Control Theory Research Articles

Robust predictive fault-tolerant control under actuator random failures

A robust predictive fault-tolerant switching control method is presented for a class of industrial processes that are prone to random actuator failures. The primary contribution is the combination of stochastic control theory with robust predictive control to represent the occurrence of system failure in the form of probability, thus significantly enhancing the conventional fault-tolerant control approach. First, a state-space model of the system is established, which is then transformed into an enhanced form that includes state deviations and output tracking errors. This augmented model enables the formulation of fault-tolerant switching control laws with fault probabilities, ensuring system convergence and tracking capabilities. Subsequently, linear matrix inequality constraints are given to find the switching controller gains under recovery and fault probabilities. Under certain probabilities, failure triggers fault-tolerant control, while recovery prompts a shift back to conventional control. Final, we validate the feasibility of our approach with a case study involving pressure regulation.

Parametric Sensitivity Analysis of Stability Margins of Holos-Quad Microreactor

An analysis of the stability margins of the innovative Holos-Quad microreactor design is presented. This high-temeprature gas-cooled reactor (HTGR) system is designed to operate fully autonomously with passive safety mechanisms. Therefore, the inherent stability of the reactor is of great importance. Using a point-reactor model, which couples point kinetics to thermal-hydraulic heat balance equations and includes reactivity feedback effects of the fuel and moderator temperatures, the closed-loop transfer function of the reactor is derived. Applying the approach of linear systems and control theory, both the gain and phase margins of the Holos-Quad design are obtained. The analysis demonstrates that the design is stable, with an infinite gain margin and a finite phase margin. A parametric uncertainty quantification study is also performed using a total Monte Carlo approach. The stability of the reactor for different power levels, such as during reactor startup or load-following transients, is also explored. Finally, two sensitivity analysis methods are applied, namely, multiple regression (deriving standardized regression coefficients) and variance-based sensitivity analysis (known as the Sobol method), to study the contribution of each of the parameters to the stability margins’ uncertainty. This analysis improves our understanding of the role of each of the parameters in the stability of the reactor.

Comprehensive degree of nonlinearity in the dynamic response of high-speed Maglev train

The aim of this study is to propose a method for evaluating the comprehensive degree of nonlinearity in the dynamic response of high-speed maglev train system through mathematical modeling and numerical simulation. A model of a typical high-speed maglev vehicle is established based on the principles of maglev vehicle dynamics, and control models for the suspension magnet and the guide magnet are developed using principles from the electromagnetism and automatic control theory. The vibration responses of the vehicle system, including acceleration and electromagnetic forces are calculated using a fast numerical integration method. This calculation is performed for various control parameters and track harmonic excitations. The Hilbert-Huang transform (HHT) is adopted to decompose the response signal and derive the local mean frequency (amplitude) and instantaneous frequency (amplitude) of the response, based on which the Degree of Nonlinearity (DN) of the vibration response of a single component is obtained by calculating the deviation between the instantaneous frequency and the mean frequency. The Comprehensive Degree of Nonlinearity (CDN) of the vibration response in the maglev train system is proposed and defined by incorporating the DNs of the vertical and lateral vibrations of the carbody, bogie, suspension magnet and guide magnet, and by quantitatively evaluating them. The effects of the electromagnet control parameters and harmonic irregularities (wavelength and amplitude) of the track on the DN of each component and the CDN of the entire vehicle system are analyzed in detail. The analysis results show that the DN and CDN can demonstrate the extent of instability in the vehicle system and can be applied for safety warning on maglev vehicles.

Protective effects of ethnic enclaves: Testing pathways to alcohol use and use disorders in Mexican American young adults.

Ethnic enclave residence is associated with decreased risk for drinking and related problems, but less is known about the mechanisms that explain this association. Informed by theories of social control, we used a multilevel framework to examine whether negative attitudes toward drinking mediated associations between ethnic enclave residence (i.e., neighborhood linguistic isolation) and alcohol outcomes among Mexican American young adults (N = 628) in Southern California. Model 1 assessed mediation effects in the pathways from linguistic isolation to current drinking and alcohol use disorder (AUD). Model 2 adjusted for parental drinking attitudes and neighborhood alcohol availability. There were differential associations by gender in direct effects of linguistic isolation and negative drinking attitudes on both drinking and AUD. Among women only, linguistic isolation was related to greater abstinence and decreased AUD after accounting for social control proxies of parent attitudes and alcohol availability. Young adults' own drinking attitudes did not mediate relationships between linguistic isolation and alcohol outcomes. This study offers evidence on the importance of disaggregating Hispanic national groups by gender to uncover social mechanisms within ethnic enclave settings for tailored supports in reducing risk of drinking and alcohol-related harms.

