Control Theory Perspective Research Articles

Pandemic control - do's and don'ts from a control theory perspective.

Managing a pandemic is a difficult task. Pandemics are part of the dynamics of nonlinear systems with multiple different interactive features that co-adapt to each other (such as humans, animals, and pathogens). The target of controlling such a nonlinear system is best achieved using the control system theory developed in engineering and applied in systems biology. But is this theory and its principles actually used in controlling the current coronavirus disease-19 pandemic? We review the evidence for applying principles in different aspects of pandemic control related to different goals such as disease eradication, disease containment, and short- or long-term economic loss minimization. Successful policies implement multiple measures in concordance with control theory to achieve a robust response. In contrast, unsuccessful policies have numerous failures in different measures or focus only on a single measure (only testing, vaccines, etc.). Successful approaches rely on predictions instead of reactions to compensate for the costs of time delay, on knowledge-based analysis instead of trial-and-error, to control complex nonlinear systems, and on risk assessment instead of waiting for more evidence. Iran is an example of the effects of delayed response due to waiting for evidence to arrive instead of a proper risk analytical approach. New Zealand, Australia, and China are examples of appropriate application of basic control theoretic principles and focusing on long-term adaptive strategies, updating measures with the evolution of the pandemic.

A Motor Adaptation Model Assuming Update of Internal Model in the Motor Cortex

When considering the human motor-adaptation mechanism from the perspective of the motor control theory, updating the internal model constitutes a critical component. The learning curve at each trial of motion can be explained by a state-space model; however, the model cannot reproduce the time-series data for the hand’s position, velocity, and acceleration (motion profiles). There is no internal model-updating rule for optimal feedback control, a plausible model for reproducing motion profiles. In this paper, we propose an adaptation model that incorporates an internal model-updating rule which modeled after Hebb’s rule into optimal feedback control. Also, we examine the neural substrate of the internal model. To validate the proposed adaptation model, we conducted behavioral experiments with humans that reflected changes in the internal model and reproduced the changes in the internal model as well as the motion profiles using the proposed adaptation model. In addition, we analyzed the data for a visuomotor rotation task performed by a monkey and checked for changes in the output characteristics of neurons in the motor cortex before and after adaptation. According to the above-mentioned validation and analysis results, the motor cortex constitutes the neural substrate of the internal model.

Diagnostic uncertainty in patients, parents, and physicians: a compensatory control theory perspective

ABSTRACT Medical diagnoses offer a structure by which psychological uncertainty can be attenuated, allowing patients to diminish psychological threats and focus on health prognosis. Yet when no diagnosis can be made, patients may experience diagnostic uncertainty – perceiving the medical field as unable to provide an accurate explanation of the cause of their health problems. This review examines the psychological threat that diagnostic uncertainty imposes on individuals’ need for control and understanding, and the resulting consequences experienced by patients, parents of pediatric patients, and physicians. Using compensatory control theory as a framework, we propose a taxonomy of behaviors that people may adopt in order to regain control in the face of diagnostic uncertainty and to reaffirm that the world is not random and chaotic. To manage diagnostic uncertainty, people may bolster their personal agency, affiliate with external systems they see as acting in their interest, affirm clear connections between behaviors and outcomes, and affirm nonspecific epistemic structure. Diagnostic uncertainty is approached from the perspectives of patients, parents of pediatric patients, and physicians, demonstrating how each group responds in order to maintain a sense that the world has structure and is not random. Discussion centers on moderators, limitations, and implications for clinical practice.

An LSTM with Differential Structure and Its Application in Action Recognition

Because of the broad application of human action recognition technology, action recognition has always been a hot spot in computer vision research. The Long Short-Term Memory (LSTM) network is a classic action recognition algorithm, and many effective hybrid algorithms have been proposed based on basic LSTM infrastructure. Although some progress has been made in accuracy, most of those hybrid algorithms have to have more and more complex structures and deeper network levels. After analyzing the structure of the classic LSTM from the perspective of control theory, we determined that the classic LSTM could strengthen the differential characteristics of human action recognition technology to reflect the change of speed. Thus, an improved LSTM structure with an input differential characteristic module is proposed. Furthermore, in this article, we considered the influence of first-order and second-order differential on the extraction of movement pose information, that is, the influence of movement speed and acceleration on action recognition. We designed four different LSTM units with first-order and second-order differential. Moreover, the experiments were performed for the four units on three common datasets repeatedly. We found that the LSTM network with the input differential feature module proposed in this article can effectively improve action recognition accuracy and stability without deepening the complexity of the network and can be used as a new basic LSTM network architecture.

