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Warnings about the risks of literal-minded automation—a system that can't tell if its model of the world is the world it is actually in—have been sounded for over 70 years. The risk is that a system will do the "right" thing—its actions are appropriate given its model of the world, but it is actually in a different world—producing unexpected/unintended behavior and potentially harmful effects. This risk—wrong, strong, and silent automation—looms larger today as our ability to deploy increasingly autonomous systems and delegate greater authority to such systems expands. It already produces incidents, outages of valued services, financial losses, and fatal accidents across different settings. This paper explores this general and out-of-control risk by examining a pair of fatal aviation accidents which revolved around wrong, strong and silent automation. [ABSTRACT FROM AUTHOR]

Finite-State Automata (FSA) are foundations for modelling, synthesis, verification, and implementation of controllers for manufacturing systems. However, FSA are limited to represent emerging features in manufacturing, such as the ability to recognise and switch contexts. One option is to enrich FSA with parameters that carry details about the manufacturing, which may favour design and control. A parameter can be embedded either on transitions or states of an FSA, and each approach defines its own modelling framework, so that their comparison and integration are not straight-forward, and they may lead to different control solutions, modelled, processed and implemented distinctly. In this paper, we show how to combine advantages from parameters in manufacturing the modelling and control. We initially presenta background that allows to understand each parameterisation strategy. Then, we introduce a conversion method that translates a design-friendly model into a synthesis-efficient structure. Finally, we use the converted models is synthesis, highlighting their advantages. Examples are used throughout the paper to illustrate and compare our results and tooling support is also provided. [ABSTRACT FROM AUTHOR]

Objective: This on-road study employed behavioral and neurophysiological measurement techniques to assess the influence of six weeks of practice driving a Level 2 partially automated vehicle on driver workload and engagement. Background: Level 2 partial automation requires a driver to maintain supervisory control of the vehicle to detect "edge cases" that the automation is not equipped to handle. There is mixed evidence regarding whether drivers can do so effectively. There is also an open question regarding how practice and familiarity with automation influence driver cognitive states over time. Method: Behavioral and neurophysiological measures of driver workload and visual engagement were recorded from 30 participants at two testing sessions—with a six-week familiarization period in-between. At both testing sessions, participants drove a vehicle with partial automation engaged (Level 2) and not engaged (Level 0) on two interstate highways while reaction times to the detection response task (DRT) and neurophysiological (EEG) metrics of frontal theta and parietal alpha were recorded. Results: DRT results demonstrated that partially automated driving placed more cognitive load on drivers than manual driving and six weeks of practice decreased driver workload—though only when the driving environment was relatively simple. EEG metrics of frontal theta and parietal alpha showed null effects of partial automation. Conclusion: Driver workload was influenced by level of automation, specific highway characteristics, and by practice over time, but only on a behavioral level and not on a neural level. Application: These findings expand our understanding of the influence of practice on driver cognitive states under Level 2 partial automation. [ABSTRACT FROM AUTHOR]

The supervisory control of Petri nets aims to enforce the undesired behavior as unreachable by designing a set of control places. This work presents a set cover approach to design maximally permissive supervisors. For each first-met bad marking, an integer linear programming problem is developed to obtain a control place to prohibit it. An objective function is formulated to make the maximal number of first-met bad markings forbidden. Then, we develop a set covering approach to minimize the number of selected control places. The proposed approach can guarantee the maximal permissiveness of the obtained supervisor and provide a trade-off between structural complexity and computational cost. Several examples are considered to validate the proposed method. [ABSTRACT FROM AUTHOR]

Introduction: In the 1950s and 1960s, John Senders carried out a number of influential experiments on the monitoring of multidegree-of-freedom systems. In these experiments, participants were tasked with detecting events (threshold crossings) for multiple dials, each presenting a signal with different bandwidth. Senders’ analyses showed a nearly linear relationship between signal bandwidth and the amount of attention paid to the dial, and he argued that humans sample according to bandwidth, in line with the Nyquist–Shannon sampling theorem. Objective: The current study tested whether humans indeed sample the dials based on bandwidth alone or whether they also use salient peripheral cues. Methods: A dial-monitoring task was performed by 33 participants. In half of the trials, a gaze-contingent window was used that blocked peripheral vision. Results: The results showed that, without peripheral vision, humans do not effectively distribute their attention across the dials. The findings also suggest that, when given full view, humans can detect the speed of the dial using their peripheral vision. Conclusion: It is concluded that salience and bandwidth are both drivers of distributed visual attention in a dialmonitoring task. [ABSTRACT FROM AUTHOR]

