Control Theory Perspective Research Articles

Control knowledge tracing: Modeling students' learning dynamics from a control-theory perspective

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

The Effects of Triiodothyronine on the Free Thyroxine Set Point Position in the Hypothalamus Pituitary Thyroid Axis.

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

In silico validation of a customizable fully-autonomous artificial pancreas with coordinated insulin, glucagon and rescue carbohydrates

Artificial pancreas systems should be designed considering different patient profiles, which is challenging from a control theory perspective. In this paper, a flexible-hybrid dual-hormone control algorithm for an artificial pancreas is proposed. The algorithm handles announced/unannounced meals by means of a non-interacting feedforward scheme that safely incorporates prandial boluses. Also, a coordination strategy is employed to distribute the counter-regulatory actions, which can be delivered as a continuous glucagon infusion via an automated pump, as an oral rescue carbohydrate recommendation, or as a rescue glucagon dose recommendation to be administrated through a glucagon pen. The different configurations of the proposed controller were evaluated in silico using a 14-day virtual scenario with random meal intakes and exercise sessions, achieving above 80% time-in-range and low time spent in hypoglycemia.

Differentially private consensus and distributed optimization in multi-agent systems: A review

In the past few decades, distributed multi-agent system (MAS) control has received growing attention due to its numerous advantages. Nonetheless, the substantial reliance on local information exchange in distributed MAS control has given rise to significant privacy concerns. Differential privacy (DP), a mathematically rigorous privacy notion, has gained popularity as a means of safeguarding privacy across multiple fields, including distributed MAS control. In this paper, we present an in-depth overview of the techniques for preserving DP in distributed MAS control, concentrating on consensus and distributed optimization. We begin by outlining the defining features and modeling of MASs from the control theory perspective. Then, we illustrate the motivation for adopting differentially private mechanisms to protect the privacy of distributed MAS control and present the fundamental principles of DP. Based on them, we investigate the cutting-edge techniques designed to preserve DP in consensus and distributed optimization. This review sheds light on the current landscape of DP applications in distributed MAS control and lays the groundwork for future progress in this essential field.

Gender-based violence against female immigrants in South Africa: the control theory perspective

Gender-based violence against female immigrants in South Africa: the control theory perspective

A Study of Methods for Predicting and Controlling Blood Glucose in Diabetic Patients

With the development of society and the improvement of people's living standard, diabetes has gradually become one of the world public health problems that endanger human life safety and affect the development of global economy. Type 1 diabetes is a chronic metabolic disorder that prevents the pancreas from producing insulin and requires lifelong treatment with daily insulin injections to prevent high blood sugar. The lack of insulin leads to the continuous high level of blood sugar in patients. The most important thing for patients is to monitor blood sugar changes and control blood sugar within the normal range. In this paper, a systematic literature search is carried out to study these two aspects, and key information such as the learning model adopted in the literature, the main results, the development of relevant technologies, and the limitations are summarized. From the perspective of glucose-insulin prediction model, due to the complex structure of physiological model, many parameters and difficult to identify, most of the modeling methods are data-driven. There is a great room for improvement in the research on how to mine and utilize existing models to effectively establish accurate glucose prediction models for different objects. From the perspective of control theory, after insulin injection, there is a certain delay in the reduction of blood glucose concentration, and the onset time is different depending on the injection site. The rapid development of deep learning and the increase in available data offer the possibility of addressing these challenges in the near future. When designing closed-loop glucose algorithms, consider using a variety of approaches to establish a personalized glucose control algorithm for each diabetic patient.

‘Adequate amount of data’‐driven system design theory: how far can we know/control/protect?

In contrast to the natural tendency of many researchers to focus on collecting as much data as possible, Assistant Professor Tomonori Sadamoto, from the Department of Mechanical Engineering and Intelligent Systems at the University of Electro-Communications in Japan, believes that the pursuit of big data is not always desirable. He is promoting a shift towards the acceptance of a “adequate amount of data”-driven system design theory. Sadamoto is concentrating on data-driven methodologies and their application in social systems. Through key international collaborations with colleagues at leading institutions, he is advancing his research. His work on data-driven methodologies focuses on interdisciplinary studies that combine machine learning and control theory. His more applied work primarily falls within the realm of smart grids. In his projects, he formulates his questions mathematically from the perspective of control theory. For instance, Sadamoto has developed a novel mathematical tool known as the VARX (vector autoregressive with exogenous input) framework, which facilitates the tractable analysis of dynamic systems. Using this new tool, he has developed data-dependent system identification analyses when only an “insufficient amount of data” is available. Furthermore, for the first time, Sadamoto was able to demonstrate that the informativeness of data in a certain class of dynamic output controller design is equivalent to the identification of the target system. His efforts are aimed at expanding the horizons of these novel control theories into the field of smart grids.

