Control Theory Research Articles

Conflict Detection in Language Processing: Using Affect Control Theory to Predict Neural Correlates of Affective Incongruency in Social Interactions

AbstractAffect control theory (ACT) is a sociological theory of meaning processing in social interactions. Meaning, according to ACT, derives from cultural institutions and situational affordances, having denotative (declarative) as well as connotative (affective) properties. Mathematical formalizations of ACT allow predictions of affective incongruency (in the terminology of ACT, deflection), which arises from conflicting institutional and situational meanings in a given interaction context. Although ACT is theoretically consistent, its propositions regarding cognitive and affective processing have rarely been tested. The present study fills this gap by investigating the neural processing of affective incongruency in linguistic descriptions of social interactions. Following a neurosociological paradigm, the study draws on neurocognitive evidence on the effects of words’ affective content on word processing as well as on a previous electroencephalography study that investigated processing of affective incongruency using event-related brain potentials. We hypothesized that affective incongruency is associated with activation in the anterior cingulate cortex (ACC), a brain area known for conflict processing. To test this hypothesis, we replicated the electroencephalography study using functional magnetic resonance imaging: We visually presented sentences describing social interactions to 23 participants in a silent reading task while measuring differences in the hemodynamic response in two conditions of affective congruency. Results show expected increases in neural activity for affectively incongruent sentences in the left ACC, supporting the assumption that affective language content influences meaning-making at very early semantic processing stages. The results also add to the emerging neuroscientific evidence for ACT’s mathematical model of impression formation.

Deciphering the enigma of Lassa virus transmission dynamics and strategies for effective epidemic control through awareness campaigns and rodenticides

This study aims to formulate a mathematical framework to examine how the Lassa virus spreads in humans of opposite genders. The stability of the model is analyzed at an equilibrium point in the absence of the Lassa fever. The model’s effectiveness is evaluated using real-life data, and all the parameters needed to determine the basic reproduction number are estimated. Sensitivity analysis is performed to pinpoint the crucial parameters significantly influencing the spread of the infection. The interaction between threshold parameters and the basic reproduction number is simulated. Control theory is employed to devise and evaluate strategies, such as awareness campaigns, advocating condom usage, and deploying rodenticides to reduce the possibility of virus transmission efficiently.

Differential games in the supply chain of innovative products with consumer purchase regret induced returns.

Innovative products entering the market will cause dynamic changes in market demand, and consumers' purchase regret and their return behavior make the market environment more and more complex, which in turn affects the dynamic decision-making in the supply chain. In this paper, under the situation of discrete decision time, combining with the objective reality, we make discrete modification to the classical Bass diffusion model (Bass model), construct a manufacturer-led, retailer-followed supply chain differential game model, analyze the optimal decision-making of the manufacturer and the retailer by combining with the theory of discrete optimal control, and then verify the conclusions by numerical simulation. The results show that: when retailers purchase directly from the manufacturer and sell in the market, the optimal pricing of the innovative product can make the supply chain as a whole, realizing Pareto optimality; consumer's purchase regret will increase the amount of returns, which will lead to the decrease of product sales and the profits of the manufacturer and the retailer; when the innovative product accounts for a different share of the market, the impact of purchase regret on the wholesale price and the retail price are also different. Therefore, manufacturers need to have an extensive comprehension of the market to minimize the negative effects of consumer regret and returns, and to formulate a reasonable pricing strategy for their products to gain as much profit as possible.

Splitting probabilities as optimal controllers of rare reactive events.

The committor constitutes the primary quantity of interest within chemical kinetics as it is understood to encode the ideal reaction coordinate for a rare reactive event. We show the generative utility of the committor in that it can be used explicitly to produce a reactive trajectory ensemble that exhibits numerically exact statistics as that of the original transition path ensemble. This is done by relating a time-dependent analog of the committor that solves a generalized bridge problem to the splitting probability that solves a boundary value problem under a bistable assumption. By invoking stochastic optimal control and spectral theory, we derive a general form for the optimal controller of a bridge process that connects two metastable states expressed in terms of the splitting probability. This formalism offers an alternative perspective into the role of the committor and its gradients in that they encode force fields that guarantee reactivity, generating trajectories that are statistically identical to the way that a system would react autonomously.

A Finite-Time Control Design for the Discrete-Time Chaotic Logistic Equations

Finite-time control theory has been widely used as a mathematical tool to design robust controllers. By manipulating the finite-time convergence proof of this theory, we developed a new control design appropriately tuned for the finite-time control of the chaotic logistics system. In this experimental setup, the logistic equation is programmed into a PIC microcontroller, and a part of the controller was conceived using analog electronics. Because the system to be controlled is in the discrete-time domain, and the finite-time stability proof is stated in the continuous-time representation, our finite-time control approach is a good example for designing control algorithms in both time domain schemes. Hence, our experimental results support our main contribution. Pulse width modulation (PWM) is the format used to translate digital signals into the continuous-time field. Therefore, the main contribution of this article is the theoretical foundations for creating a recent controller that satisfies the convergence criterion in finite time and its construction using an 8-bit microcontroller. All this contributes to the chaotic logistical map.

