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Intimate partner violence (IPV), including intimate partner homicide (IPH) and femicide, raises issues for general theories of crime, such as control and opportunity theories, that see close relationships among friends and family as barriers to interpersonal crimes. Crime-specific studies of both correlates and trends in IPV, including recent interrupted trend studies that examine the effects of COVID restrictions, often test opportunity theories absent considerations of theoretically driven images of actors. Review of empirical research on IPV and IPH reveals strong compatibility between the predictions of modern control theory and consistent findings from trend data. Barriers to understanding of the explanatory power of general theories of crime (including, for example, control theories and feminist perspectives) in contemporary research include use of poor definitions of intimacy, misspecification of age effects, failure to consider the versatility of offending behavior, neglecting the importance of trends in analogous behaviors, neglecting the role of situational factors in violence, and the limitations in the measurement of repetitive victimization. Theories such as routine activity and situational crime prevention that fail to explicitly include characteristics of actors can go only a limited way in providing meaningful policy. Research supports the potential policy effects of investments in early childhood and attention to situational barriers (including limitations on alcohol use and firearm availability) to reduce IPV. Although modern control theory is used to illustrate these issues, other general theories, like feminist theories, can make similar arguments. [ABSTRACT FROM AUTHOR]

Personalized educational interventions have been shown to facilitate successful and inclusive statistics, mathematics, and data science (SMDS) in higher education through timely and targeted reduction of heterogeneous training disparities caused by years of cumulative, structural challenges in contemporary educational systems. However, the burden on the institutions and instructors to provide personalized training resources to large groups of students is also formidable, and often unsustainable. We present Individualized Pathways and Resources to Adaptive Control Theory-Inspired Scientific Education (iPRACTISE), a free, publicly available web app that serves as a tool to facilitate personalized trainings on SMDS and related topics through provision of personalized training recommendations as informed by computerized assessments and individuals' training preferences. We describe the resources available in iPRACTISE, and some proof-of-concept evaluation results from deploying iPRACTISE to supplement in-person and online classroom teaching in real-life settings. Strengths, practical difficulties, and potentials for future applications of iPRACTISE to crowdsource and sustain personalized SMDS education are discussed. [ABSTRACT FROM AUTHOR]

The aim of this study is to present the notion of observability for a specific class of linear time-fractional systems of Riemann-Liouville type with a differentiation order between 1 and 2. To accomplish this goal, we first define the concept of observability and its features, then we extend the Hilbert Uniqueness Method (HUM) to determine the system's initial state. This method converts the reconstruction problem into a solvability one, leading to an algorithm that calculates the initial state. The effectiveness of the proposed algorithm is demonstrated through numerical simulations, which are presented in the final section. [ABSTRACT FROM AUTHOR]

This paper studies the event triggered delayed impulsive control strategy for quasi‐synchronization for complex dynamical systems with parameter mismatch and packet loss. By constructing a comparative lemma with time delays and Lyapunov function, a few sufficient synchronization conditions are proposed by Cauchy matrix with time delays to analyze the system. Meanwhile, the possibility of packet loss is also considered and the quasi‐synchronization of the system is investigated by using both event triggered delayed impulsive control and self‐triggered impulsive control mechanisms. Theoretical analysis results are validated by numerical simulations. [ABSTRACT FROM AUTHOR]

Counterproductive (or deviant) work behavior and organizational citizenship behavior are often collectively referred to as voluntary work behavior. The current paper examines the effects of counterdispositional levels of current counterproductive and citizenship behavior on their respective future levels. We contend that an employee's trait level of voluntary work behavior acts as a "set point" from which any deviations in the current state level trigger a discrepancy-reducing decrease or increase in the future state level of behavior. We moreover invoke the control theory idea of multiple-goal pursuit by suggesting that, on a given occasion, individuals may respond to a discrepancy in one form of voluntary work behavior (e.g., current counterproductive work behavior) by regulating the other form of voluntary work behavior (e.g., future organizational citizenship behavior) instead of or in addition to the same form of behavior (e.g., future counterproductive work behavior). Multilevel polynomial regression and response surface analysis of data from a large-scale archival experience-sampling method study involving 20,403 measurements of counterproductive and citizenship behavior from 1454 working adults yielded support for these control-theory-based predictions. We conclude by providing suggestions for theoretical and empirical extensions of the current work. [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]

We investigate Quantum Electrodynamics corresponding to the holographic brain theory introduced by Pribram to describe memory in the human brain. First, we derive a super-radiance solution in Quantum Electrodynamics with non-relativistic charged bosons (a model of molecular conformational states of water) for coherent light sources of holograms. Next, we estimate memory capacity of a brain neocortex, and adopt binary holograms to manipulate optical information. Finally, we introduce a control theory to manipulate holograms involving biological water's molecular conformational states. We show how a desired waveform in holography is achieved in a hierarchical model using numerical simulations. [ABSTRACT FROM AUTHOR]

