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AbstractPersonalized 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 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.

This manuscript explores a new class of Hilfer fractional stochastic differential system, as driven by the Wiener process and Rosenblatt process through the application of non-instantaneous impulsive effects and Poisson jumps. Existence of a mild solution to the considered system is proved. Sufficient conditions for the controllability of the proposed control system are established. To prove our main results, we utilize fractional calculus, stochastic analysis, semigroup theory, and the Sadovskii fixed point theorem. In addition, to illustrate the theoretical findings, we present an example.

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]

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]

Abstract 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.

AbstractIn this paper, we introduce a ground-breaking approach to defining fractional calculus for a selected class of analytic functions. Our new definitions, based on a novel and intuitive understanding of fractional derivatives and integrals, offer improved mathematical tractability for a variety of applications, including physics, engineering and finance. Our approach significantly simplifies the complexity of mathematical functions compared to the traditional Riemann-Liouville approach, by using simple functions rather than special functions, while preserving the intrinsic sense of fractional calculus. This article not only presents our proposed definitions but also provides a thorough analysis of their properties and advantages. The conclusion of this paper discusses the potential for future research in the field of fractional calculus.

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.

The analysis and control of stability in high-bandwidth systems characterized by non-minimum phase delays represent a formidable challenge within the realm of control theory and engineering. This research aims to address the pivotal question of whether it is feasible to enhance the stability of such intricate systems. These systems inherently possess uncertain and swiftly changing delay characteristics, rendering them exceptionally demanding to control effectively. In the course of this investigation, we embark on a comprehensive exploration of the theoretical underpinnings of the stability of high-bandwidth, non-minimum phase delay systems. This encompassing inquiry encompasses a meticulous consideration of both derivative-delay and piecewise continuous delay components. To underpin our analysis, we judiciously incorporate feedback mechanisms, drawing upon mathematical tools such as the Jensen inequality and Lyapunov-based methodologies to rigorously establish stability conditions. Furthermore, our exploration extends to encompass the concept of input-output stability and complements it with the notion of asymptotic stability, thereby ensuring that the systems in question exhibit uniform stability across diverse temporal domains. The outcomes of our investigation furnish compelling evidence that by harnessing the power of discrete-time Lyapunov-Krasovskii functionals, it becomes conceivable to circumscribe the maximum delay within predefined thresholds. This achievement holds the promise of enhancing stability in non-minimum phase delay systems characterized by high bandwidth. These findings have far-reaching implications, profoundly influencing the design and control paradigms across a spectrum of engineering applications. Notably, this impact extends to areas such as communication networks, real-time control systems, and robotics, where the mitigation of instability due to non-minimum phase delays has been an enduring challenge.

Minerals and nutrients are vital to life, but their excess can harm health and sometimes lead to life risks. Harmful impact of nutrients in water can be a threat to marine life as well. Waquoit Bay linked with its eight rivers and ponds is becoming the reason for excess nutrient passage into the Bay. Excess amounts of nutrients were found in Waquoit Bay from coastal areas and therefore analyzed in this work by a system of nine differential equations. The main objective of this paper is to develop a mathematical model to assess the situation of pollution in Waquoit Bay and suggest some solutions using optimal control theory with Hamiltonian system. Mathematical Model of water pollution will be evaluated numerically, with different input models to track environmental contamination in a water body. To prove the accuracy of this model, its numerical results will be compared with real data and the impact of control strategies i.e. nutrient control from housing area and bio dredging will be studied. Benefit of developing such mathematical models can be a decision support for planning restrictions in coastal areas near Waquoit Bay.

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]

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]

The dynamics of fluid-conveying pipelines with different shapes has received extensive research attention. Significant wall shear stress and flow separation occur when the fluid flows through pipelines with various curvatures. These phenomena trigger pipeline vibration, the generation of mechanical and hydrodynamic noise, damage, and even the rupture of the pipeline. However, previous studies have not considered the mechanism of internal pipeline flow to eliminate flow separation and the generation of secondary flow inside bent pipelines by redirecting and manipulating the flow. To steer the fluid flow, a 'hydrodynamic transformation strategy' based on the metamaterial technology is proposed for the first time in this work; through this strategy, the fluid in pipelines can be made to flow along trajectories that are always parallel to the central axis of the bent pipelines. Interestingly, this innovative method can effectively eliminate the elbow-induced secondary flow and prevent the generation of a pressure gradient toward the pipeline wall. Using the soft lithography technology or the three-dimensional printing technology, the hydrodynamic metamaterial microstructure required to manipulate the fluid flow path in actual engineering applications can be achieved. Our work paves the way for developing new approaches for controlling the flow characteristics and reducing the turbulence intensity of the fluid flowing in pipelines with elbows and corners. [ABSTRACT FROM AUTHOR]