Microflexiblity and local integrability of horizontal curves

AbstractLet be an analytic bracket‐generating distribution. We show that the subspace of germs that are singular (in the sense of control theory) has infinite codimension within the space of germs of smooth curves tangent to . We formalize this as an asymptotic statement about finite jets of tangent curves. This solves, in the analytic setting, a conjecture of Eliashberg and Mishachev regarding an earlier claim by Gromov about the microflexibility of the tangency condition.From these statements it follows, by an argument due to Gromov, that the ‐principle holds for maps and immersions transverse to .

A hybrid model for shop floor simulation and work-in-process feedback control

In this paper, a smart hybrid model for shop floor simulation and control is proposed based on dynamic modelling and control theory’s analytical tools. The model is smart because the production levels are automatically adjusted based on feedback loops, considering shop floor current states (i.e., it is a closed-loop model). It is hybrid because it deals with discrete and continuous transitions, and integrates two approaches: switched arrival systems (SAS) and bond graphs (BG). This novel integrated SAS + BG model is applied to a production system in the food industry and four production control rules (well-known from the operations management literature) are tested: pure push and pull rules and extended push and pull rules. The results showed that the SAS is effective to dynamically represent intermittent production systems, and its use, coupled with bond graphs, allows the definition of capacity adjustment and production control rules that are effective to control the work in process (WIP). The extended pull rule outperformed the remaining ones, corroborating prior literature. This paper contributes with a novel approach that is modular and can be extended to represent the closed-loop dynamics of different configurations of multi-product multi-station production systems, with a disaggregated level of detail.

Advancing orchestration synthesis for contract automata

Contract automata allow to formally define the behaviour of service contracts in terms of service offers and requests, some of which are moreover optional and some of which are necessary. A composition of contracts is said to be in agreement if all service requests are matched by corresponding offers. Whenever a composition of contracts is not in agreement, it can be refined to reach an agreement using the orchestration synthesis algorithm. This algorithm is a variant of the synthesis algorithm used in supervisory control theory and it is based on the fact that optional transitions are controllable, whereas necessary transitions are at most semi-controllable and cannot always be controlled. In this paper, we present advancements of the orchestration synthesis for contract automata. Notably, we identify the existing limits of the orchestration synthesis and propose a novel orchestration synthesis along with additional constructs to enhance the expressiveness and scalability of contract automata. The proposed advancements have been implemented and experimented on two case studies, one of which originates from the railway domain and the other is a card game.

Control of HPV infection leading to cervical cancer

HPV is the most common sexually transmitted infection and can lead to cancer. Vaccination has been considered as a very effective measure against HPV and along with regular screenings is recommended for the prevention of cervical cancer. In this study we propose and analyze a model for the transmission dynamics of Human Papilloma Virus (HPV). We discuss the optimal vaccination strategy in the case when multiple vaccines are available. We have considered an ODE based compartmental model, incorporating sex structure, we also consider HPV leading to cervical cancer by including pre-cancerous and cancerous compartments in the model. Adding the pre-cancerous and cancerous compartments will help us better understand the role of vaccination in prevention of cancers due to HPV. Using standard techniques from dynamical systems theory, we determine the disease free (DFE) and endemic steady states (EE). We determine a threshold quantity, the basic reproductive number R0 in terms of the model parameters, such that, the DFE is stable when R0<1 and the EE is stable in the case R0>1. The goal of control measures is to try an reduce R0 to be below the threshold value of 1, the regime in which the disease will ultimately be driven to extinction. We also prove the existence of a backward bifurcation in the model, epidemiologically this means that the long time behavior of the model depends on the initial infected population and that the disease may be harder to control, and the condition R0<1 may not suffice to eradicate the disease. Finally, using optimal control theory we consider the effective vaccination strategies using multiple vaccines, the goal is to study the best vaccination strategy using multiple vaccines. Our results show that a higher coverage with vaccines that provide protection against multiple strains, although more expensive, is best suited to control HPV transmission and subsequent development of cancer.