Organized conflict, while confined by the laws of physics—and, under profound strategic incompetence, by the Lanchester equations—is not a physical process but rather an extended exchange between cognitive entities that have been shaped by path-dependent historical trajectories and cultural traditions. Cognition itself is confined by the necessity of duality, with an underlying information source constrained by the asymptotic limit theorems of information and control theories. We introduce the concept of a 'basic underlying probability distribution' characteristic of the particular cognitive process studied. The dynamic behavior of such systems is profoundly different for 'thin-tailed' and 'fat-tailed' distributions. The perspective permits the construction of new probability models that may provide useful statistical tools for the analysis of observational and experimental data associated with organized conflict, and, in some measure, for its management. [ABSTRACT FROM AUTHOR]

This paper proposes a non‐singular fixed‐time fault‐tolerant adaptive tracking control method for a kind of multi‐input and multi‐output nonlinear system, which is in form of non‐strict feedback and is subject to actuator faults. The major contribution, compared to other practical fixed‐time fault‐tolerant control methods, is that the fixed time can be calculated precisely. By applying fuzzy logic systems, the identification of unknown nonlinearities is achieved, and by the boundness of fuzzy basis functions, the algebraic loop problem is avoided. Based on the fixed‐time stability theory, the practical fixed‐time fault‐tolerant adaptive tracking control method is proposed with adaptive back‐stepping control technology, in which the control singular problem is overcome by applying the adding power integration method. By the Lyapunov function theory, the practical fixed‐time stability of the closed‐loop system is theoretically proved. And even in the case of unknown actuator faults, the tracking error of the controlled system for the given command signal can still converge to a small neighbourhood of zero within a fixed time, regardless of the system initial state. Finally, the proposed control method is successfully implemented on a multi‐motor control platform, proving its effectiveness, and multiple comparative simulations are carried out, demonstrating its superiority. [ABSTRACT FROM AUTHOR]

To promote the widespread use of mobile robots in diverse fields, the performance of trajectory tracking must be ensured. To address the constraints and nonlinear features associated with mobile robot systems, nonlinear model predictive control (MPC) is applied to realize trajectory tracking of mobile robots. Specifically, to alleviate the online computational complexity of nonlinear MPC, this paper devises a lattice piecewise affine (PWA) approximation method that can approximate both the nonlinear system and control law of explicit nonlinear MPC. The kinematic model of the mobile robot is successively linearized along the trajectory to obtain a linear time‐varying description of the system, which is then expressed using a lattice PWA model. Subsequently, the nonlinear MPC problem can be transformed into a series of linear MPC problems. Furthermore, to reduce the complexity of online calculation of multiple linear MPC problems, the optimal solution of the linear MPCs is approximated by using the lattice PWA models. That is, for different sampling states, the optimal control inputs are obtained offline, and lattice PWA approximations are constructed for the state control pairs. Simulations are performed to evaluate the performance of the method in comparison with the state‐of‐the‐art methods, and the results show that the method has a higher online computing speed and can decrease the offline computing time without significantly increasing the tracking error. [ABSTRACT FROM AUTHOR]

Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies. [ABSTRACT FROM AUTHOR]

This paper studies the active fault tolerant load frequency control of multi‐area power systems with electric vehicles under deception attacks. An integrated design of fault estimation and fault‐tolerant control is proposed to guarantee the stability of the system under sensor faults and deception attacks. Considering the uncertainty caused by the demand of the owner and the state of the battery, a multi‐area power system model is proposed. Then, an active fault tolerant load frequency control scheme is designed. The proportional‐derivative sliding mode observer is used to estimate the fault and system status in real‐time. During the fault occurrence, the estimated value obtained by the observer is utilized to design the controller without any fault diagnosis scheme, which simplifies the controller design process. A sufficient Lyapunov‐Krasovskii criterion is derived to ensure the stability performance of the multi‐area power system. Finally, simulation examples are provided for a three‐area power system contains electric vehicles, and the results prove the correctness and feasibility of the proposed fault‐tolerant control scheme. [ABSTRACT FROM AUTHOR]

The stability control of suspension density is an important means to ensure the quality of fine coal in dense medium separation (DMS) process. This paper focuses on the tracking control issue of the dense medium suspension density in a non‐uniform sampling and networked control environment, and proposes a multi‐rate event trigger control design method. Firstly, lifting technique and multi‐rate identification method are adopted to establish the multi‐rate model. Secondly, a multi‐rate event‐triggered predictive control (MEPC) algorithm is proposed to reduce the waste of communication resources and ensure tracking performance. Furthermore, by using a Kalman filter to eliminate the effects of noise and developing a compensation mechanism to predict the lost data, a robust MEPC (RMEPC) algorithm is further proposed for imperfect data in the actual production process. Finally, the hardware‐in‐the‐loop simulation experiments with actual parameters of coal preparation plant have been carried out, showing the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper concentrates on network congestion and security issue for networked control systems under the hybrid attacks. The hybrid attacks including deception attack, replay attack and denial‐of‐service attack are modelled as Bernoulli random process. A general form adaptive event‐triggered scheme (AETS) under the hybrid attacks is designed to alleviate the network congestion and save communication resources utilizing adaptive threshold and the weighted average of data packets. Meanwhile, the security issue under the hybrid attacks is addressed by a dynamic output feedback controller (DOFC) based on the AETS. Moreover, the sufficient conditions are obtained by a piecewise Lyapunov function to guarantee that the closed‐loop system is exponentially mean‐square stable. A practical experiment on networked motor control system verifies the effectiveness of the proposed scheme. The proposed scheme can not only save communication resources to further alleviate network congestion, but also defense the hybrid attacks in the network. [ABSTRACT FROM AUTHOR]

