation coefficient, a two‐loop algorithm is suggested. Finally, the effectiveness of the proposed method is demonstrated by simulation examples.

In this study based on the accelerated over relaxation (AOR) method we make an iterative scheme for solving generalized Lyapunov matrix equation$$ {A}{X}+{X}{B}+\sum_{j=1}^{m}{N}_j{X}{M}_j={C},$$over complex or real matrices. Then we analyze the convergence of the new iterative method in detail. There have been discussions for the calculation of optimal parameters. Finally a numerical example is given to demonstrate the capability of the new method.

This paper investigates the control problem of spacecraft rendezvous with obstacle constraint, considering the external disturbance forces caused by orbit perturbation. Firstly, the translational dynamic model of spacecraft rendezvous is given and then rewritten into a second‐order fully‐actuated system form. Then, by employing the prescribed performance control method, the performance function and error transformation are determined, pre‐defining the prescribed performance bounds. Moreover, the fully‐actuated system approach is used to linearize the original nonlinear system, which simplifies the processes of control law design and ensures model accuracy. After that, to ensure that the spacecraft could avoid the dangerous zone during its manoeuvre, the artificial potential function is introduced, based on which a sliding mode surface is designed. Finally, the prescribed performance control–artificial potential function‐based control law is derived, further adopting the neuro‐adaptive method to deal with external interferences. The stability of the close‐loop control system is analysed through the Lyapunov approach and the effectiveness of the proposed control scheme is verified by carrying out a numerical simulation. [ABSTRACT FROM AUTHOR]

In this paper we advocate for Isaacs' method for the solution of differential games to be applied to the solution of optimal control problems. To make the argument, the vehicle employed is Pontryagin's canonical optimal control example, which entails a double integrator plant. However, rather than controlling the state to the origin, we require the end state to reach a terminal set that contains the origin in its interior. Indeed, in practice, it is required to control to a prescribed tolerance rather than reach a desired end state; constraining the end state to a terminal manifold of co-dimension n − 1 renders the optimal control problem easier to solve. The global solution of the optimal control problem is obtained and the synthesized optimal control law is in state feedback form. In this respect, two target sets are considered: a smooth circular target and a square target with corners. Closed-loop state-feedback control laws are synthesized that drive the double integrator plant from an arbitrary initial state to the target set in minimum time. This is accomplished using Isaacs' method for the solution of differential games, which entails dynamic programming (DP), working backward from the usable part of the target set, as opposed to obtaining the optimal trajectories using the necessary conditions for optimality provided by Pontryagin's Maximum Principle (PMP). In this paper, the case is made for Isaacs' method for the solution of differential games to be applied to the solution of optimal control problems by way of the juxtaposition of the PMP and DP methods. [ABSTRACT FROM AUTHOR]

This paper introduces an adaptive control scheme for quadrotor suspended load systems, to track desired trajectory with variable payload and wind disturbances. The dynamic model of the quadrotor suspended load system is developed, taking into account the impact of the wind field described by the Dryden model. To attenuate the effects of payload variation and wind disturbances on the system, an adaptive control method based on disturbance observers is devised. Additionally, the uniform boundedness of all error signals is demonstrated. The effectiveness of the designed control method is verified through simulations, which serves to strengthen its applicability in practical applications. [ABSTRACT FROM AUTHOR]

Due to variable and complex work environments, nonlinearities, uncertainty and disturbances are inevitable in multi‐agent systems. Approximation‐free control can address these issues without involving approximators, such as fuzzy logic systems and neural networks. However, some issues like the singularity problem caused by the signals exceeding the preset boundary in changing work conditions still remain. This paper proposes an adaptive and reliable approximation‐free control, which comprises a novel singularity compensator and a modified transforming function. The proposed control scheme performs better in terms of convergence rate and overshoot, avoids issues relating to singularity, and has added flexibility in terms of parameter choice. The proposed control law adapts to changes in operating conditions and nonlinearities—the efficacy of which is demonstrated using simulations. [ABSTRACT FROM AUTHOR]