Method for adapting radioacoustic sounding systems of the atmosphere

Stations of radioacoustic sounding (RAS) of the atmosphere are a promising means of obtaining information about the altitudinal distribution of meteorological parameters in the Earth's atmosphere used in the process of solving current scientific and applied tasks to ensure aircraft flights, weather forecasting, etc. However, the effectiveness of the existing radio-acoustic means is insufficient, and there are practical needs for the development of appropriate prospective structures and algorithms, which will be implemented during the construction of specific stations designed to solve actual applied tasks. The article presents the synthesis of the RAS systems algorithm adaptation performed by changing the frequency of the sounding radio signal to fulfill the Bragg condition when the emitted acoustic pulse signal moves along the sounding path from the standpoint of optimal control theory. Since in the problem of synthesizing the algorithm for controlling the frequency of a radio signal, the disturbing and leading to violations of the Bragg condition during the propagation of an acoustic packet along the sounding path, as well as the process causing measurement errors, are considered as random, this problem is considered as a problem of stochastic optimal control. It is shown that in accordance with the separation theorem, known from the theory of optimal stochastic control, the method of controlling the frequency of a sounding radio signal should include sequentially peformed operations of forming estimates of the information parameter of the scattered radio signal, optimal linear filtering of the obtained estimates, and deterministic control of the frequency of the sounding radio signal.

Skin Comfort Sensation with Mechanical Stimulus from Electronic Skin.

The field of electronic skin has received considerable attention due to its extensive potential applications in areas including tactile sensing and health monitoring. With the development of electronic skin devices, electronic skin can be attached to the surface of human skin for long-term health monitoring, which makes comfort an essential factor that cannot be ignored in the design of electronic skin. Therefore, this paper proposes an assessment method for evaluating the comfort of electronic skin based on neurodynamic analysis. The holistic analysis framework encompasses the mechanical model of the skin, the modified Hodgkin-Huxley model for the transduction of stimuli, and the gate control theory for the modulation and perception of pain sensation. The complete process, from mechanical stimulus to the generation of pain perception, is demonstrated. Furthermore, the influence of different factors on pain perception is investigated. Sensation and comfort diagrams are provided to assess the mechanical comfort of electronic skin. The comfort assessment method proposed in this paper provides a theoretical basis when assessing the comfort of electronic skin.

Optimal control of sweeping processes in unmanned surface vehicle and nanoparticle modelling

ABSTRACT This paper addresses novel applications to practical modelling of the newly developed theory of necessary optimality conditions in controlled sweeping/Moreau processes with free time and pointwise control and state constraints. Problems of this type appear, in particular, in dynamical models dealing with unmanned surface vehicles (USVs) and nanoparticles. We formulate optimal control problems for a general class of such dynamical systems and show that the developed necessary optimality conditions for constrained free-time controlled sweeping processes lead us to designing efficient procedures to solve practical models of this class. Moreover, the paper contains numerical calculations of optimal solutions to marine USVs and nanoparticle models in specific situations. Overall, this study contributes to the advancement of optimal control theory for constrained sweeping processes and its practical applications in the fields of marine USVs and nanoparticle modelling.

Uncertain random optimal control model for deteriorating inventory with the finite horizon

This paper examines a deteriorating inventory model with combined pricing and production during a limited selling time in an uncertain random setting. Demand is influenced by market size and price, which are portrayed in terms of random variables. The uncertainty factors affecting the inventory are represented by multiple Liu processes. Inventories deteriorate by a certain percentage and back-orders for inventories are permitted. This study aims to determine the optimal level of production rate and pricing that maximizes the producer’s total expected profit through the application of uncertain random optimal control theory, which is based on uncertain theory and probability theory. Numerical examples are used to compare joint pricing dynamic production model with dynamic production model under an optimal static price, highlighting the benefits of implementing joint pricing dynamic production model. Furthermore, it is evident that an increase in deterioration rate results in decreased on-hand inventory, yet higher production rate and price. It should also be noted that different levels of probability expectation of demand can have an impact on production inventory problem.