This paper deals with the optimal control problem for partially observed leader–follower stochastic differential game. By virtue of the classical variational method and Girsanov's theorem, the stochastic maximum principles for the follower under one type of partially observed case and for the leader under the complete information structure are derived. As applications, two partially observed cases are considered for the linear–quadratic models. Then by the stochastic filtering technique, the optimal feedback controls for the follower and the leader are represented by the new stochastic Riccati equations. [ABSTRACT FROM AUTHOR]

Here, a gradient‐based neural network (GNN) model is constructed for solving the discrete periodic Lyapunov matrix equation (DPLME) associated with discrete‐time linear periodic systems. In practical applications, the recurrent neural network model should not only converge rapidly, but also be able to tolerate noise. However, the influence of noise on GNN models was seldom considered in the past. In order to improve the convergence and robustness of the GNN model, a novel type of non‐linear activation function is applied to the GNN model. Compared with the traditional activation functions, the activation function used here makes the GNN model to achieve fixed‐time convergence. Besides, when disturbed by bounded noise, the unique positive definite solution of the DPLME can still be obtained by using the GNN model. Finally, simulation experiment is performed to verify the effectiveness and superiority of the proposed GNN model. [ABSTRACT FROM AUTHOR]

This paper analyzes classical and quantum physical systems from an optimal control perspective. Specifically, we explore whether their associated dynamics can correspond to an open- or closed-loop feedback evolution of a control problem. Firstly, for the classical regime, when it is viewed in terms of the theory of canonical transformations, we find that a closed-loop feedback problem can describe it. Secondly, for a quantum physical system, if one realizes that the Heisenberg commutation relations themselves can be considered constraints in a non-commutative space, then the momentum must depend on the position of any generic wave function. That implies the existence of a closed-loop strategy for the quantum case. Thus, closed-loop feedback is a natural phenomenon in the physical world. By way of completeness, we briefly review control theory and the classical mechanics of constrained systems and analyze some examples at the classical and quantum levels. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to develop a unified theoretical framework that presents a cohesive picture of workplace cyberbullying to better understand the interplay between cyberbullying, its effects on organizations and organizational controls enacted to contain these effects. Design/methodology/approach: The study conducts a theoretical review of the workplace cyberbullying literature between 2005 and 2021 drawing upon existing literature and two important theories, the routine activities theory and control theory. The final sample of 54 empirical papers represents a comprehensive body of literature on cyberbullying published across various disciplines. Findings: A theoretical model of workplace cyberbullying is developed, which highlights major antecedents to workplace cyberbullying and its impact on individual employees as well as organizations. Originality/value: As firms increasingly rely on information and communication technologies (ICTs), the misuse of ICTs in the form of cyberbullying is also increasing. Workplace cyberbullying severely hurts an organization's employees and compromises the efficacy of its information systems. Fortunately, various controls can be utilized by firms to minimize workplace cyberbullying and its attendant costs. In all, eleven propositions are offered, providing a robust agenda for future research. The authors also offer insights for practitioners on how to minimize cyberbullying in the workplace and its damaging effects. [ABSTRACT FROM AUTHOR]

Mounting evidence suggests that plants engage complex computational processes to quantify and integrate sensory information over time, enabling remarkable adaptive growth strategies. However, quantitative understanding of these computational processes is limited. We report experiments probing the dependence of gravitropic responses of wheat coleoptiles on previous stimuli. First, building on a mathematical model that identifies this dependence as a form of memory, or a filter, we use experimental observations to reveal the mathematical principles of how coleoptiles integrate multiple stimuli over time. Next, we perform two-stimulus experiments, informed by model predictions, to reveal fundamental computational processes. We quantitatively show that coleoptiles respond not only to sums but also to differences between stimuli over different timescales, constituting evidence that plants can compare stimuli--crucial for search and regulation processes. These timescales also coincide with oscillations observed in gravitropic responses of wheat coleoptiles, suggesting shoots may combine memory and movement in order to enhance posture control and sensing capabilities. [ABSTRACT FROM AUTHOR]

Recent (scale-free) quantitative unique continuation estimates for spectral subspaces of Schrödinger operators are extended to allow singular potentials such as certain L p -functions. The proof is based on accordingly adapted Carleman estimates. Applications include Wegner and initial length scale estimates for random Schrödinger operators and control theory for the controlled heat equation with singular heat generation term. [ABSTRACT FROM AUTHOR]