Analytical design of modified Smith predictor for second-order stable time delay plants incorporating a zero

Simplified control tuning not only enhances engineers' proficiency in control theory and its application but also contributes to their skill development. This study focuses on the control of stable second-order plus time delay (SOPTD) plants incorporating a zero, by employing a modified Smith predictor structure. A forward path PID controller and a PID controller with a lead/lag filter in the feedback path contribute to the proposed control system. The PID controller determines the set-point tracking. The load disturbance rejection is influenced by both the PID and lead/lag filter. Each controller is simply designed using pole placement, while considering a tuning parameter to make a balance between performance and robustness. Filtering of the set-point signal is implemented to enhance its response. To assess the effectiveness of the proposed technique, the study involves examining a SOPTD plant and either a negative or a positive zero. Simulation and comparison analysis show that the proposed controller is appropriate for industrial usage since it quickly tracks the set-point and efficiently rejects disturbances, resulting in a smooth control signal. Furthermore, robustness tests reveal its satisfactory degree of robustness against model parameter uncertainties. In summary, the proposed approach is straightforward and outperforms recently published design methodologies in terms of both performance and robustness.

Intentional action, knowledge, and cognitive extension

Intentional actions exhibit control in a way that mere lucky successes do not. A longstanding tradition in action theory characterizes actional control in terms of the knowledge with which one acts when acting intentionally. Given that action theorists, no less than epistemologists, typically take for granted the orthodox thesis that knowledge is in the head (viz., realized exclusively by brainbound cognition), the idea that intentional action is controlled in virtue of knowledge is tantamount to the idea that the knowledge by which intentional actions exhibit control supervenes intracranially. We raise some challenges for this idea, and in doing show, we show how epistemic theories of actional control are naturally aligned moreso than has been appreciated with cognitive extension in the theory of mind.

The Incentive-Sensitization Theory of Addiction 30 Years On.

The incentive-sensitization theory (IST) of addiction was first published in 1993, proposing that (a) brain mesolimbic dopamine systems mediate incentive motivation ("wanting") for addictive drugs and other rewards, but not their hedonic impact (liking) when consumed; and (b) some individuals are vulnerable to drug-induced long-lasting sensitization of mesolimbic systems, which selectively amplifies their "wanting" for drugs without increasing their liking of the same drugs. Here we describe the origins of IST and evaluate its status 30 years on. We compare IST to other theories of addiction, including opponent-process theories, habit theories of addiction, and prefrontal cortical dysfunction theories of impaired impulse control. We also address critiques of IST that have been raised over the years, such as whether craving is important in addiction and whether addiction can ever be characterized as compulsive. Finally, we discuss several contemporary phenomena, including the potential role of incentive sensitization in behavioral addictions, the emergence of addiction-like dopamine dysregulation syndrome in medicated Parkinson's patients, the role of attentional capture and approach tendencies, and the role of uncertainty in incentive motivation.

Learning and Controlling Multiscale Dynamics in Spiking Neural Networks Using Recursive Least Square Modifications.

Invasive brain-computer interfaces (BCIs) have the capability to simultaneously record discrete signals across multiple scales, but how to effectively process and analyze these potentially related signals remains an open challenge. This article introduces an innovative approach that merges modern control theory with spiking neural networks (SNNs) to bridge the gap among multiscale discrete information. Specifically, the macroscopic point-to-point trajectory is formulated as an optimal control problem with fixed terminal time and state, and it is iteratively solved using the direct dynamic programming (DDP) algorithm. Additionally, SNN is utilized to simulate microscale neural activities in the premotor cortex, employing the product of the weighted adjacency matrix and the mesoscale firing rate to approximate the macroscopic trajectory. The error between actual macroscale behavior and the preceding approximation is then used to update the weighted adjacency matrix through the recursive least square (RLS) method. Analysis and simulation of various tasks, including low-dimensional point-to-point tasks, high-dimensional complex Lorenz systems, and center-out-and-back tasks, verify the feasibility and interpretability of our method in processing multiscale signals ranging from spiking neurons to motion trajectory through the integration of SNN and control theory.

Moving in an Uncertain World: Robust and Adaptive Control of Locomotion from Organisms to Machine Intelligence.