Compensatory control in public emergencies

<p id="p00005">Public emergencies refer to social events that occur suddenly, cause, or may cause serious social harm and require emergency measures in dealing with them. Previous studies have focused on the psychological and behavioral responses of individuals during public emergencies. However, most of these studies have focused on describing psychological phenomena rather than exploring psychological mechanisms. Thus, the present article proposes that compensatory control theory can be used as a basic theoretical framework to understand a series of psychological and behavioral manifestations of individuals in the context of public emergencies. The core idea of this theory is that when individuals lack a sense of control, they tend to show a variety of reactions that have a common motivation; that is seeking structure. We analyzed the applicability of compensatory control theory to explain individuals’ psychological performance in the context of public emergencies from four perspectives. First, after major public emergencies occur, conspiracy theories on the cause of the event often follow. From the perspective of compensatory control theory, when individuals’ sense of control is threatened due to the occurrence of public emergencies, they tend to form conspiratorial thinking to some extent. Hence, they are eager to obtain a sense of order or structure to compensate. Since it is often difficult for authorities to immediately give a definitive answer about the cause of the incident, the conspiracy theory is often believed, which satisfies people’s search for causality and certainty. Second, during public emergencies, people tend to believe in and spread rumors. From the perspective of compensatory control theory, this can also be interpreted as individuals’ need to obtain a sense of structure brought about by deterministic information in a crisis, which leads to blind acceptance of unreliable information. Third, people exposed to public emergencies will also show stronger concern for moral issues, which, according to the compensatory control theory, is an individual’s search for certain social values. Further, the need for order and structure still matters when focusing on values. By emphasizing the objective standard of morality and strengthening the value that they believe in, people can achieve a sense of structure and order to some extent and thus, compensate for the lack of perceived control. Finally, in the context of public emergencies, there is another typical public behavior: irrational buying and hoarding of commodities required for daily life or event protection materials. This phenomenon can also be partially attributed to the performance of the individual need for structure. Due to the lack of a sense of control, individuals need to satisfy their internal needs for control and order through the possession of tangible material resources, which leads to their irrational purchasing behavior. These typical symptoms (belief in conspiracy theories, spreading rumors, moral concerns, and irrational hoarding) can also be explained by other social psychological theories to some extent. For example, by analyzing an individual’s fear of death and the worldview defense process used to overcome this fear, terror management theory can explain an individual’s focus on moral values. Similarly, the uncertainty management theory and meaning maintenance model can also explain some psychological and behavioral reactions of individuals in public emergencies. However, we believe that compensatory control theory has a unique advantage in understanding the above phenomena. This is because it has a clearer definition of antecedents (lack of perceived control) and consequences (need for structure) and shows a wide range of explanatory power and cross-cultural applicability. We suggest that in future studies, people’s psychological and behavioral responses to public emergencies should be further explored from the perspective of compensatory control. In addition, future research should delve deeper to explore this issue from theoretical, empirical, and practical perspectives.

Optimal control approach to gradient-index design for beam reshaping.

We address the problem of reshaping light in the Schrödinger optics regime from the perspective of the optimal control theory. In technological applications, Schrödinger optics is often used to model a slowly varying amplitude of a para-axially propagating electric field where the square of the waveguide's index of refraction is treated as the potential. The objective of the optimal control problem is to find the controlling potential which, together with the constraining Schrödinger dynamics, optimally reshapes the intensity distribution of Schrödinger eigenfunctions from one end of the waveguide to the other. This work considers reshaping problems found in work by Kunkel and Leger, and addresses computational needs by adopting tools from the quantum control literature. The success of the optimal control approach is demonstrated numerically.

Optimal control for parameter estimation in partially observed hypoelliptic stochastic differential equations

We deal with the problem of parameter estimation in stochastic differential equations (SDEs) in a partially observed framework. We aim to design a method working for both elliptic and hypoelliptic SDEs, the latters being characterized by degenerate diffusion coefficients. This feature often causes the failure of constrast estimator based on Euler Maruyama discretization scheme and dramatically impairs classic stochastic filtering methods used to reconstruct the unobserved states. All of theses issues make the estimation problem in hypoelliptic SDEs difficult to solve. To overcome this, we construct a well-defined cost function no matter the elliptic nature of the SDEs. We also bypass the filtering step by considering a control theory perspective. The unobserved states are estimated by solving deterministic optimal control problems using numerical methods which do not need strong assumptions on the diffusion coefficient conditioning. Numerical simulations made on different partially observed hypoelliptic SDEs reveal our method produces accurate estimate while dramatically reducing the computational price comparing to other estimation procedures.