This paper proposes a simplified and analytical method for designing fractional order lead compensator (FOLC). In the proposed method, the structure of fractional order lead compensator considered is more generalized. The fractional operator of the FOLC is approximated to its equivalent rational form using binomial expansion to lower the computation complexity of the designing approach. Then, steady-state gain, gain cross over frequency and phase margin are used as the design specifications of the FOLC. The proposed method also considers the magnitude and phase of the plant to be controlled. Phase margin criteria ensure the stability of the designed compensator. The compensator is designed strictly adhering to gain and phase margin criteria. The proposed methodology is validated with four different illustrative examples through simulation. Integral absolute error (IAE) and integral square error (ISE) are considered as the performance indices. The simulation results demonstrate that the proposed scheme gives a better transient response, IAE, and ISE as compared to the existing FOLC design techniques. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive review of the Active Simultaneous Localization and Mapping (A-SLAM) research conducted over the past decade. It explores the formulation, applications, and methodologies employed in A-SLAM, particularly in trajectory generation and control-action selection, drawing on concepts from Information Theory (IT) and the Theory of Optimal Experimental Design (TOED). This review includes both qualitative and quantitative analyses of various approaches, deployment scenarios, configurations, path-planning methods, and utility functions within A-SLAM research. Furthermore, this article introduces a novel analysis of Active Collaborative SLAM (AC-SLAM), focusing on collaborative aspects within SLAM systems. It includes a thorough examination of collaborative parameters and approaches, supported by both qualitative and statistical assessments. This study also identifies limitations in the existing literature and suggests potential avenues for future research. This survey serves as a valuable resource for researchers seeking insights into A-SLAM methods and techniques, offering a current overview of A-SLAM formulation. [ABSTRACT FROM AUTHOR]

Cooperative control of high‐speed trains can improve the safety and efficiency of high‐speed railway transportation. However, the velocity and distance overshoots in the cooperative control of multiple high‐speed trains is an undesirable behavior, which will lead to unsafe and inefficient train operation. To address this issue, a cooperative predictive set point modulation control approach for high‐speed trains is proposed. First, the cyber‐physical modeling of multiple high‐speed trains is presented. The longitudinal dynamic characteristics of high‐speed trains are established in the physical layer, and the cyber layer represents the communication topology of high‐speed trains based on graph theory. Second, a cooperative control rule is designed to achieve the velocity consensus and maintain safe distance between adjacent trains. Moreover, the set point automatic adjustment with correction enable (SPAACE) is leveraged to smoothly adjust the set‐point of high‐speed trains to suppress the overshoots of train velocities and inter‐train distance. Finally, the performance of the proposed method is verified in three different simulation scenarios: normal, acceleration and deceleration conditions. The simulation results show that the proposed method reduces the maximum relative displacement by 62% and the velocity error by 34%. [ABSTRACT FROM AUTHOR]

Random jump of network communication topology challenges the tracking accuracy of linear switched reluctance motor (LSRM) system. This paper studies the pilot‐following consensus control of the multiple direct‐drive motion system (MDDMS) with semi‐Markov jump topology. The mathematical model of LSRM is constructed and the multi‐phase excitation strategy is studied. The directed graph is used to model the information transmission among the leaders and follower, and a new mechanism consensus based on previous states is provided. A consensus system based on semi‐Markov jump topology is constructed and the problem of consensus is shifted into a problem of stability analysis. A sufficient condition for consensus problem is obtained by using semi‐Markov switching system theory and graph theory. The MDDMS achieves consensus in the form of exponential convergence under the designed consensus controller. Several groups of simulation results verify the effectiveness of the proposed consensus control method. [ABSTRACT FROM AUTHOR]

I apply a recently emerging perspective on the complexity of action selection, the rate-distortion theory of control, to provide a computational-level model of errors and difficulties in human language production, which is grounded in information theory and control theory. Language production is cast as the sequential selection of actions to achieve a communicative goal subject to a capacity constraint on cognitive control. In a series of calculations, simulations, corpus analyses, and comparisons to experimental data, I show that the model directly predicts some of the major known qualitative and quantitative phenomena in language production, including semantic interference and predictability effects in word choice; accessibility-based ("easy-first") production preferences in word order alternations; and the existence and distribution of disfluencies including filled pauses, corrections, and false starts. I connect the rate-distortion view to existing models of human language production, to probabilistic models of semantics and pragmatics, and to proposals for controlled language generation in the machine learning and reinforcement learning literature. [ABSTRACT FROM AUTHOR]

The milestones of the history of the scientific school on cybernetics (the School), established in 1959 by outstanding scientist V.A. Yakubovich at Leningrad State University (LSU), are presented. The connections of the School with other Russian and foreign scientific schools in related fields are outlined. [ABSTRACT FROM AUTHOR]