Whether walking, running, slithering, or flying, organisms display a remarkable ability to move through complex and uncertain environments. In particular, animals have evolved to cope with a host of uncertainties-both of internal and external origin-to maintain adequate performance in an ever-changing world. In this review, we present mathematical methods in engineering to highlight emerging principles of robust and adaptive control of organismal locomotion. Specifically, by drawing on the mathematical framework of control theory, we decompose the robust and adaptive hierarchical structure of locomotor control. We show how this decomposition along the robust-adaptive axis provides testable hypotheses to classify behavioral outcomes to perturbations. With a focus on studies in non-human animals, we contextualize recent findings along the robust-adaptive axis by emphasizing two broad classes of behaviors: 1) compensation to appendage loss and 2) image stabilization and fixation. Next, we attempt to map robust and adaptive control of locomotion across some animal groups and existing bio-inspired robots. Finally, we highlight exciting future directions and interdisciplinary collaborations that are needed to unravel principles of robust and adaptive locomotion.

A Novel Fuzzy-type Zeroing Neural Network for Dynamic Matrix Solving and Its Applications

Solving dynamic matrix problems has always been an important research topic in the field of science and engineering, for this, we design a method to solve time-varying problems through a novel fuzzy-type zeroing neural network (NFTZNN) model. A new activation function is proposed to construct zeroing neural network model and ensure fast convergence in predefined-time. In addition, combined with the fuzzy control theory, convergence parameters are replaced by fuzzy parameters to enhance the adaptability of the model as well as its robustness in the presence of external noise perturbations. Furthermore, the convergence and robustness of this neural network system are analyzed theoretically, ensuring the effectiveness. Finally, the feasibility of the model is further verified by the simulation experiments including matrix inversion, circuit solution, encryption transmission and dual-arm robot control.

High-Performance Control Method for Six-Degree-of-Freedom Micromotion Stage Based on High-Order Sliding Mode Control Theory

High-Performance Control Method for Six-Degree-of-Freedom Micromotion Stage Based on High-Order Sliding Mode Control Theory

A Numerical Method for Investigating Fractionali Volterra-Fredholm Integro-Differential Model

In this article, we investigate the fractional Volterra-Fredholm integro-differential equations. These equations appear in several applications such as control theory, biology, and particle dynamics in physics. We derive a numerical method based on the operational matrix method to solve this class of integro-differential equations. We prove the existence and uniqueness of the ex-act solution. Additionally, we demonstrate the uniform convergence of the numerical solutions to the exact solution. We present several numerical examples to show the numerical efficiency of the proposed method. In the first example, we choose a linear problem and find that the approximate solution converges to the exact solution when the number of block pulse functions is very large. In the next two examples, we consider the nonlinear case and compute the L2-local truncation error since exact solutions are not available. The error was of order 10−12. Furthermore, we sketch the graph of the approximate solutions for different values of the fractional derivative to observe the influence of the fractional derivative on the profile of the solutions. Theoretical and numerical results show that the proposed method is accurate and can be applied to other nonlinear problems in science.

A Cortical-Inspired Contour Completion Model Based on Contour Orientation and Thickness

An extended four-dimensional version of the traditional Petitot–Citti–Sarti model on contour completion in the visual cortex is examined. The neural configuration space is considered as the group of similarity transformations, denoted as M=SIM(2). The left-invariant subbundle of the tangent bundle models possible directions for establishing neural communication. The sub-Riemannian distance is proportional to the energy expended in interneuron activation between two excited border neurons. According to the model, the damaged image contours are restored via sub-Riemannian geodesics in the space M of positions, orientations and thicknesses (scales). We study the geodesic problem in M using geometric control theory techniques. We prove the existence of a minimal geodesic between arbitrary specified boundary conditions. We apply the Pontryagin maximum principle and derive the geodesic equations. In the special cases, we find explicit solutions. In the general case, we provide a qualitative analysis. Finally, we support our model with a simulation of the association field.

3D Chaotic Nonlinear Dynamic Population-Growing Mathematical System Modeling with Multiple Controllers

Modeling, stabilization, and identification processes are significant stages in the process of developing knowledge about chaotic dynamical systems which entail the effective prediction depending on the degree of uncertainty toleration in the forecast, accuracy of the current state to be measured as well as a time scale resting on the dynamics of the system. Control of under-activated dynamical systems has been considered substantially, and it is for periods and is currently developing in various domains such as biology, data analysis, computing systems, and so forth. Dynamic systems of growing population signifies a model describing the way a population evolves over time during which population goes through major life events, split into discrete time periods. The size of the population at a given time period is determined by the rate of growth as well as other related factors. Most progress has been made in model-based control theory, which has drawbacks when the system under consideration is exceedingly complicated, and no model can be constructed. Accordingly, a 3D-discrete and dynamic human population growth system with many controllers is proposed by examining the stability and symmetry of controller system clarifications. The symmetric stability control results are presented by considering a special parametric dynamic system in its coefficients besides suggesting periodic functional coefficients in terms of sin and cos functions. The controllers have the ability to reduce population growth rate unpredictability or enhance system stability under various external conditions. The unique and very effective strategies in relevant domains could provide a deeper understanding of their impact as well as the theoretical or technological innovations thereof. These controllers are capable of reducing population growth rate unpredictability or improving system stability under various external conditions, and applicable strategies in the relevant domains can provide profound comprehension over the impact along with the theoretical as well as technological advancements.