Nonlinear modelling and design of synergetic controllers for single penstock multi-machine hydropower system

The existence of a shared pipeline in a multi-machine system can lead to hydraulic coupling effects between units. It increases the difficulty of controlling hydroelectric units and affects the stability of the power system. This paper refines the model of the hydroelectric generator unit and builds a nonlinear model of a hydro-generator unit with the single penstock multi-machine system. The synergetic control theory was introduced, and the governor and excitation system controllers were designed separately. A joint synergistic controller is obtained by analyzing the two systems' synergistic factors and controlled characteristics. This controller realizes the joint control of the excitation and governor system. The simulation selected a proportion-integral-derivative (PID) controller with optimized parameters, synergistic governor controller (SEC), and synergistic excitation controller (SGC) for comparison to verify the effectiveness of the proposed method. The results indicate that the proposed control method exhibits competent control effects in the single penstock multi-machine system. It can reduce the impact of hydraulic coupling, shorten the adjusting time, and ensure the fast and stable operation of the system. In addition, the values of system time parameters and synergetic control parameters are analyzed separately to provide a reference for achieving optimal control.

Causes and impacts of domestic violence against women: Cases in Indonesia

Domestic violence against women is still a common phenomenon in Indonesia. Data from Komnas Perempuan 2021 showed from 8,234 cases of violence against women handled by its partner institutions, 6,480 cases (79%) were happened at home. 3,221 cases of them (50%) are violence against wives. This article aims to uncover the causes and effects of domestic violence against women in Indonesia, what specific patterns are implicit in these causes, expressed by both perpetrators and victims, that could lead to a particular theory of domestic violence, and what perpetrators do to victims and what the consequences are. Using data gathered from 27 informants and 86 online news, this research finds that there are various causes of domestic violence. These causes lead to specific pattern that strengthen the use of social theories, in particular control, resource, exosystem and social isolation theories. Various forms of domestic violence done by offenders from light to heavy hits using bare hands or other tools. The impact of domestic violence can range from injury to death of the victim.

Strong-coupling limits induced by weak-coupling expansions

A method is described for the extrapolation of perturbative expansions in powers of asymptotically small coupling parameters or other variables onto the region of finite variables and even to the variables tending to infinity. The method involves the combination of ideas from renormalization group theory, approximation theory, dynamical theory, and optimal control theory. The extrapolation is realized by means of self-similar factor approximants, whose control parameters can be uniquely defined. The method allows to find the large-variable behavior of sought functions knowing only their small-variable expansions. Convergence and accuracy of the method are illustrated by explicit examples, including the so-called zero-dimensional field theory and anharmonic oscillator. Strong-coupling behavior of Gell-Mann–Low functions in multicomponent field theory, quantum electrodynamics, and quantum chromodynamics is found, being based on their weak-coupling perturbative expansions.

Socially adaptive cognitive architecture for human-robot collaboration in industrial settings.

This paper introduces DAC-HRC, a novel cognitive architecture designed to optimize human-robot collaboration (HRC) in industrial settings, particularly within the context of Industry 4.0. The architecture is grounded in the Distributed Adaptive Control theory and the principles of joint intentionality and interdependence, which are key to effective HRC. Joint intentionality refers to the shared goals and mutual understanding between a human and a robot, while interdependence emphasizes the reliance on each other's capabilities to complete tasks. DAC-HRC is applied to a hybrid recycling plant for the disassembly and recycling of Waste Electrical and Electronic Equipment (WEEE) devices. The architecture incorporates several cognitive modules operating at different timescales and abstraction levels, fostering adaptive collaboration that is personalized to each human user. The effectiveness of DAC-HRC is demonstrated through several pilot studies, showcasing functionalities such as turn-taking interaction, personalized error-handling mechanisms, adaptive safety measures, and gesture-based communication. These features enhance human-robot collaboration in the recycling plant by promoting real-time robot adaptation to human needs and preferences. The DAC-HRC architecture aims to contribute to the development of a new HRC paradigm by paving the way for more seamless and efficient collaboration in Industry 4.0 by relying on socially adept cognitive architectures.