Controlling the dynamics of chaotic systems is a task which is often addressed in an empirical way, particularly for placing sensors and actuators. Here, we show that selecting the measured variable and placing the actuator can be guided by considering the observability and controllability symbolic coefficients and applying the notion of flatness. This approach is here demonstrated on the piecewise linear Chua circuit, whose specific features are leveraged in constructing a discretized controller with a switch mechanism and optimally placed sensor and actuator. The feedback linearization is compared to a homogeneous and a passivity-based control laws, the flat control laws being more efficient than the others. It is thus shown that the proposed flat control law by feedback linearization is very efficient. The continuous time and discretized Chua circuit, governed by differential and difference equations, respectively, are treated. Most likely, these results could be extendable to a large group of natural and experimental systems. [ABSTRACT FROM AUTHOR]

Version 5.99 of the empirical Gramian framework – emgr – completes a development cycle which focused on parametric model order reduction of gas network models while preserving compatibility to the previous development for the application of combined state and parameter reduction for neuroscience network models. Second, new features concerning empirical Gramian types, perturbation design, and trajectory post-processing, as well as a Python version in addition to the default MATLAB / Octave implementation, have been added. This work summarizes these changes, particularly since emgr version 5.4, see Himpe, 2018 [Algorithms 11(7): 91], and gives recent as well as future applications, such as parameter identification in systems biology, based on the current feature set. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive dynamic programming discrete‐time linear quadratic Riccati (ADP‐DTLQR) control is designed for an electromagnetic levitation system (EMS) to track the reference trajectories and preserve stability in severe situations efficiently. The EMS system has a strongly nonlinear and unstable dynamic in nature. The ADP‐DTLQR approach is a novel method based on the Q‐learning algorithm for unknown discrete‐time systems in a causal manner. The ADP‐DTLQR can determine the online gains of the controller in different conditions as a model‐free structure, which can minimize the combination of state errors and control efforts of the systems. Moreover, the DTLQR controller and linear matrix inequality controller on the EMS system are designed and compared with the present strategy in different scenarios in the simulation section to verify the robustness and strongness of the proposed method. The evaluation of simulation results demonstrates that the proposed control scheme is suitable not only for preserving the levitated object stability but also for compensating the disturbances and uncertainties in the EMS structure. [ABSTRACT FROM AUTHOR]

This paper develops a new approach to small time local attainability of smooth manifolds of any dimension, possibly with boundary and to prove Hölder continuity of the minimum time function. We give explicit pointwise conditions of any order by using higher order hamiltonians which combine derivatives of the controlled vector field and the functions that locally define the target. For the controllability of a point our sufficient conditions extend some classically known results for symmetric or control affine systems, using the Lie algebra instead, but for targets of higher dimension our approach and results are new. We find our sufficient higher order conditions explicit and easy to compute for targets with curvature and general control systems. Some cases of nonsmooth targets are also included. [ABSTRACT FROM AUTHOR]

This paper investigates the proportional‐integral‐derivative (PID) control of positive systems in the discrete‐time case. First, a tuning parameter is introduced to construct the proportional‐integral‐derivative control framework of positive systems. The proportional‐integral‐derivative control problem is thus transformed into the control synthesis of positive systems with time‐delay. By virtue of a matrix decomposition approach, the gain matrices of proportional‐integral‐derivative controller are designed. Using co‐positive Lyapunov functions and linear programming, the positivity and stability of positive systems are achieved under the designed proportional‐integral‐derivative controller. Then, the proportional‐integral‐derivative control of positive systems with reference signal is solved. The main contribution of this paper lies in that linear programming associated to the matrix decomposition approach is first presented to handle the proportional‐integral‐derivative control of positive systems. Finally, two illustrative examples are provided to verify the effectiveness of the proposed design. [ABSTRACT FROM AUTHOR]

We investigate the structure of discontinuities in clearance (or minimum time) functions for nonlinear control systems with general, closed obstacles (or targets). We establish general results regarding interactions between admissible trajectories and clearance discontinuities, e.g., instantaneous increases in clearance when passing through a discontinuity, and propagation of discontinuity along optimal trajectories. Then, investigating sufficient conditions for discontinuities, we explore a common directionality condition for velocities at a point, characterized by strict positivity of the minimal Hamiltonian. Elementary consequences of this common directionality assumption are explored before demonstrating how, in concert with corresponding obstacle configurations, it gives rise to clearance discontinuities both on the surface of the obstacle and propagating out into free space. Minimal assumptions are made on the topological structure of obstacle sets. [ABSTRACT FROM AUTHOR]

In this paper, a new integral‐type transformation is being introduced, which is the generalization of Laplace and Fourier transform. This integral transform is named as "Fareeha transform." It is successfully applied on ordinary differential equation to show its efficacy and simplicity. Also, the possible applications of Fareeha transform in control theory, electric circuits, and data compression have been discussed in detail. [ABSTRACT FROM AUTHOR]