A new conceptual model to treat tourist behaviour: locus of control theory application

Tourist attachment to destination brands is crucial for destination revenue growth. However, no prior study investigates how different tourists' ascriptions to destination events could affect tourism destination brands' attachment. From the locus of control (LOC) theory perspectives which considers one of the crucial theories to treat consumer behaviour: individuals with an internal LOC (vs external LOC) attribute events' outcomes to themselves more than ascribing it to external causes. Thus, this study uses the LOC theory perspective to examine tourists' ascriptions, whether internal or external, with its impacts on attachment to tourism destination brands. Moreover, this study contributes theoretically by adding new constructs that help to fathom tourists' attribution toward destination brands, such as information, knowledge, and experiences at destinations with its impacts on tourists' LOC. Also, it provides potential ideas to test these constructs empirically. The theoretical and practical implications are discussed.

Type of Evaluation and Features of the Activity to Be Evaluated: Where Does the Trap Lie? The Case of the Teaching Excellence Framework

ABSTRACT The article adopts the Management Control Theory (MCT) perspective to investigate the relationship between the characteristics of tasks/activities to be assessed and the type of assessment employed. Two analytical dimensions are considered: the measurability/attributability of outputs and the knowledge of cause-effect relations producing outputs. The introduction of an evaluation system of teaching in higher education is here used as case-study. The article shows how the complexity of teaching, expressed by a high interdependency among actors and multiple heterogeneous outputs, is not adequately tackled by an evaluation system that is narrowly focused on the quantification of observable outputs. Unintended consequences might therefore arise.

Nonlinear spacing policies for vehicle platoons: A geometric approach to decentralized control

In this paper a decentralized approach to the platooning problem with nonlinear spacing policies is considered. A predecessor–follower control structure is presented in which a vehicle is responsible for tracking of a desired spacing policy with respect to its predecessor, regardless of the control action of the latter. From the perspective of geometric control theory, we state necessary and sufficient conditions for the existence of decentralized controllers that guarantee tracking and asymptotic stabilization of a general nonlinear spacing policy. Moreover, all nonlinear spacing policies for which there exists a decentralized state feedback controller that achieves asymptotic tracking are characterized. It is shown that string stability is a consequence of the choice of spacing policy and sufficient conditions for a spacing policy to imply string stability are given. As an example, we fully characterize all state feedback controllers that achieve the control goals for a given nonlinear spacing policy, guaranteeing asymptotic tracking for a heterogeneous platoon. The results are illustrated through simulations.

Developing an Unreal Engine 4-Based Vehicle Driving Simulator Applicable in Driver Behavior Analysis—A Technical Perspective

Vehicle safety remains a topic of major interest, and diverse assistance systems are implemented that focus primarily on analyzing the immediate vicinity of the car and the driver’s control inputs. In this paper, by contrast, we emphasize understanding the driver’s control performance via obtaining valuable data and relevant characteristics. To acquire the data, we employed an in-house-designed, laboratory-built vehicle driving simulator. This simulator exploits the Unreal Engine 4 framework to deliver a high level of realism. The fact that the actual designing and associated processes were materialized through our own efforts has brought advantages such as simplified data acquisition, possibility of creating custom scenarios, and modification of the virtual elements according to our specific needs. We also developed an application to analyze the measured data from the perspective of control theory, establishing a set of parameters that provided the basis for an early version of a driver performance index indicator.

A brief review on attack design and detection strategies for networked cyber-physical systems

Networked cyber-physical systems (NCPSs) can be found in various fields such as industrial process, robotics, smart buildings, energy, healthcare systems, transportation, and surveillance. Recently accomplished real-time attacks indicate security vulnerabilities that weaken the reliability of NCPSs. Research areas on the security of NCPSs can be categorized into two groups: from the perspective of information security, from the perspective of control theory. In this paper, first possible attack locations on the control scheme of a NCPS which can be divided into three different groups namely sensor side, actuator side, and state estimator side are discussed and then a brief survey containing some recent studies on security strategies for NCPSs from the perspective of control theory is presented. After that attack detection strategies for a NCPS are briefly introduced and a general architecture utilized for attack detection on a NCPS is presented. In addition, some of recent studies on attack detection strategies for NCPSs from the perspective of control theory are discussed.