State space representations of the Roesser type for convolutional layers

From the perspective of control theory, convolutional layers (of neural networks) are 2-D (or N-D) linear time-invariant dynamical systems. The usual representation of convolutional layers by the convolution kernel corresponds to the representation of a dynamical system by its impulse response. However, many analysis tools from control theory, e.g., involving linear matrix inequalities, require a state space representation. For this reason, we explicitly provide a state space representation of the Roesser type for 2-D convolutional layers with cinr1+ coutr2states, where cin/coutis the number of input/output channels of the layer and r1/r2characterizes the width/length of the convolution kernel. This representation is shown to be minimal for cin= cout. We further construct state space representations for dilated, strided, and N-D convolutions.

An Accelerated Approach on Adaptive Gradient Neural Network for Solving Time-Dependent Linear Equations: A State-Triggered Perspective.

To improve the acceleration performance, a hybrid state-triggered discretization (HSTD) is proposed for the adaptive gradient neural network (AGNN) for solving time-dependent linear equations (TDLEs). Unlike the existing approaches that use an activation function or a time-varying coefficient for acceleration, the proposed HSTD is uniquely designed from a control theory perspective. It comprises two essential components: adaptive sampling interval state-triggered discretization (ASISTD) and adaptive coefficient state-triggered discretization (ACSTD). The former addresses the gap in acceleration methods related to the variable sampling period, while the latter considers the underlying evolutionary dynamics of the Lyapunov function to determine coefficients greedily. Finally, compared with commonly used discretization methods, the acceleration performance and computational advantages of the proposed HSTD are substantiated by the numerical simulations and applications to robotics.

Causes and Prevention of Juvenile Delinquency from the Perspective of Social Control Theory —Take the Case of Five Teenagers who Killed and Split Their Bodies in Hangzhou as an Example

Causes and Prevention of Juvenile Delinquency from the Perspective of Social Control Theory —Take the Case of Five Teenagers who Killed and Split Their Bodies in Hangzhou as an Example

An integrative control theory perspective on consciousness.

An integrative account of consciousness should have a number of properties. It should build upon a framework of nonconscious behavior in order to explain how and why consciousness contributes to, and addresses the limitations of, nonconscious processes. It should also encompass the primary (phenomenal), secondary (access), and tertiary (self-awareness) aspects of consciousness. A number of accounts have proposed a role for consciousness in the prediction of sensory input, yet these proposals do not address how organisms deal with multiple, unpredictable, disturbances to maintain control. According to perceptual control theory (PCT), purposiveness is the control of hierarchically organized perceptual variables via changes in output that counteract disturbances which would otherwise increase error between the current value and the reference value (goal state) of each perceptual variable. In PCT, reorganization is the process required for the adaptive modification of control systems in order to reduce the error in intrinsic systems that control essential, largely physiological, variables. The current article proposes that primary consciousness emerges from this system, and is sustained as secondary consciousness through a number of processes including the control of the integration rate of novel information via exploratory behavior, attention, imagination, and altering the mutation rate of reorganization. Tertiary consciousness arises when internally sustained perceptual information is associated with specific symbols that form a parallel, propositional system for the use of language, logic, and other symbolic systems. The hypotheses and initial research designs to test this account are provided. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Kenyamanan Lingkungan Kerja Work From Anywhere Meningkatkan Produktivitas Pekerja

The presence of Covid-19 created a new working method called work from home to avoid the spread of Covid-19 and forced people to adapt. During it’s implementation, people felt that work from home have a positive impact on individual work comfort and productivity. Then, work from anywhere phenomenon appears which allows individuals to choose the location where they will work. The work from anywhere working method has now been implemented by government agencies and companies to increase the comfort of workers in carrying out their work. When associated with environmental comfort and control theory, it can be seen that the interaction between the environment and individuals has an important role in creating and increasing a sense of comfort and productivity for individuals at work. This study uses the literature study research method to examine how the influence of environmental comfort on individual comfort in working from anywhere seen through the perspective of control theory.