This paper presents a self‐triggered control approach with the view of risks for discrete‐time linear stochastic systems. More specifically, under the assumption that the first two moments of the disturbance distribution are known, the paper considers the problem of designing a control law that reduces the use of communication and power resources by aperiodically sampling the states and updating control inputs, while achieving a given performance goal at a risk below a specified level. This problem is approached by using the worst‐case Conditional Value‐at‐Risk as a measure of risk, to quantify the tail behavior of the stochastic systems, thus allowing us to suppress large losses. A numerical example is provided to illustrate the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

The purpose of this work is to provide a novel approach for studying the chaos control of hybrid nanoparticles in a porous medium under feedback control and quasiperiodic gravity sinusoidal modulation. We use the powerful tool of Fourier modes to convert the governing flow model PDEs into ordinary ones. Under the combined effect, the properties of hybrid nanofluids a silver–magnesium oxide/water can be used to suppress heat transfer in a variety of industrial applications, i.e., cooling nuclear components. Numerical data comparison with the results presented in the previous literature shows a significant agreement with the present study. On the basis of nonlinear control theory and chaotic dynamics, the equilibrium points of the system are analyzed along with the local stability. As part of the article, different aspects of hybrid nanofluids (Ag–MgO/ H 2 O ) stability are discussed, starting from the preliminary stages to the practical application of hybrid nanofluids. It has been specifically focused on preventing nonlinear conditions from being aggravated by hybrid nanofluids. A pitchfork and inverted bifurcation can occur at various control gain and frequency or amplitude parameters. As a consequence of chaos, the paper proposes the implications of feedback control combined with quasi-gravity modulation to effectively control the chaotic system. [ABSTRACT FROM AUTHOR]

The Lagrange–Dirac interconnection theory has been developed for primitive subsystems coupled by a standard interaction Dirac structure, i.e. a structure of the form D int = Σ int ⊕ Σ int ∘ , where Σ int ⊂ T (T * Q) is a regular distribution, Σ int ∘ ⊂ T * (T * Q) is its annihilator and Q is the configuration manifold of the theory. In this work, we extend this theory to allow for parameter-dependent subsystems coupled by nonstandard interaction Dirac structures. This is done, first, by using the Dirac tensor product and, then, by using interaction forces. Both approaches are shown to be equivalent, and also equivalent to a variational principle. After that, we demonstrate the relevance of this generalization by investigating three applications. First, an electromechanical system is modeled; namely, a piston driven by an ideal DC motor through a scotch-yoke mechanism. Second, we relate the interconnection theory to the Euler–Poincaré–Suslov reduction. More specifically, we show that the reduced system may be regarded as an interconnected Lagrange–Dirac system with parameters. The nonholonomic Euler top is presented as a particular instance of this situation. Lastly, control interconnected systems are defined and a control for a planar rigid body with wheels is designed. [ABSTRACT FROM AUTHOR]

This paper considers the regional boundary observability problem for semilinear time-fractional systems. The main objective is to reconstruct the initial state on a subregion of the boundary of the evolution domain of the considered fractional system using the output equation. We proceed by providing a link between the regional boundary observability of the considered semilinear system on the desired boundary subregion, and the regional observability of its linear part, in a well chosen subregion of the evolution domain. By adding some assumptions on the nonlinear term appearing in the considered system, we give the main theorem that allows us to reconstruct the initial state in the well-chosen subregion using the Hilbert uniqueness method (HUM). From it, we recover the initial state on the boundary subregion. Finally, we provide a numerical example that backs up the theoretical results presented in this paper with a satisfying reconstruction error. [ABSTRACT FROM AUTHOR]

Regarding the low accuracy and instability of common online methods for estimating dynamic models in the time domain, in the presence of uncertainty in system dynamics, sensor noise and environmental disturbances, this area is still open for further research. In this paper, a new estimation method is proposed based on a new online robust meta‐heuristic adaptive LSQR (ORALSQR) for simultaneous estimation of a multi input/output linear dynamic model and system state variables. This new adaptive LSQR algorithm is used to solve the output matrix equations of the least squares error problem. The presented algorithm, based on its iterative nature, searches the answer subspace by using a new meta‐heuristic logic. In addition, the algorithm solving steps and the search domain size in each iteration are intelligently determined by the method. In an identification maneuver, this method estimates the state variables using an estimated model in the Kalman filter, then estimates the model online for the next iteration using the state variables. In addition the stability proof of this method is presented. Numerical results show more accuracy and robustness of this method compared to the other methods mentioned in this paper which contain LS and RLS based estimation methods. [ABSTRACT FROM AUTHOR]

In this manuscript, we consider the problem of regional boundary observability for semilinear time-fractional systems involving the Riemann-Liouville fractional derivative. Our primary goal is to focus on reconstructing the initial state in the desired subregion located on the boundary of the spatial domain. To do that, we firstly construct a link between regional boundary observability of the considered semilinear system and regional observability of its linear part. And with the help of an extension of the Hilbert uniqueness method (HUM), we recover the value of the initial state on the desired boundary subregion. We also provide a numerical simulation based on the steps of the HUM approach that shows the proposed algorithm's efficiency and backs up our theoretical results. [ABSTRACT FROM AUTHOR]