Summary: Supervisory control has been shown to be a very effective approach to adaptive control which ensures step‐tracking, exponential stability, and a degree of robustness to unmodeled dynamics. Here we apply the technique to the classical d$$ d $$‐step‐ahead adaptive control problem: we not only prove exponential stability and tracking of a general bounded reference signal, but also a never‐before‐seen linear‐like convolution bound on the effect of the noise/disturbance. This latter property is then leveraged to not only prove robustness to a degree of unmodeled dynamics, but also to a never‐before‐seen tolerance to a degree of nonlinearities and to slow (on average) plant parameter variations. [ABSTRACT FROM AUTHOR]

This paper considers modeling and state estimation of timed discrete-event systems (TDESs) with communication delays and losses. In the previous framework, the specified closed-loop system language may contain physically impossible strings, i.e. strings never occur in practice. To exclude these physically impossible strings, this paper presents a new modeling framework for supervisory control of networked TDESs. Specifically, we first analyse how to model and update the observation channel and the control channel under communication delays and losses. We then construct an automaton that explicitly describes the interaction process between the plant and the supervisor over the observation channel and the control channel so that the language of the closed-loop system can be specified. Compared with the existing work, the proposed framework can model the 'dynamics' of the closed-loop system more accurately. Under the proposed framework, we further estimate states of the closed-loop system under communication delays and losses. The state estimation is implemented on-the-fly and bases on only the information of observations and controls in history. As an application of the proposed modeling and state estimation approaches, we finally extend the existing approaches to find all the admissible and safe supervisors of networked TDESs. [ABSTRACT FROM AUTHOR]

Abstract This work investigates quantitative supervisory control of discrete event systems modeled with Petri nets under the fixed‐initial‐credit energy objective. A weight function referred to as an energy function is defined on a Petri net to characterize the energy level of a transition. The proposed fixed‐initial‐credit energy problem aims to design a supervisor such that the energy level of a transition sequence in a supervised system is higher than 0 under a given initial energy level. The problem is eventually transformed into a two‐player game between a system and a supervisor; supervisor synthesis is reduced to finding a winning strategy in the two‐player game. Instead of enumerating the complete state space of the underlying Petri net, two information structures are utilized, namely the conventional basis reachability graph and the newly proposed essential marking graph, to construct two‐player games based on each of them. It is shown that a winning strategy for a supervisor decoded from the game based on the basis reachability graph of the Petri net is a solution to the problem but is in general restrictive. Further, it is shown that the set of strategies for a supervisor in the game based on the essential marking graph is consistent with that from the game based on the reachability graph of a Petri net. The two developed approaches do not require an exhaustive exploration of the state space of a plant, thus achieving higher efficiency.

The article is devoted to the development of an approach to solving the main problems of the theory of supervisory control of logical discrete-event systems (DES), based on their representation in the form of positively constructed formulas (PCF). We consider logical DESs in automata form, understood as generators of some regular languages. The PCF language is a complete first-order language, the formulas of which have a regular structure of alternating type quantifiers and do not contain a negation operator in the syntax. It was previously proven that any formula of the classical first-order predicate calculus can be represented as a PCF. PCFs have a visual tree representation and a natural question-and-answer procedure for searching for an inference using a single inference rule. It is shown how the PCF calculus, developed in the 1990s to solve some problems of control of dynamic systems, makes it possible to solve basic problems of the theory of supervisory control, such as checking the criteria for the existence of supervisory control, automatically modifying restrictions on the behavior of the controlled system, and implementing a supervisor. Due to some features of the PCF calculus, it is possible to use a non-monotonic inference. It is demonstrated how the presented PCF-based method allows for additional event processing during inference. The Bootfrost software system, or the so-called prover, designed to refute the obtained PCFs is also presented, and the features of its implementation are briefly described. As an illustrative example, we consider the problem of controlling an autonomous mobile robot.