This paper looks at the bacteria chemotaxis control process utilizing the System Modeling Language (SysML) to leverage well‐defined and proven engineering tools for architecting, analyzing, and refining complex systems. It proposes a new methodology called reverse‐engineering object‐oriented systems engineering method (RE‐OOSEM) that converts descriptive biology research information into descriptive systems engineering information. It utilizes SysML and model‐based systems engineering (MBSE) to capture system architecture from biological system knowledge and inputs them into systems engineering tools. From an engineering point of view, this allows greater insight into how biological systems operate and suggests how much model detail is required to uncover a top‐down system understanding. RE‐OOSEM methodology guides the SysML chemotaxis control capture process. SysML syntax is used instead of biological syntax to facilitate biological chemotaxis control system analysis from an engineered system point of view. The model can act as a scaffolding to help uncover system function, the relationships of system components and processes, and bioinformatic phenotype and genotype correlation. An executable MathWorks Stateflow chemotaxis control process model based on the SysML architectural model is included. The results show the following engineering perspective observations. (1) Several control components are not dedicated but are available and utilized when needed. (2) Individual chemoreceptors act together as a sensor array. (3) Phosphate groups act as a signaling mechanism. (4) Methylation via CH3 groups of the chemoreceptor results in sensitivity adaptation. (5) Closed‐loop control collaboratively utilizes ligand bonding, phosphorylation, and methylation. (6) Timing relationships of the control subprocesses give insight into the system's architecture. [ABSTRACT FROM AUTHOR]

Hybrid fire testing is a testing method for structures on fire based on a substructuring method. A complete structure is divided in two substructures, one in a fire test laboratory (physical substructure), and one numerically simulated (numerical substructure). In fire engineering, some hybrid fire tests have been successfully performed in the last decades, but as the method is still in its infancy, these hybrid tests were limited to one-degree-of-freedom tests. The paper presents the first successful multi-degree-of-freedom hybrid test performed in fire engineering. The physical substructure is a steel column with an axial displacement and rotations at the ends controlled by electric jacks. The numerical substructure is a non-linear 2D plane frame structure modelled in SAFIR®. The equations of the algorithm, the experimental setup, the testing process, and the results are presented. [ABSTRACT FROM AUTHOR]

Overt controlees have been observed in different languages, and previous proposals diverge on whether to analyze them as lexicalized PROs or not. In this study, we present new evidence from Mandarin for non-PRO overt controlees: We argue that although the construction of embedding overt subjects under control verbs is indeed obligatory control, the overt controlees should not be analyzed as lexicalized PROs. Based on the distribution and behavior of overt controlees, this study also demonstrates a structural difference between exhaustive control and partial control, offering independent evidence for recent theoretical proposals on the structure of control and Mandarin clause structure. [ABSTRACT FROM AUTHOR]

In clinical endocrinology, it is often assumed that the results of thyroid hormone function tests (TFTs) before total thyroidectomy are considered euthyroid when the circulating concentrations of thyrotropin [TSH] and free thyroxine [FT4] are within the normal reference ranges. Postoperative thyroid replacement therapy with levothyroxine. The aim of L-T4 is to reproduce the preoperative euthyroid condition. Currently, intra-individual changes in the euthyroid set point before and after total thyroidectomy are only partly understood. After total thyroidectomy, a greater postoperative [FT4] than preoperative [FT4] for equivalent euthyroid [TSH] was found, with differences ranging from 3 to 8 pmol/L. This unexplained difference can be explained by the use of a mathematical model of the hypothalamus-pituitary-thyroid (HPT) axis set point theory. In this article, the postoperative HPT euthyroid set point was calculated using a dataset of total thyroidectomized patients with at least three distinguishable postoperative TFTs. The postoperative [TSH] set point was used as a homeostatic reference for the comparison of preoperative TFTs. The preoperative [FT4] value was equal to the postoperative [FT4] value in 50% of the patients, divided by a factor of ~ 1.25 (within +/- 10%). The factor of 1.25 stems from the lack of postoperative use of thyroidal triiodothyronine (T3). Furthermore, approximately 25% of the patients presented a greater preoperative [FT4] difference than postoperative [FT4]/1.25 combined with a normal [TSH] difference. Based on these observations, the effect of T3 on the value of the [FT4] set point was analyzed and explained from a control theory perspective. [ABSTRACT FROM AUTHOR]