To optimize flapping foil performance, in the current study we apply deep reinforcement learning (DRL) to plan foil non-parametric motion, as the traditional control techniques and simplified motions cannot fully model nonlinear, unsteady and high-dimensional foil–vortex interactions. Therefore, a DRL training framework is proposed based on the proximal policy optimization algorithm and the transformer architecture, where the policy is initialized from the sinusoidal expert display. We first demonstrate the effectiveness of the proposed DRL-training framework, learning the coherent foil flapping motion to generate thrust. Furthermore, by adjusting reward functions and action thresholds, DRL-optimized foil trajectories can gain significant enhancement in both thrust and efficiency compared with the sinusoidal motion. Last, through visualization of wake morphology and instantaneous pressure distributions, it is found that DRL-optimized foil can adaptively adjust the phases between motion and shedding vortices to improve hydrodynamic performance. Our results give a hint of how to solve complex fluid manipulation problems using the DRL method. [ABSTRACT FROM AUTHOR]

We explore the effects of Clausewitzian fog and friction using a data rate theorem–based model of the phase transition from control to failure for inherently unstable systems that include, but are not limited to, the many possible modalities of organized conflict. Fog-and-friction challenge any and all cognitive structures facing dynamic patterns of threat or opportunity, whether control is manifested through an institution, a machine entity, or some composite. The fundamental nature of challenge appears independent of the degree of sophistication of those institutions, entities, or composites, and of the technical modalities employed. The dialog/Zweikampf of organized conflict is—and will remain—an intimate and most human enterprise. Implications for other existential threats of inherently unstable circumstance, like pandemic disease or climate change, are evident. [ABSTRACT FROM AUTHOR]

This manuscript explores a new class of Hilfer fractional stochastic differential system, as driven by the Wiener process and Rosenblatt process through the application of non-instantaneous impulsive effects and Poisson jumps. Existence of a mild solution to the considered system is proved. Sufficient conditions for the controllability of the proposed control system are established. To prove our main results, we utilize fractional calculus, stochastic analysis, semigroup theory, and the Sadovskii fixed point theorem. In addition, to illustrate the theoretical findings, we present an example. [ABSTRACT FROM AUTHOR]

Applications that are constrained by low-latency requirements can hardly be executed on cloud infrastructures, given the high network delay required to reach remote servers. Multi-access Edge Computing (MEC) is the reference architecture for executing applications on nodes that are located close to users (i.e., at the edge of the network). This way, the network overhead is reduced but new challenges emerge. The resources available on edge nodes are limited, workloads fluctuate since users can rapidly change location, and complex tasks are becoming widespread (e.g., machine learning inference). To address these issues, this article presents NEPTUNE, a serverless-based framework that automates the management of large-scale MEC infrastructures. In particular, NEPTUNE provides (i) the placement of serverless functions on MEC nodes according to users' location, (ii) the resolution of resource contention scenarios by avoiding that single nodes be saturated, and (iii) the dynamic allocation of CPUs and GPUs to meet foreseen execution times. To assess NEPTUNE, we built a prototype based on K3S, an edge-dedicated version of Kubernetes, and executed a comprehensive set of experiments. Results show that NEPTUNE obtains a significant reduction in terms of response time, network overhead, and resource consumption compared with five state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

This paper considers the problem of a deputy spacecraft constrained to remain a fixed distance from another spacecraft. The relative dynamics of the deputy spacecraft are derived from a velocity-dependent potential function. A constant of motion is presented that aids in the development of multiple Lyapunov functions and stabilizing control laws. Through both numerical and analytical methods, estimates of the region of attraction are presented for each of the control laws. These regions of attraction serve as domains on which it is conclusively known that the equilibrium points are stabilizable. Lastly, a control law with a spatially maximal region of attraction is presented that can be used to track a time-varying trajectory. [ABSTRACT FROM AUTHOR]

Stability theory has significant applications in technology, especially in control systems. On the other hand, the newly defined generalized mean-square stochastic fractional (GMSF) operators are particularly interesting in control theory and systems due to their various controllable parameters. Thus, the combined study of stability theory and GMSF operators becomes crucial. In this research work, we construct a new class of GMSF differential equations and provide a rigorous proof of the existence of their solutions. Furthermore, we investigate the stability of these solutions using the generalized Ulam-Hyers-Rassias stability criterion. Some examples are also provided to demonstrate the effectiveness of the proposed approach in solving fractional differential equations (FDEs) and evaluating their stability. The paper concludes by discussing potential applications of the proposed results in technology and outlining avenues for future research.

