Control Theory Research Articles

Application of fuzzy logic control theory combined with target tracking algorithm in unmanned aerial vehicle target tracking

This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance.

Parameterized evasion strategy for hypersonic glide vehicles against two missiles based on reinforcement learning

In practical combat scenarios, Hypersonic Glide Vehicles (HGV) face the challenge of evading Successive Pursuers from the Same Direction while satisfying the Homing Constraint (SPSDHC). To address this problem, this paper proposes a parameterized evasion guidance algorithm based on reinforcement learning. The three-player optimal evasion strategy is firstly analyzed and approximated by parametrization. The switching acceleration command of HGV optimal evasion strategy considering the upper limit of missile acceleration command is analyzed based on the optimal control theory. The terminal miss of HGV in the case of evading two missiles is analyzed, which means that the three-player optimal evasion strategy is a linear combination of two one-to-one strategies. Then, a velocity control algorithm is proposed to increase the terminal miss by actively controlling the flight speed of the HGV based on the parametrized evasion strategy. The reinforcement learning method is used to implement the strategy in real time and a reward function is designed by deducing homing strategy for the HGV to approach the target, which ensures that the HGV satisfies the homing constraint. Experimental results demonstrate the feasibility and robustness of the proposed parameterized evasion strategy, which enables the HGV to generate maximum terminal miss and satisfy homing constraint when facing single or double missiles.

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

Affect 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 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.

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.

Problems for Selection Problems: Comments on Wayne Wu's Movements of the Mind

Early in Movements of the Mind, Wayne Wu puts forth a foundational picture of action. On this picture, intentional action is necessarily a solution to a selection problem, a problem of choice among multiple causally possible alternatives. Forming an intention solves one selection problem; acting on that intention requires solving yet further selection problems about how to execute that intention. There are two serious issues with this picture of action. First: some intentional actions are causally necessitated. They can't be solutions to selection problems. Wu's argument for this claim, which contrasts reflex with action, does not succeed in showing that action must solve a selection problem — nor that reflex does not solve such a problem. Second: the requirement to solve further selection problems while acting on an intention leads to a vicious regress that threatens the possibility of action in general. If these two issues are addressed, Wu's groundbreaking scientific approach to mental action might offer us a powerful new theory of ongoing causal control over intentional action.

Focus on the disruption of networks and system dynamics.

Networks are designed to ensure proper functioning and sustained operability of the underlying systems. However, disruptions are generally unavoidable. Internal interactions and external environmental effects can lead to the removal of nodes or edges, resulting in unexpected collective behavior. For instance, a single failing node or removed edge may trigger a cascading failure in an electric power grid. This Focus Issue delves into recent advances in understanding the impacts of disruptions on networks and their system dynamics. The central theme is the disruption of networks and their dynamics from the perspectives of both data-driven analysis as well as modeling. Topics covered include disruptions in the dynamics of empirical systems such as nuclear reaction networks, infrastructure networks, social networks, epidemics, brain dynamics, and physiology. Emphasis is placed on various phenomena in collective behavior, including critical phase transitions, irregular collective dynamics, complex patterns of synchrony and asynchrony, chimera states, and anomalous oscillations. The tools used for these studies include control theory, diffusion processes, stochastic processes, and network theory. This collection offers an exciting addition to the evolving landscape of network disruption research.

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.

Robust control of charged particles position in a homogeneous isotropic turbulence using an electric field

This research investigates the position control of spherical charged particles suspended in a directly simulated homogeneous isotropic turbulence, which has the potential to improve the efficiency of the electrostatic precipitators (ESPs) in removing gaseous pollutants. To do this, the electric field used in ESPs is controlled by the sliding mode control method to steer the particles toward their desired positions. The particles are charged using the field charging method, which leads to electrical interactions between them. Employing the linked-list method reduces the computational cost related to these interaction forces. The dynamics of particles is simulated using the Maxey–Riley equation including the control force. Coupling the direct numerical simulation solver with the proposed control method and the linked-list scheme, along with solving the particle dynamics, complicates the problem from a control theory point of view. The robustness of the proposed model-free control method leads to overcoming disturbances and uncertainties in the simulation due to turbulence and repulsive electric forces. Hence, steering a large number of charged particles to places with more pollution concentration or increasing the duration of exposure of the particles to gaseous pollutants becomes possible. The findings of the present research are pertinent to improving the performance of air purification equipment.

Sleep Deprivation Effects on Brain State Dynamics Are Associated With Dopamine D2 Receptor Availability Via Network Control Theory

BackgroundSleep deprivation (SD) negatively affects brain function. Most brain imaging studies have investigated the effects of SD on static brain function. SD effects on functional brain dynamics and their relationship with molecular changes remain relatively unexplored. MethodsWe used functional magnetic resonance imaging to examine resting-brain state dynamics after one night of SD compared with rested wakefulness (N = 41) and assessed the association of brain state dynamics with striatal brain dopamine D2 receptor availability measured by positron emission tomography [11C]raclopride using network control theory. ResultsSD reduced dwell time and persistence probabilities, with the strongest effects in two brain states, one characterized by high default mode network and low dorsal attention network activity and the other by high frontoparietal network and low somatomotor network activity. Using network control theory, we showed that after SD, there was an overall increase in the control energy required for brain state transitions, with effects varying for different brain state transitions. Control energy requirement was negatively associated with transition probabilities under SD and restful wakefulness and accounted for SD-induced changes in transition probabilities. Alteration in the energy landscape was associated with SD-induced changes in striatal D2 receptor distribution. ConclusionsThese findings demonstrate altered occurrence of internally and externally oriented brain states following acute SD and suggest an association with energy requirements for brain state transitions modulated by striatal D2 receptors.

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

Impact speed and angle constrained guidance law for unpowered gliding vehicle

In this paper, a novel guidance law is proposed which can achieve the desired impact speed and angle simultaneously for unpowered gliding vehicles. A guidance law with only impact angle constraint is used to produce the guidance profile, and its convergence in the varying speed scenario is proved. A relationship between flight states, guidance input and impact speed is established. By applying the fixed-time convergence control theory of error dynamics, an impact speed corrector is built with the above guidance profile, which can implement impact speed correction without affecting the impact angle constraint. Numerical simulations with various impact speed and angle constraints are conducted to demonstrate the performance of the proposed guidance law, and the robustness is also verified by Monte Carlo tests.

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.