Summary: Lack of psychological detachment from work during leisure time is related to unfavorable affective states and poor well‐being but little is known about the processes that drive this relationship. We propose that lack of detachment from work translates into next‐day negative and positive affect by specific thought processes. Building on a control‐theory approach to repetitive thought and rumination, we introduce a refined conceptualization of job‐related cognitions during leisure time that integrates a valence perspective (referring to negative versus positive events) with a temporal‐direction perspective (backward‐oriented vs. forward‐oriented). Using daily‐survey data collected from 243 employees over two workweeks, multilevel path analysis revealed that lack of detachment from negative events predicted backward‐oriented negative rumination and forward‐oriented solution seeking. Lack of detachment from positive events predicted backward‐oriented positive rumination and forward‐oriented goal generation. Only backward‐oriented negative rumination, in turn, predicted next‐day negative affect. Neuroticism and extraversion moderated the relationships between lack of detachment and job‐related cognitions, resulting in a particularly strong serial indirect effect between lack of detachment from negative events and next‐day negative affect for persons high in neuroticism. Our study helps to understand why and for whom lack of psychological detachment from work during leisure time is particularly adverse. [ABSTRACT FROM AUTHOR]

Because of COVID-19 lockdowns, managers and administrators have begun to look for new ways to monitor and control their stranded-at-home workforce. Yet long before the pandemic already, advancements in datification, sensorization, and artificial intelligence have given rise to what we call connected workplace surveillance. At the heart of this new mode of employee monitoring and control is the extension of the scope of data collection beyond what is necessary and reasonable for performance appraisals or managerial oversight. This includes treating an employee's body as a data source, disrespecting the boundaries between business and private life, or using gathered surveillance information for subtle persuasion, manipulation, and coercion. This article provides a new perspective on control theory, examining the characteristics of connected surveillance and comparing it to visual or computerized surveillance. Taking an employee-centric position, it also proposes a research agenda for critical, behavioral, and design-oriented scholars who wish to explore the identified issues. [ABSTRACT FROM AUTHOR]

This paper addresses the lower limits of encoding and processing the information acquired through interactions between an internal system (robot algorithms or software) and an external system (robot body and its environment) in terms of action and observation histories. Both are modeled as transition systems. We want to know the weakest internal system that is sufficient for achieving passive (filtering) and active (planning) tasks. We introduce the notion of an information transition system (ITS) for the internal system which is a transition system over a space of information states that reflect a robot's or other observer's perspective based on limited sensing, memory, computation, and actuation. An ITS is viewed as a filter and a policy or plan is viewed as a function that labels the states of this ITS. Regardless of whether internal systems are obtained by learning algorithms, planning algorithms, or human insight, we want to know the limits of feasibility for given robot hardware and tasks. We establish, in a general setting, that minimal information transition systems (ITSs) exist up to reasonable equivalence assumptions, and are unique under some general conditions. We then apply the theory to generate new insights into several problems, including optimal sensor fusion/filtering, solving basic planning tasks, and finding minimal representations for modeling a system given input-output relations. [ABSTRACT FROM AUTHOR]

Formation control is a fundamental task in the realm of autonomous multiagent systems. To drive a group of agents to maneuver continuously with the desired formation, this paper studies the finite‐time affine formation control problem with disturbances, input constraints and safety guarantee. A non‐singular terminal sliding mode control (NTSMC) is implemented to achieve finite‐time convergence of all followers to their desired positions. Additionally, an auxiliary system is deployed to address input constraints resulting from the physical properties of the affine formation system. To mitigate the impact of lumped disturbances, a finite‐time disturbance observer (FTDO) is employed to estimate the disturbances and compensate for their effects. Based on FTDO, the auxiliary system and the above NTSMC, a finite‐time robust controller is developed as the nominal controller. By modifying the nominal controllers to comply with safety constraints, control barrier functions are employed to ensure the safety of the formation system in obstacle‐filled environment. Finally, the effectiveness and feasibility of this method are validated through simulations and real‐world experiments. [ABSTRACT FROM AUTHOR]