Abstract In practice, many physical systems, including physiological ones, can be considered whose input can take only positive quantities. However, most of the conventional control methods do not support the positivity of the main input data to the system. Furthermore, the parameters of these systems, similar to other non‐linear systems, are either not accurately identified or may change over time. Therefore, it is reasonable to design a controller that is robust against system uncertainties. A robust positive‐input control method is proposed for the automatic treatment of targeted anti‐angiogenic therapy implementing a recently published tumour growth model based on experiments conducted on mouse models. The backstepping (BS) approach is applied to design the positive input controller using sensory data of tumour volume as feedback. Unlike previous studies, the proposed controller only requires the measurement of tumour volume and does not require the measurement of inhibitor level. The exponential stability of the controlled system is proved mathematically using the Lyapunov theorem. As a result, the convergence rate of the tumour volume can be controlled, which is an important issue in cancer treatment. Moreover, the robustness of the system against parametric uncertainties is verified mathematically using the Lyapunov theorem. The real‐time simulation results‐based (OPAL‐RT) and comparisons with previous studies confirm the theoretical findings and effectiveness of the proposed method.

Abstract 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 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.

In the field of architectural decoration engineering, construction quality control is the main issue, which has a significant impact on project safety, cost and schedule. Under the background of the increasing maturity of big data technology, its application in the quality control of architectural decoration engineering has become a new trend in the industry, which is expected to improve quality management through technological innovation. This study analyzes the construction quality control measures of architectural decoration projects using big data technology to improve the efficiency and effectiveness of project quality management. The study utilizes PDCA and SDCA quality control theories, BIM technology, and extensive data analysis to fully monitor and assess the construction process of building decoration projects. The construction quality control mode using big data technology performs significantly better in improving project quality than traditional mode. The project’s second phase adopts the new model, and its construction quality score is higher than that of the first phase by more than 5 points on average, and the frequency of quality problems is reduced by 23%. A multi-layer fuzzy comprehensive evaluation model evaluates the quality control effect, and the construction quality control effect is rated as “excellent” with an affiliation value of 62.1%. The introduction of data technology significantly improves the efficiency and effectiveness of construction quality control in architectural decoration projects, and provides an effective management model for the industry.

This paper presents the utilization of the ESP32 microcontroller as a teaching tool for signal acquisition, processing, and control theory in biotechnological engineering. The ESP32 microcontroller, equipped with Bluetooth and WiFi capabilities, offers an affordable and versatile solution for educational purposes. By leveraging the Arduino© software, students can easily learn microcontroller programming and utilize various peripherals such as sensors and actuators. Several practical exercises related to process control have been conducted using this microcontroller. Additionally, remote process monitoring and control are enabled through integration with a database. Furthermore, concepts of artificial intelligence are explored using the Edge Impulse platform to obtain an artificial neural network that can be downloaded onto the ESP32. Positive feedback from students highlights the effectiveness and engagement of utilizing these microcontrollers, and the integration of internet connectivity enhances the overall learning experience.

This paper starts by presenting the concept of a mobile application. A literature review is conducted, which shows that there is still a certain lack with regard to smartphone applications in the domain of engineering as independent simulation applications and not only as extensions of smartphone tools. The challenges behind this lack are then discussed. Subsequently, three case studies of engineering applications for both smartphones and the internet are presented, alongside their solutions to the challenges presented. The first case study concerns an engineering application for systems control. The second case study focuses on an engineering application for composite materials. The third case study focuses on the finite element method and structure generation. The solutions to the presented challenges are then described through their implementation in the applications. The three case studies show a new system of thought concerning the development of engineering smartphone applications.

In this paper, we explore the approximative controllability of fractional stochastic differential inclusions (SDIs) of Sobolev-type with fractional derivatives in Atangana-Baleanu (AB) sense and Poisson jumps. Our findings are supported by the fixed point theorem, multi-valued map theory, compact semigroup theory and stochastic analysis principles. In the later part, an illustration is provided to clarify the established outcomes.

Abstract 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 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 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.

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]