Minimum-Degree Distributed Graph Filter Design

Here we consider the problem of designing finite-impulse-response (FIR) graph filter (GF) in a fully distributed way. For a directed graph with $\mathsf {N}$ nodes, each node designs filter coefficients in a distributed manner, when the knowledge of the graph structure, recognized as global information, is unavailable to each node. By modeling graph signal shifting with observations at a node as a linear dynamical system, we establish fundamental connections between local response of shifting at a node, concerned in the graph signal processing (GSP) field, and the observability of the system, investigated in control theory. The observability, as a measure of how well internal states of a dynamical system can be learned from node's observations, is reflected by the minimal polynomial of a matrix pair related to the system. Specifically, by introducing a notion of observable graph frequencies to a node, we show that the output signals (observations) at a node only contain the spectral components of its so-called observable graph frequencies. Furthermore, we unveil that the observability of a node to the spectral components of a GS is related to the rank of its observability matrix from the perspective of control theory. Our work reveals that partial signal outputs at a node are sufficient to design the FIR GF locally in terms of node-variant (NV) GFs. These findings further enable us to characterize the minimum-degree NV GF, where a minimum number of its shifted GSs are involved in the filter's output, and devise a distributed GF design algorithm for it.

Governing Intra-project Modular Interdependencies in ISD Projects: A Control Theory Perspective

Though information systems development (ISD) projects use modularization as an approach to better manage complex tasks by decomposing them into simpler intra-project modules, we lack clearly established modalities for managing such modularized ISD projects. Adopting a control theory perspective and leveraging a case study research approach, we unearth the underlying “control mechanisms” that an organization leveraged to manage eight modularized ISD projects. Specifically, we explore the intra-project modular dependencies that the projects’ business requirement documents indicated and use results from semi-structured interviews with project members to identify the corresponding control mechanisms. Our results indicate that, in scenarios with a low level of intra-project modular interdependencies, formal outcome and formal behavior constitute the preferred control mechanisms. However, specific situations related to flexible project practices and volatile client requirements may minimize the level of formal outcome and formal behavior control mechanisms in such projects. A low level of interdependencies between intra-project modules minimizes the need for informal clan control; nonetheless, informal clan-control mechanisms may help team members understand project requirements in a shared manner. Projects with a high level of interdependencies between intra-project modules have a high level of informal clan control. However, in some situations, projects with a high level of intra-project modular interdependencies have a low level of informal clan control often due to time pressures. Organizations may govern projects with a high level of intra-project modular interdependencies and poor structures through an enabling control style. Organizations can effectively govern projects with a low level of intra-project modular interdependencies through authoritative control style except in the projects where they assign team members to multiple projects simultaneously. By leveraging control theory to examine the intra-project modular dependencies, we add to the ongoing discourse on control theory and ISD project governance.

External environment modelling in the control system of the regional socio-economic system

The concept and methods of describing the external environment of a regional socioeconomic system from the perspective of control theory are considered. The position and relations of the external environment with other elements of the control system of the regional socio-economic system are also determined. It is shown that the external environment is set by factors that determine the linguistic composition of external influence on the object of management; the main disturbances that determine the dynamic nature of the influence; interferences. A structural-functional and mathematical model of the external environment of a regional socio-economic system is proposed which is characterized by the author’s classification of environmental factors, formalization of its main components and information flows. It is also shown that for describing disturbances of the external environment and their interferences fuzzy logic is applicable; the description of fuzzy dynamic characteristics of the external environment is provided. The general model of using measuring devices for determining the influence of the external environment on the object of control– the regional socio-economic system - is presented.

Are individuals from lower social classes more susceptible to conspiracy theories? An explanation from the compensatory control theory

Previous research has indicated that social class is likely to be an important factor that influences individuals’ beliefs in conspiracy theories; however, the underlying psychological mechanisms between these two variables remain unclear. This study directly investigated the relationship between these two variables through questionnaires and experimental manipulation, and introduced the perspective of compensatory control theory to investigate the serial mediating role of perceived control and need for structure in the process of social class influencing individuals’ belief in conspiracy theories. The results showed that social class can significantly negatively predict individuals’ belief in conspiracy theories, and perceived control and need for structure played a serial mediating role between them. To some extent, these results reveal the psychological mechanism that causes the higher likelihood of people from lower social classes to believe in conspiracy theories, and advances the explanation from the compensatory control theory.