An electrical motors, together with its appropriate drive system, is one of the most important elements of electromobility. In recent years, there has been a particular interest by academic researchers and engineers in permanent-magnet motors (PMSMs) in various applications, such as electric vehicles, Unmanned Aerial Vehicles (UAVs), elevator systems, etc., as the main source of drive transmission. Nowadays, the elevator industry, with the evolution of magnetic materials, has turned to gearless PMSMs over geared induction motors (IMs). One of the most important elements that is given special emphasis in these applications is proper motor design in consideration of the weight and speed of the chamber to be served during operation. This paper presents a design of a high-efficiency PMSM, in which finite elements analysis (FEA) and the study of the lift operating cycle provided useful conclusions on the magnetic field of the machine in different operating states. In addition, a simulated model was compared with experimental results of test operations. Furthermore, the drive system also required the use of appropriate electrical power and controls to drive the PMSM. Especially in elevator applications, the control of the motor speed by the variable voltage variable frequency technique (VVVF) is the most common technology used to avoid endangering the safety of the passengers. Thus, suitable speed and current controllers were used for this purpose. In our research, we focused on studying different control techniques using a suitable inverter to compare the system operation in each case studied. [ABSTRACT FROM AUTHOR]

In this paper, an improved zeroing neural network (ZNN) model is proposed to obtain the positive definite solutions of the continuous coupled Lyapunov matrix equations (CLMEs) associated with continuous‐time Markovian jump (CMJ) systems. To achieve this, a general ZNN model is established by constructing a matrix‐valued error function. Then, to accelerate the convergence rate of the proposed ZNN model, the latest estimation is introduced to obtain an improved ZNN model. Some convergence conditions have been derived for the presented improved ZNN model through Lyapunov theory. Comparisons among the improved ZNN model and the existing results are conducted to illustrate the advantages of the proposed improved ZNN model in numerical examples. [ABSTRACT FROM AUTHOR]

Harnessing complex body dynamics has long been a challenge in robotics, particularly when dealing with soft dynamics, which exhibit high complexity in interacting with the environment. Recent studies indicate that these dynamics can be used as a computational resource, exemplified by the McKibben pneumatic artificial muscle, a common soft actuator. This study demonstrates that bifurcations, including periodic and chaotic dynamics, can be embedded into the pneumatic artificial muscle, with the entire bifurcation structure using the framework of physical reservoir computing. These results suggest that dynamics not present in training data can be embedded through bifurcation embedment, implying the capability to incorporate various qualitatively different patterns into pneumatic artificial muscle without the need to design and learn all required patterns explicitly. Thus, this study introduces a novel approach to simplify robotic devices and control training by reducing reliance on external pattern generators and the amount and types of training data needed for control. [ABSTRACT FROM AUTHOR]

This paper proposes a nonlinear control law with a dynamic controller based on high gain observer (HGO) for a co‐axial double rotor magnetic gear (CADRMG) based on Halbach array used in wind turbines. The generalized canonical form (GCF) is used to normalize the nonlinear augmented system to achieve the dynamic control signal with a chain of integrator of system output. In addition, a tracking problem is defined to track high speed rotor (HSR) with respect to GCF. On the other hand, the HGO is used to estimate the error tracking at the expense of nonlinear terms. Furthermore, the nonlinear system observability of the augmented system is evaluated. A dynamic control law is used to solve the tracking problem based on HGO. This controller can eliminate the effect of load torque as a constant and unknown disturbance in the closed‐loop system. Moreover, the closed‐loop stability of the system under dynamic signal control is guaranteed. The system states estimation is calculated by recursive equations from tracking error estimations. Finally, numerical simulations are given to illustrate the theoretical results of proposed system. [ABSTRACT FROM AUTHOR]