Particularities of Rotorcraft in Dealing with Advanced Controllers

Advanced nonlinear controllers are a desirable solution to rotorcraft flight control as they can solve the system high nonlinear dynamic behavior. However, conventional nonlinear controllers such as Nonlinear Dynamic Inversion (NDI) controller heavily rely on the availability of accurate model knowledge and this can be problematic for rotorcraft. Therefore, incremental control theory can solve the modelling errors sensitivity by relying on the information obtained from the sensors instead. The paper applied the Incremental Nonlinear Dynamic Inversion (INDI) controller to rotorcraft case. It will be demonstrated that, for rotorcraft, the incremental nonlinear controllers depend on the delays introduced in the controller by the rotor dynamics. To correct this behaviour, residualization and synchronization methods need to be applied accordingly in order to remove the effects of rotor flapping (disctilt) dynamics from the controller. These particularities of rotorcraft in dealing with advanced controllers shows that incremental nonlinear controllers can have relatively small stability robustness margin and careful controller design is needed in order to account properly for rotorcraft time delays and unmodelled dynamics.

Adaptive synergetic control for electronic throttle valve system

AbstractThis study has developed adaptive synergetic control (ASC) algorithm to control the angular position of moving plate in the electronic throttle valve (ETV) system. This control approach is inspired by synergetic control theory. The adaptive controller has addressed the problem of variation in systems parameters. The control design includes two elements: the control law and adaptive law. The adaptive law is developed based on Lyupunov stability analysis of the controlled system, and it is responsible for estimating the potential uncertainties in the system. The effectiveness of the proposed adaptive synergetic control has been verified by numerical simulation using MATLAB/Simulink. The results showed that the ASC algorithm could give good tracking performance in the presence of uncertainty perturbations. In addition, a comparison study has been made to compare the tracking performance of ASC and that based on conventional synergetic control (CSC) for the ETV system. The simulated results showed that the performance of ASC outperforms that based on CSC. Moreover, the results showed that the estimation errors between the actual and estimated uncertainties are bounded and there is no drift in the developed adaptive law of ASC.

Edge-centric network control on the human brain structural network

Abstract Network control theory models how gray matter regions transition between cognitive states through associated white matter connections, where controllability quantifies the contribution of each region to driving these state transitions. Current applications predominantly adopt node-centric views and overlook the potential contribution of brain network connections. To bridge this gap, we use edge-centric network control theory (E-NCT) to assess the role of brain connectivity (i.e., edges) in governing brain dynamic processes. We applied this framework to diffusion MRI data from individuals in the Human Connectome Project. We first validate edge controllability through comparisons against null models, node controllability, and structural and functional connectomes. Notably, edge controllability predicted individual differences in phenotypic information. Using E-NCT, we estimate the brain’s energy consumption for activating specific networks. Our results reveal that the activation of a complex, whole-brain network predicting executive function (EF) is more energy efficient than the corresponding canonical network pairs. Overall, E-NCT provides an edge-centric perspective on the brain’s network control mechanism. It captures control energy patterns and brain-behavior phenotypes with a more comprehensive understanding of brain dynamics.

Research and Experiment on Cruise Control of a Self-Propelled Electric Sprayer Chassis

In order to address the issues of poor stability in vehicle speed and deteriorated spraying quality caused by changes in road slope and the decrease in overall mass due to liquid spraying, this study focuses on analyzing the structure and longitudinal dynamic characteristics of a 4WID high ground clearance self-propelled electric sprayer. By utilizing MATLAB/Simulink software, three subsystems, namely, the inverse longitudinal dynamics model, torque distribution model, and motor model, are established. The model takes into account the effects of longitudinal driving resistance, slope, and vehicle roll angle on the distribution of loads among the four wheels during slope driving. A seven-degrees-of-freedom dynamic model is developed. A hierarchical control structure is designed, incorporating an upper-level PID controller and a lower-level fuzzy PID controller, to control the overall system. The control algorithms are tailored to the specific characteristics of the sprayer’s operation, and simulation experiments are conducted under the corresponding operating conditions. Building upon this, a sensor-equipped experimental platform is set up in the self-propelled sprayer manufactured by the team in the preliminary stage. Real vehicle tests are conducted in two scenarios: transition transportation and field operations, with the evaluation of the overall vehicle speed serving as the performance metric to validate the correctness of the model and the control theory.