A quantum particle swarm optimization-based optimal LQR-PID controller for load frequency control of an isolated power system

One of the characteristics of a robust power grid is minimal variations in its frequency to load change or loss in generating unit(s). From the perspective of optimal control theory, the issue of load frequency control in the context of the interconnected functioning of power systems is investigated in this work, and a novel load frequency controller is proposed for a single area isolated power network. This novelty incorporates all the primary characteristics of the solutions that are based on a mixture of optimal controller designs by establishing a linear quadratic regulator optimized with quantum particle swarm optimization to design a proportional integral derivative (PID) controller unlike the conventional PID controller designs that are based on a combined Ziegler-Nichols and root locus (ZN-RL) method and manual tuning. The simulation results of the proposed controller using MATLAB show its efficacy in not only ensuring that there is no steady-state error in terms of the system frequency with load changes but also in achieving smoother transients. Following these landmark achievements, a transfer function model of the resulting power grid is constructed. The outcome of the model reveals that the system transients have been improved while keeping the intended steady-state characteristics. Furthermore, it is observed that the proposed load frequency controller has the best performance when compared with the combined ZN-RL method and the manual PID designs. This, therefore, demonstrates the superiority of the proposed design for load frequency control in power systems.

Effect of Dynamic Pressure Feedback Orifice on Stability of Cartridge-Type Hydraulic Pilot-Operated Relief Valve

Current research on pilot-operated relief valve stability is primarily conducted from the perspective of system dynamics or stability criteria, and most of the existing conclusions focus on the spool shape, damping hole size, and pulsation frequency of the pump. However, the essential factors pertaining to the unstable vibration of relief valves remain ambiguous. In this study, the dynamic behavior of a pilot-operated relief valve is investigated using the frequency-domain method. The result suggests that the dynamic pressure feedback orifice is vital to the dynamic characteristics of the valve. A large orifice has a low flow resistance. In this case, the fluid in the main spring chamber flows freely, which is not conducive to the stability of the relief valve. However, a small orifice may create significant flow resistance, thus restricting fluid flow. In this case, the oil inside the main valve spring chamber is equivalent to a high-stiffness liquid spring. The main mass–spring vibration system has a natural frequency that differs significantly from the operating frequency of the relief valve, which is conducive to the stability of the relief valve. Good agreement is obtained between the theoretical analysis and experiments. The results indicate that designing a dynamic pressure feedback orifice of an appropriate size is beneficial to improving the stability of hydraulic pilot-operated relief valves. In addition, the dynamic pressure feedback orifice reduces the response speed of the relief valve. This study comprehensively considers the stability, rapidity, and immunity of relief valves and expands current investigations into the dynamic characteristics of relief valves from the perspective of classical control theory, thus revealing the importance of different parameters.

Adaptive control strategy improves synchronization of self-propelled agents

This paper introduces an adaptive control strategy to the Vicsek model and investigates the collective behavior of self-propelled agents from the perspective of control theory. In the model, each agent adopts the classic proportional-integral (PI) control strategy to adjust its direction based on the deviation from the average direction of local neighbors. It is found that the integral term of the controller can eliminate the steady-state error, but strong integral effect will cause fluctuation of the system and prolong the convergence time. Therefore, the moderate integral control can adjust the headings of all agents to be synchronized after a short transient time. However, the proportional term incurs over-regulation of the directions, thus impeding the global convergence to be achieved. The robustness of the synchronizability with respect to different noise intensities is also investigated. Even in the strong noise environments, the control model can still maintain a high degree of directional consistency by adopting the conservative adjustment strategy. Furthermore, the diversity of control parameter is analyzed by considering the integral parameter following the normal distribution. It reveals that inconsistent adjustment magnitude is unfavorable to the synchronization of the system.

Post-injury pain and behaviour: a control theory perspective.

Injuries of various types occur commonly in the lives of humans and other animals and lead to a pattern of persistent pain and recuperative behaviour that allows safe and effective recovery. In this Perspective, we propose a control-theoretic framework to explain the adaptive processes in the brain that drive physiological post-injury behaviour. We set out an evolutionary and ethological view on how animals respond to injury, illustrating how the behavioural state associated with persistent pain and recuperation may be just as important as phasic pain in ensuring survival. Adopting a normative approach, we suggest that the brain implements a continuous optimal inference of the current state of injury from diverse sensory and physiological signals. This drives the various effector control mechanisms of behavioural homeostasis, which span the modulation of ongoing motivation and perception to drive rest and hyper-protective behaviours. However, an inherent problem with this is that these protective behaviours may partially obscure information about whether injury has resolved. Such information restriction may seed a tendency to aberrantly or persistently infer injury, and may thus promote the transition to pathological chronic pain states.

Study on effect of collaborative governance participation willingness of online food delivery platform restaurants and consumers from perspective of control theory: Based on moderating effects of perceived risks.