This paper studies the H∞ control problem for polynomial time‐varying systems. The H∞ control problem has been much less investigated for time‐varying systems in comparison to the time‐invariant systems. Approximate dynamic programming (ADP) is an optimal method to solve the control problems. Therefore, it is valuable to solve the polynomial time‐varying H∞ control problem with the ADP approach. Considering the time as an independent variable for sum‐of‐squares (SOS) optimization problems, an SOS‐based ADP method is proposed to solve this problem. A policy iteration algorithm is presented, where in its policy evaluation step it is sufficient to solve an optimization problem. Some constraints are added to this optimization problem to guarantee the closed‐loop exponential stability. The convergence and stability properties of the proposed algorithm are stated and proven. Moreover, in order to design an H∞ controller with a smaller disturbance attenuation coefficient, a two‐loop algorithm is suggested. Finally, the effectiveness of the proposed method is demonstrated by simulation examples. [ABSTRACT FROM AUTHOR]

Sustainable forest management is one of the warming issues in the present century. In this manuscript, we have employed the model of control theory to control the consequence of toxicity and illegal logging of mature trees in the ecosystem of Sundarbans, the largest mangrove forest in the world. In this investigation, we have momentarily mentioned some of the fields in which these challenges are present. These fields especially consist of sustainable forestry management of ecosystem. We have reflected on the modified Leslie-Gower response function to set up as the alternative resource for industries when forestry resources are divested. The boundedness, persistence, equilibria and stability are examined.Our main aim is to investigate the spans and applications of control theory to control the effect of toxicity and illegal logging. [ABSTRACT FROM AUTHOR]

In this study based on the accelerated over relaxation (AOR) method we make an iterative scheme for solving generalized Lyapunov matrix equation ... over complex or real matrices. Then we analyze the convergence of the new iterative method in detail. There have been discussions for the calculation of optimal parameters. Finally a numerical example is given to demonstrate the capability of the new method. [ABSTRACT FROM AUTHOR]

A student's learning system is a system that guides the student's knowledge acquisition process using available learning resources to produce certain learning outcomes that can be evaluated based on the scores of questions in an assessment. Such a learning system is analogous to a control system, which regulates the process of a plant through a controller in order to generate a desired response that can be inferred from sensor measurements. Inspired by this analogy, this study proposes to model the monitoring of students' knowledge acquisition process from a control-theory viewpoint, which is referred to as control knowledge tracing (CtrKT). The proposed CtrKT comprises a dynamic equation that characterizes the temporal variation of students' knowledge states in response to the effects of learning resources and an observation equation that maps their knowledge states to question scores. With this formulation, CtrKT enables tracking students' knowledge states, predicting their assessment performance, and teaching planning. The insights and accuracy of CtrKT in postulating the knowledge acquisition process are analyzed and validated using experimental data from psychology literature and two naturalistic datasets collected from a civil engineering undergraduate course. Results verify the feasibility of using CtrKT to estimate the overall assessment performance of the participants in the psychology experiments and the students in the naturalistic datasets. Lastly, this study explores the use of CtrKT for teaching scheduling and optimization, discusses its modeling issues, and compares it with other knowledge-tracing approaches.

In this paper, we study the influence of seasonal demands and forecasts on the performance of an Automatic Pipeline, Variable Inventory, Order-Based, Production Control System (APVIOBPCS) using linear control theory. In particular, we consider a system that uses a seasonal forecast based on a no-trend, additive-seasonality exponential-smoothing model, and compare its performance to an equivalent system using simple exponential smoothing. We find that the system with seasonal forecasting significantly outperforms the system with simple exponential smoothing under certain demand assumptions. With optimal parameter settings, the forecast error of the seasonal model can be up to 40% lower. However, we also find that the forecast superiority does not necessarily translate to the performance of the system measured through the bullwhip metrics. In addition, the seasonal forecasting model is very sensitive to the demand frequency and smoothing parameters, while the simple exponential smoothing model is very robust. This implies that the real life benefits of implementing a seasonal forecasting model are not obvious and depend on the particular situation; under a large number of settings (e.g. low seasonality, high-smoothing), the good performance of simple exponential smoothing certainly justifies its popularity in the industry and research worlds alike. [ABSTRACT FROM AUTHOR]