Achieving stable dynamics in neural circuits.

The brain consists of many interconnected networks with time-varying, partially autonomous activity. There are multiple sources of noise and variation yet activity has to eventually converge to a stable, reproducible state (or sequence of states) for its computations to make sense. We approached this problem from a control-theory perspective by applying contraction analysis to recurrent neural networks. This allowed us to find mechanisms for achieving stability in multiple connected networks with biologically realistic dynamics, including synaptic plasticity and time-varying inputs. These mechanisms included inhibitory Hebbian plasticity, excitatory anti-Hebbian plasticity, synaptic sparsity and excitatory-inhibitory balance. Our findings shed light on how stable computations might be achieved despite biological complexity. Crucially, our analysis is not limited to analyzing the stability of fixed geometric objects in state space (e.g points, lines, planes), but rather the stability of state trajectories which may be complex and time-varying.

Adaptive treatment and robust control.

A control theory perspective on determination of optimal dynamic treatment regimes is considered. The aim is to adapt statistical methodology that has been developed for medical or other biostatistical applications to incorporate powerful control techniques that have been designed for engineering or other technological problems. Data tend to be sparse and noisy in the biostatistical area and interest has tended to be in statistical inference for treatment effects. In engineering fields, experimental data can be more easily obtained and reproduced and interest is more often in performance and stability of proposed controllers rather than modeling and inference per se. We propose that modeling and estimation should be based on standard statistical techniques but subsequent treatment policy should be obtained from robust control. To bring focus, we concentrate on A-learning methodology as developed in the biostatistical literature and -synthesis from control theory. Simulations and two applications demonstrate robustness of the strategy compared to standard A-learning in the presence of model misspecification or measurementerror.

The energy impact of adaptive cruise control in real-world highway multiple-car-following scenarios

BackgroundSurging acceptance of adaptive cruise control (ACC) across the globe is further escalating concerns over its energy impact. Two questions have directed much of this project: how to distinguish ACC driving behaviour from that of the human driver and how to identify the ACC energy impact. As opposed to simulations or test-track experiments as described in previous studies, this work is unique because it was performed in real-world car-following scenarios with a variety of vehicle specifications, propulsion systems, drivers, and road and traffic conditions.MethodsTractive energy consumption serves as the energy impact indicator, ruling out the effect of the propulsion system. To further isolate the driving behaviour as the only possible contributor to tractive energy differences, two techniques are offered to normalize heterogeneous vehicle specifications and road and traffic conditions. Finally, ACC driving behaviour is compared with that of the human driver from transient and statistical perspectives. Its impact on tractive energy consumption is then evaluated from individual and platoon perspectives.ResultsOur data suggest that unlike human drivers, ACC followers lead to string instability. Their inability to absorb the speed overshoots may partly be explained by their high responsiveness from a control theory perspective. Statistical results might imply the followers in the automated or mixed traffic flow generally perform worse in reproducing the driving style of the preceding vehicle. On the individual level, ACC followers have tractive energy consumption 2.7–20.5% higher than those of human counterparts. On the platoon level, the tractive energy values of ACC followers tend to consecutively increase (11.2–17.3%).ConclusionsIn general, therefore, ACC impacts negatively on tractive energy efficiency. This research provides a feasible path for evaluating the energy impact of ACC in real-world applications. Moreover, the findings have significant implications for ACC safety design when handling the stability-responsiveness trade-off.

A parallel computing method based on zeroing neural networks for time-varying complex-valued matrix Moore-Penrose inversion

This paper analyzes the existing zeroing neural network (ZNN) models from the perspective of control theory. It proposes an exclusive ZNN model for solving the dynamic complex-valued matrix Moore-Penrose inverse problem: the complex-valued zeroing neural network (CVZNN). Then, a method of constructing a special type of saturation-allowed activation function is defined, which relaxes the convex constraint on the activation function when constructing the ZNN model. The convergence of the CVZNN model activated by proposed saturation-allowed functions is analyzed. Besides, the robustness of the CVZNN model under different types of noise interference is investigated based on the perspective of the control theory. Finally, the effectiveness and superiority of the CVZNN model are illustrated by simulation experiments.