The introduction of Laplace transform theory into the principles of automatic control allows complex differential equations to be transformed into algebraic equations after modelling the system, which plays an important role in analyzing stability and system correction. The researchers use a ball-and-stick system for modelling, combine the properties of the Laplace transform, and derive its corresponding algebraic equations. The stability of the system is analyzed in three ways: time domain, root trajectory and frequency domain. Next, the unstable system is corrected to achieve the desired effect, and tested for stability with multiple signals in the Simulink module of Matlab. [ABSTRACT FROM AUTHOR]

Multirate methods have been used for decades to temporally evolve initial-value problems in which different components evolve on distinct time scales, and thus use of different step sizes for these components can result in increased computational efficiency. Generally, such methods select these different step sizes based on experimentation or stability considerations. For problems that evolve on a single time scale, adaptivity approaches that strive to control local temporal error are widely used to achieve numerical results of a desired accuracy with minimal computational effort, while alleviating the need for manual experimentation with different time step sizes. However, there is a notable gap in the publication record on the development of adaptive time step controllers for multirate methods. In this paper, we extend the single-rate controller work of Gustafsson [ACM Trans. Math. Software, 20 (1994), pp. 496--517] to the multirate method setting. Specifically, we develop controllers based on polynomial approximations to the principal error functions for both the "fast" and "slow" time scales within multirate infinitesimal (MRI) methods. We additionally investigate a variety of approaches for estimating the errors arising from each time scale within MRI methods. We then numerically evaluate the proposed multirate controllers and error estimation strategies on a range of multirate test problems, comparing their performance against an estimated optimal performance. Through this work, we combine the most performant of these approaches to arrive at a set of multirate adaptive time step controllers that robustly achieve desired solution accuracy with minimal computational effort. [ABSTRACT FROM AUTHOR]

As respondents assess the cultural normalcy of social events, they employ both affective and cognitive criteria. Does this event feel normal? Does this event make sense? While these related questions often have the same answer, we know little about the assessment process under circumstances of signal mismatch. Using qualitative and quantitative data from two experimental studies, this research separately evaluates the effects of deflection level (is this event affectively normative) and institutional concordance (do the components of this event obey the guiding parameters of social institutions) in the assessment of social events. Online-administered surveys gathered data for a 3-condition experiment in an undergraduate sample (N = 74) and a 4-condition experiment in a non-undergraduate quasi-nationally representative sample (N = 507). Results from linear mixed models and ANOVAs show that (1) both concordance and low deflection are significant predictors of event assessment ratings, (2) when controlling for concordance, event deflection level remains a statistically significant predictor, and (3) deflection and concordance have a significant and positive interaction effect. Qualitative data patterns and a visualization of predicted probabilities from a multinomial logit model further suggest that (4) cognitive work in respondents' assessments—transforming high-deflection events into low-deflection events through contextualized reinterpretations in accord with institutional domain parameters—follow affect control theory principles. This research strengthens understanding of and predictive abilities concerning social responses to culturally contextualized events. [ABSTRACT FROM AUTHOR]

The brain is a system that performs numerous functions by controlling its states. Quantifying the cost of this control is essential as it reveals how the brain can be controlled based on the minimization of the control cost, and which brain regions are most important to the optimal control of transitions. Despite its great potential, the current control paradigm in neuroscience uses a deterministic framework and is therefore unable to consider stochasticity, severely limiting its application to neural data. Here, to resolve this limitation, we propose a novel framework for the evaluation of control costs based on a linear stochastic model. Following our previous work, we quantified the optimal control cost as the minimal Kullback-Leibler divergence between the uncontrolled and controlled processes. In the linear model, we established an analytical expression for minimal cost and showed that we can decompose it into the cost for controlling the mean and covariance of brain activity. To evaluate the utility of our novel framework, we examined the significant brain regions in the optimal control of transitions from the resting state to seven cognitive task states in human whole-brain imaging data of either sex. We found that, in realizing the different transitions, the lower visual areas commonly played a significant role in controlling the means, while the posterior cingulate cortex commonly played a significant role in controlling the covariances. [ABSTRACT FROM AUTHOR]

The article discusses an approach aimed at the formation of a methodology for applying the principles and models used in the automatic control theory in relation to the processes of enrichment of mineral raw materials. The problem solved in the article is posed as follows, try to represent the process of gravitational enrichment, namely centrifugal concentration, in the form of a transfer function and try to find its parameters. The paper proposes an approach to modeling centrifugal concentrators based on transfer functions, using averaged values of output characteristics. Proposal of a mechanism for determining the parameters of transfer functions and their calculation based on experimental data. To check the presented calculations, the MatLAB software tool with the Simulink package was used. As a result, the transfer function of the KC-CVD6 apparatus used in the gold deposit was found and satisfactory results were obtained. The work uses methods of identification of automatic systems, methods of system analysis, as well as methods of mathematical modeling, namely the numerical solution of differential equations, implemented in the MatLAB environment with the Simulink package. A comparison with the classical regression model was made; as a result, the modeling error was reduced by two times. The simulation error for the concentrate yield was less than 1%. [ABSTRACT FROM AUTHOR]