This work investigates quantitative supervisory control of discrete event systems modeled with Petri nets under the fixed‐initial‐credit energy objective. A weight function referred to as an energy function is defined on a Petri net to characterize the energy level of a transition. The proposed fixed‐initial‐credit energy problem aims to design a supervisor such that the energy level of a transition sequence in a supervised system is higher than 0 under a given initial energy level. The problem is eventually transformed into a two‐player game between a system and a supervisor; supervisor synthesis is reduced to finding a winning strategy in the two‐player game. Instead of enumerating the complete state space of the underlying Petri net, two information structures are utilized, namely the conventional basis reachability graph and the newly proposed essential marking graph, to construct two‐player games based on each of them. It is shown that a winning strategy for a supervisor decoded from the game based on the basis reachability graph of the Petri net is a solution to the problem but is in general restrictive. Further, it is shown that the set of strategies for a supervisor in the game based on the essential marking graph is consistent with that from the game based on the reachability graph of a Petri net. The two developed approaches do not require an exhaustive exploration of the state space of a plant, thus achieving higher efficiency. [ABSTRACT FROM AUTHOR]

Industrial control systems (ICS) require system design and operation under cyber-attacks. This study aims to design a fallback control system that can switch from normal control to fallback control and verify its superiority. The target system is a factory automation (FA) system consisting of a normal programmable logic controller (PLC) and a fallback PLC. In this system, we design a fallback logic that takes over control in the case of cyber-attacks. The design of this logic requires a system model that manages smooth state transitions between normal control and fallback control in an integrated manner under cyber-attacks. In response, we model a control program in the framework of discrete event systems (DESs) and apply supervisory control to derive a supervisor model that can manage the system in an integrated manner. To ensure the controllability of the FA system during cyber-attacks, we design a control specification that includes a detection function to enable rapid switching of the control state. As a result, we generate the fallback logic from the supervisor model with guaranteed controllability under cyber-attacks and implement it in a fallback PLC to verify the effectiveness of the proposed logic. [ABSTRACT FROM AUTHOR]

This paper establishes a connection between control theory for partially observed discrete-event systems (DESs) and automated theorem proving (ATP) in the calculus of positively constructed formulas (PCFs). The language of PCFs is a complete first-order language providing a powerful tool for qualitative analysis of dynamical systems. Based on ATP in the PCF calculus, a new technique is suggested for checking observability as a property of formal languages, which is necessary for the existence of supervisory control of DESs. In the case of violation of observability, words causing a conflict can also be extracted with the help of a specially designed PCF. With an example of the problem of path planning by a robot in an unknown environment, we show the application of our approach at one of the levels of a robot control system. The prover Bootfrost developed to facilitate PCF refutation is also presented. The tests show positive results and perspectives for the presented approach. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive exploration of an integrated Buck-Boost converter and Sliding Mode Control (SMC) Maximum Power Point Tracking (MPPT) system for optimizing photovoltaic energy conversion. The study focuses on enhancing solar energy extraction efficiency, regulating output currents, and ensuring effective battery utilization. Through a systematic analysis of converter component sizing and operational modes, the paper delves into the intricacies of the Buck-Boost converter. The unique contribution lies in the innovative integration of SMC with the traditional Perturb and Observe (P&O) algorithm, providing robust and adaptive MPPT under varying environmental conditions. Additionally, the paper introduces a battery management system with three distinct modes, namely, Charging, Direct, and Discharging, offering intelligent control over critical scenarios. Simulation results underscore the robustness of the proposed system under diverse conditions, demonstrating its effectiveness in managing power distribution based on battery charge levels, even in scenarios of insufficient solar power. Overall, this research significantly contributes to advancing the understanding of PV/battery systems and offers a practical, sustainable solution for optimizing energy production, distribution, and storage, marking a substantial stride towards a more efficient and sustainable energy future. [ABSTRACT FROM AUTHOR]

Central banks want adequate control and supervision of credit because of the substantial credit risks they face, which may even affect the reputation and faith of the banking system as a whole. Central banks ' supervisory monitoring of banks has various advantages, including ensuring the financial stability of banks as a whole, stabilizing the overall level of prices, as well as safeguarding the rights and finances of depositors, shareholders and investors. The main role of supervisory oversight is to control credit by controlling it from excessive expansion in periods of economic inflation or its activation, and to improve and expand credit currencies in situations of economic recession, or when central banks wish to support the national economy or a sector of various economic sectors. Financial indicators provided by the Central Bank have been used to control government and commercial institutions operating under its supervision. These indicators have been used to a number of private banks, including Egypt, Baghdad and Babylon . The research has reached several important conclusions, including that the supervisory oversight of central banks has an important role to play in protecting the banking system as a whole and maintaining public and private funds at the same time in accordance with their laws, instructions and practices. Central banks have their own methods by which they can manage and control the level of bank credit. At the same time, central banks can identify other solutions that can increase credit activity by changing techniques of surveillance and the use of advanced electronic tools. As a result of the application of these indicators, there is a weak correlation between the supervisory oversight of the Central Bank in stimulating the credit policies of the private commercial banking sector and the fact that banks do not have in-depth plans to stimulate bank credit at the same level as they do in maximizing profits by relying on another source of profit, namely, the foreign exchange window and foreign transfers. [ABSTRACT FROM AUTHOR]