A Compartmental Approach to Modeling the Measles Disease: A Fractional Order Optimal Control Model

Measles is the most infectious disease with a high basic reproduction number (R0). For measles, it is reported that R0 lies between 12 and 18 in an endemic situation. In this paper, a fractional order mathematical model for measles disease is proposed to identify the dynamics of disease transmission following a declining memory process. In the proposed model, a fractional order differential operator is used to justify the effect and success rate of vaccination. The total population of the model is subdivided into five sub-compartments: susceptible (S), exposed (E), infected (I), vaccinated (V), and recovered (R). Here, we consider the first dose of measles vaccination and convert the model to a controlled system. Finally, we transform the control-induced model to an optimal control model using control theory. Both models are analyzed to find the stability of the system, the basic reproduction number, the optimal control input, and the adjoint equations with the boundary conditions. Also, the numerical simulation of the model is presented along with using the analytical findings. We also verify the effective role of the fractional order parameter alpha on the model dynamics and changes in the dynamical behavior of the model with R0=1.

Integrated control‐structure co‐design for flexible manipulators

AbstractThis work aims to develop a methodology for the integrated control‐structure design of flexible multibody systems. Such an issue is linked to a generalization of the inverse dynamics problem, where both the feedforward control and mechanical parameters are designed to perform a desired trajectory optimally. The resulting constrained motion is aimed at improving the dynamic performance of the system, which is underactuated, with less power demand but possible elastic deformation. Stable inversion methods are required to deal with the internal dynamics related to underactuation. The proposed methodology is based on the optimal control theory. For illustrative purposes, we considered a fully actuated linear time‐invariant (LTI) system, establishing the integrated design results in a least‐squares homogeneous system and in an optimization problem. An additional formulation is proposed for the integrated design of the flexible multibody systems, where both a multi‐criterion optimization and a scalarized problem of a corresponding nonlinear programming problem are formulated to deal with the unstable zero‐dynamics related to non‐minimum phase systems and nonlinearity. The numerical simulation of an underactuated two‐masses‐spring‐damper system and a flexible robotic manipulator in planar motions are used for validation. Finally, the beneficial aspects of the integrated design are analyzed.

Is the Effect of Trust on Risk Perceptions a Matter of Knowledge, Control, and Time? An Extension and Direct-Replication Attempt of Siegrist and Cvetkovich (2000)

The complexity of societal risks such as pandemics, artificial intelligence, and climate change may lead laypeople to rely on experts and authorities when evaluating these threats. While Siegrist and Cvetkovich showed that competence-based trust in authorities correlates with perceived societal risks and benefits only when people feel unknowledgeable, recent research has yielded mixed support for this foundational work. To address this discrepancy, we conducted a direct-replication study (preregistered; 1,070 participants, 33 risks, 35,310 observations). The results contradict the original findings. However, additional non-preregistered analyses indicate an alternative perspective aligning with compensatory control theory and the description-experience framework: experiences with insufficient personal control over a threat may amplify individuals’ dependency on powerful others for risk mitigation. These findings highlight the need to reevaluate how trust shapes risk perceptions. Recent societal and technological shifts might have heightened the desire for control compared to subjective knowledge in why people resort to trust.

A network control theory pipeline for studying the dynamics of the structural connectome.

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.

Perception of School Social Bonding (PSSB) Instrument: Structural and Concurrent Validity

AbstractSchool bonding refers to the connections that students have with their schools and with various aspects of their academic environments, with an emphasis on close affective relationships and investment in doing well at school. The Perception of School Social Bonding (PSSB) instrument is a 10-item, self-report instrument with 3 subscales measuring different aspects of student school bonding: attachment; involvement; and belief. The instrument is grounded in Hirschi’s theory of social control and was evaluated for structural and concurrent validity in a sample of 3,507 students from mixed ethnicity, gender, and age groups. The 3-factor structure of the scale was supported; differences across gender and ethnic groups were identified; and some subscale scores related inversely to chronic school absenteeism. The brevity and sensitivity of the PSSB may make it particularly useful as a universal screening measure in multi-tiered systems of support models.