The development of quantum technologies present important challenges such as the need for fast and precise protocols for implementing quantum operations. Shortcuts to adiabaticity (STAs) are a powerful tool for achieving these goals, as they enable us to perform an exactly adiabatic evolution in finite time. In this paper, we present a shortcut to adiabaticity for the control of an optomechanical cavity with two moving mirrors. Given reference trajectories for the mirrors, we find analytical expressions that give us effective trajectories which implement an STA for the quantum field inside the cavity. We then solve these equations numerically for different reference protocols, such as expansions, contractions and rigid motions, thus confirming the successful implementation of the STA and finding some general features of these effective trajectories. [ABSTRACT FROM AUTHOR]

This paper describes a general-purpose control standard and demonstrates its implementation as part of a controller-effector assembly. It elaborates on the specific layers on which the standard is defined: an adaptive control structure, a data transmission protocol and its corresponding instruction set. The aforementioned effector component consists of a reduced form factor robotics platform capable of remote controlled movement and optical inspection. [ABSTRACT FROM AUTHOR]

The increasing access to inexpensive sensors, computing power, and more accurate forecasting of storm events provides unique opportunities to shift flood management practices from static approaches to an optimization-based real-time control (RTC) of urban drainage systems. Recent studies have addressed a plethora of strategies for flood control in stormwater reservoirs; however, advanced control theoretic techniques are not yet fully investigated and applied to these systems. In addition, there is an absence of a coupled integrated control model for systems composed of watersheds, reservoirs, and channels for flood mitigation. To this end, we developed a novel nonlinear state-space model of hydrologic and hydrodynamic processes in watersheds, reservoirs, and one-dimensional channels. The model was tested under different types of reservoir control strategies based on real-time measurements (reactive control) and predictions of the future behavior of the system (predictive control) using rainfall forecastings. We applied the modeling approach in a system composed of a single watershed, reservoir, and channel connected in series for the observed rainfall data in San Antonio. Results indicate that for flood mitigation, the predictive control strategy outperforms the reactive controls not only when applied for synthetic design storm events, but also for a continuous simulation. Moreover, the predictive control strategy requires smaller valve operations while still guaranteeing efficient hydrological performance. From the results, we recommend the use of the nonlinear model predictive control strategy to control stormwater systems because of the ability to handle different objective functions, which can be altered according to rainfall forecasting and shift the reservoir operation from flood-based control to strategies focused on increasing detention times, depending on the forecasting. [ABSTRACT FROM AUTHOR]

This review of the state of the art aims to collect the description and main research results in the field of development and validation of control algorithms with the main purpose to solve the problem of cogging torque and main sources of electromagnetic torque ripple. In particular, we focus on electric drives for advanced and modern mechatronic applications such as industrial automation, robotics, and automotive applications, with special emphasis on work that exploits model-based design. A great added value of this paper is to explicitly show the operational steps required for the model-based design design of optimized control algorithms for electric drives where it is necessary to make up for electromagnetic torque oscillations due to the main sources of ripple, particularly cogging torque. The ultimate goal of this paper is to provide researchers approaching this particular problem with a comprehensive collection of the most effective solutions reported in the state of the art and also a summary for effectively applying the model-based design methodology. [ABSTRACT FROM AUTHOR]

We study dynamics of a bimodal planar linear switched system with a Hurwitz stable and an unstable subsystem. For given flee time from the unstable subsystem, the goal is to find corresponding dwell time in the Hurwitz stable subsystem so that the switched system is asymptotically stable. The dwell-flee relations obtained are in terms of certain smooth functions of the eigenvalues and (generalised) eigenvectors of the subsystem matrices. The results are extended to a special class of symmetric bilinear systems. The results are also extended to a multimodal planar linear switched system in which the switching is governed by an undirected star graph, where the internal node corresponds to a Hurwitz stable (unstable, respectively) subsystem and all the leaves correspond to unstable (Hurwitz stable, respectively) subsystems. For this multimodal system, dwell-flee relations are obtained as solutions of a minimax optimisation problem. [ABSTRACT FROM AUTHOR]

Although research suggests a positive association between adolescent residential mobility and offending, the mechanism by which this process unfolds is less understood. One commonly cited explanation is that moving severs ties to pro-social others, reducing perceptions of informal social costs that then leads to offending. We test this mechanism with data from the Pathways to Desistance Study, which is a longitudinal study of serious offending youth. We integrate monthly-level residential mobility information from the access-restricted Life Event Calendar data with the publicly available data comprised of informal social costs and offending. We estimate fixed effects models with bootstrapping techniques to produce point estimates of the indirect effect. Our results provide insight into a prominent mechanism by which mobility affects offending. [ABSTRACT FROM AUTHOR]