A power distribution system is usually composed of a number of distribution substations and distribution lines, where the latter are fragile and often suffer from faults, leading to serious local power shortages. For all distribution substations, it is complex and costly to diagnose and locate faulty input lines from the multiple input lines of distribution substations. This paper reports a Petri net strategy to diagnose the faulty input lines to prevent local power shortages. The structure of a supervisor is proposed to detect and locate the faulty input lines by supervising the variations of power loads that are stored in a battery energy storage system. The stored power loads can be uninterruptedly distributed to the output lines during the fault diagnosis processes. Moreover, there are other auxiliary distribution substations that can urgently supply power loads to a distribution substation once the faulty input lines of the distribution substation are diagnosed. The local power shortages of the distribution substation are prevented during the fault diagnosis processes. In addition, the structures of power distribution systems and their control systems are formally defined by using Petri nets. The derivation processes of fault detection and location strategies are verified by the mathematical methods of Petri nets. Finally, a case study is presented.

This paper presents a unified multi-timescale control approach for a power system with distributed energy resources to achieve cyber-resilient operation. The proposed concept combines two cyber-resilient control methods: proactive and reactive methods. The proactive method uses a blockchain that ensures measurement and control data can be securely exchanged among grid assets and also derives control set points as a load-sharing supervisory control, with an embedded logic called chaincode. The proactive method ensures data integrity, but it inherits stochastic latency with significant standard deviation due to the nature of the distributed ledgers and security measures, leading to challenges in control. To overcome this trade-off, the reactive approach uses event-driven communication. For this approach, rather than communicating the complete data, a lightweight data packet is communicated in a peer-to-peer fashion. Therefore, it guarantees driving the system into a stable operation in case the proactive operation degrades with insufficient latency. To validate the concept, Hyperleger Fabric blockchain 2.2 is used to characterize the latency and is customized for an inverter control system in this study. Based on the use case, a stability analysis is presented to evaluate the impact of the variable delay and to identify the need for a reactive approach to mitigate the effects of a prolonged delay in the proactive method. A test bed with two hardware inverter prototypes and a custom blockchain programmed with the unified method is developed for validation. A set of hardware experimental results validates the methodology and demonstrates the inverter system operations achieving frequency recovery and load-sharing restoration based on the unified control method.

Abstract Vehicle automation is becoming more prevalent. Understanding how drivers use this technology and its safety implications is crucial. In a 6–8 week naturalistic study, we leveraged a hybrid naturalistic driving research design to evaluate driver behavior with Level 2 vehicle automation, incorporating unique naturalistic and experimental control conditions. Our investigation covered four main areas: automation usage, system warnings, driving demand, and driver arousal, as well as secondary task engagement. While on the interstate, drivers were advised to engage Level 2 automation whenever they deemed it safe, and they complied by using it over 70% of the time. Interestingly, the frequency of system warnings increased with prolonged use, suggesting an evolving relationship between drivers and the automation features. Our data also revealed that drivers were discerning in their use of automation, opting for manual control under high driving demand conditions. Contrary to common safety concerns, our data indicated no significant rise in driver fatigue or fidgeting when using automation, compared to a control condition. Additionally, observed patterns of engagement in secondary tasks like radio listening and text messaging challenge existing assumptions about automation leading to dangerous driver distraction. Overall, our findings provide new insights into the conditions under which drivers opt to use automation and reveal a nuanced behavioral profile that emerges when automation is in use.