The popularization of the Internet and the convenience of e-commerce are driving the online restaurant industry's rapid development of worldwide. However, serious information asymmetries in online food delivery (OFD) transactions not only aggravate food safety risks, resulting in simultaneous government and market failures, but also intensify consumers' perceived risks. This paper innovatively constructs a research framework for the governance participation willingness of OFD platform restaurants and consumers under the moderating effects of perceived risks from the perspective of control theory and then develops scales for analyzing the governance willingness of both restaurants and consumers. Using data collected through a survey, this paper explores the effect of control elements on governance participation by restaurants and consumers and analyzes the moderating effects of perceived food safety risks. Results showed that both government regulations and restaurant reputation (formal control elements) and online complaints and restaurant management response (informal control elements) can increase governance participation willingness among both platform restaurants and consumers. The moderating effects of perceived risks are partially significant. When the risks perceived by restaurants and consumers are strong, government regulation and online complaints can more effectively boost the governance participation willingness of restaurants and consumers, respectively. At this moment, consumers' willingness to pursue problem solving through online complaints is evidently enhanced. Accordingly, the perceived risks and the online complaints jointly motivate restaurants and consumers to participate in governance activities.

Impulsive Artificial Defense Against Advanced Persistent Threat

Advanced persistent threat (APT) as a new type of cyber espionage poses a severe threat to modern organizations. Artificial APT defense, in which an organization engages experienced cybersecurity experts to artificially check if there exist rootkits implanted by APT actors within the organizational internet and, if so, artificially remove the discovered rootkits, is recognized as an indispensable part of APT defense. There are two different ways of artificial APT defenses: continuous artificial defense (CAD), where the defense work is conducted at all time points, and impulsive artificial defense (IAD), where the defense work is conducted at a scheduled sequence of time points. IAD is superior to CAD in terms of the overall service cost. In the context of IAD, we refer to each sequence of service costs as an IAD policy. This paper addresses the problem of developing a cost-effective IAD policy (the IAD problem). First, by introducing an impulsive state evolutionary model for the organizational intranet, the IAD problem is reduced to an optimal impulsive control model (the IAD model). Second, by deriving the optimality system for the IAD model, an iterative algorithm for solving the IAD model (the IAD algorithm) is presented. Next, the convergence and effectiveness of the IAD algorithm are validated through numerical experiments. Finally, the effect of some factors is inspected. To our knowledge, this is the first time IAD is inspected from the perspective of optimal impulsive control theory.

A Novel Control-Theory-Based Approach to Scheduling of High-Throughput Screening System for Enzymatic Assay

Nowadays, high-throughput screening (HTS) systems are widely used in pharmaceutical industries and laboratories for the discovery of new drugs and biomedical substances. It is important to efficiently schedule them so as to reduce the cost. In the operation of an HTS system, a microplate may visit some resources more than once and complex time window constraints are imposed on some activities and activity sequences. Moreover, with the consistency requirement, a one-microplate cyclic schedule is necessary. Thus, its scheduling problem is very challenging. This article studies the scheduling problem of an HTS system for an enzymatic assay, a typical application of HTSs, from the perspective of control theory. The system is modeled by resource-oriented Petri nets (ROPNs). With the model, necessary and sufficient conditions under which a feasible cyclic schedule exists are established. Then, we determine how many microplates should be in the system for concurrent processing and the transition firing sequence to obtain the activity sequence for an optimal and feasible schedule. In this way, a feasible and optimal cyclic schedule can be found by very simple computations, which shows that polynomial algorithms for an optimal schedule exist, while the existing studies apply mathematical programming with exponential computation complexity. Also, its efficient implementation is given.

Control Theory for Physicists [Bookshelf

The author of this book is a physicist with ample experience in experimental implementations of control. His research concerns fundamental and cross-disciplinary aspects of nonlinear dynamics, biophysics, thermodynamics, statistical mechanics, information theory, and control theory. The book builds on his experience as the author of an influential review on control theory for physical scientists. The text is geared toward physicists. Aside from notation and language, how different is it from textbooks for control theorists and engineers? The key differences are threefold. First, the book places emphasis on foundational aspects of control, including physical limitations of control such as causal and information-theoretic limitations. Second, it offers a balanced coverage of topics that takes advantage of concepts familiar to this audience, such as measurement, while elaborating further on concepts that might be less familiar, such as control objectives. Third, the book offers new insights into physical phenomena from a control theory perspective and covers material not usually treated in control theory books, which should nevertheless be of interest to engineers and control theorists. Owing to this structure, the book does well with its promise to “make the strange familiar, and the familiar strange.” This is not the first control book written for physicists. The treatment throughout prioritizes first-principles descriptions, with an emphasis on not only when control works but also when it fails. It includes well-contextualized examples and well-formulated problems. It is ready for classroom use, with additional resources for instructors—such as a solution manual and associated Mathematica notebooks—available from the publisher. A 100-page supplement on background mathematics is also available on the publisher’s website, which provides a comprehensive review of key mathematical topics. As already noted, this book may indeed lead more departments to include control theory in their curriculum.