This article is concerned with the problem of stabilising nonlinear uncertain systems subject to signal quantisation using nonlinear sector design. The boundary of nonlinear sector decided by the behaviour of the system showing an input-to-state stability is equivalent to $ \mathcal {KL} $ K L -stability. A novel control design based on supervisory-based quantised hands-off control is introduced. With this, the true state of the nonlinear plant is estimated and the quantised control steers the trajectory of the system inside the nonlinear sector in finite time and further to the origin asymptotically. The proposed approach is validated through an illustrative example to demonstrate its theoretical effectiveness. [ABSTRACT FROM AUTHOR]

Vehicle automation is becoming more prevalent. Understanding how drivers use this technology and its safety implications is crucial. In a 6–8 week naturalistic study, we leveraged a hybrid naturalistic driving research design to evaluate driver behavior with Level 2 vehicle automation, incorporating unique naturalistic and experimental control conditions. Our investigation covered four main areas: automation usage, system warnings, driving demand, and driver arousal, as well as secondary task engagement. While on the interstate, drivers were advised to engage Level 2 automation whenever they deemed it safe, and they complied by using it over 70% of the time. Interestingly, the frequency of system warnings increased with prolonged use, suggesting an evolving relationship between drivers and the automation features. Our data also revealed that drivers were discerning in their use of automation, opting for manual control under high driving demand conditions. Contrary to common safety concerns, our data indicated no significant rise in driver fatigue or fidgeting when using automation, compared to a control condition. Additionally, observed patterns of engagement in secondary tasks like radio listening and text messaging challenge existing assumptions about automation leading to dangerous driver distraction. Overall, our findings provide new insights into the conditions under which drivers opt to use automation and reveal a nuanced behavioral profile that emerges when automation is in use. Key findings: Drivers were less likely to use automation when roadway demands were higher. Secondary task engagements did not alarmingly change with automation usage (i.e., we only observed an increase in radio listening with Automation-L2). Automation usage alarms increased over time suggesting that drivers adopt a more relaxed interaction strategy with practice. The use of automation did not, by itself, increase fatigue or fidgeting. Rather, drivers used automation when they were already at risk of fatigue (i.e., during situations of low driving demand). Naturalistic Driving Research may benefit from true experimental control, especially in cases where driver behavior is contextually dependent (e.g., drivers may choose to use Automation only when they feel it is safe to do so). [ABSTRACT FROM AUTHOR]

In the realm of data protection strategies, differential privacy ensures that unauthorized entities cannot reconstruct original data from system outputs. This study explores discrete event systems, specifically through probabilistic automata. Central is the protection of state data, particularly the initial state privacy of multiple starting states. We introduce an evaluation criterion to safeguard initial states. Using advanced algorithms, the proposed method counters the probabilistic identification of any state within this collection by adversaries from observed data points. The efficacy is confirmed when the probability distributions of data observations tied to these states converge. If a system's architecture does not meet state differential privacy demands, we propose an enhanced supervisory control mechanism. This control upholds state differential privacy across all initial states, maintaining operational flexibility within the probabilistic automaton framework. Concluding, a numerical analysis validates the approach's strength in probabilistic automata and discrete event systems. [ABSTRACT FROM AUTHOR]

Summary: Standalone DC microgrids are progressively becoming explored in the modern power system. Proper voltage regulation and effective power distribution among sources are the main challenges for DC microgrid operation. A supervisory coordinated control strategy for proportionate power‐sharing and enhanced voltage regulation is presented in this paper to overcome these challenges. The formation of multiple power pools on the basis of available power from each renewable energy source (RES) is a crucial element of this approach. It modifies the power sharing between power pools and individual sources based on the amount of power available in the sources. In addition, for effective power management, it coordinates the operation of all power pools with all RESs. Also, by reducing the charging and discharging cycle of energy storage systems (ESSs), it is possible to make effective use of ESSs. The proposed control adapts to intermittent RESs and changes in the microgrid network. The proposed method's capabilities are validated by experimental results. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive exploration of an integrated Buck-Boost converter and Sliding Mode Control (SMC) Maximum Power Point Tracking (MPPT) system for optimizing photovoltaic energy conversion. The study focuses on enhancing solar energy extraction efficiency, regulating output currents, and ensuring effective battery utilization. Through a systematic analysis of converter component sizing and operational modes, the paper delves into the intricacies of the Buck-Boost converter. The unique contribution lies in the innovative integration of SMC with the traditional Perturb and Observe (P&O) algorithm, providing robust and adaptive MPPT under varying environmental conditions. Additionally, the paper introduces a battery management system with three distinct modes, namely, Charging, Direct, and Discharging, offering intelligent control over critical scenarios. Simulation results underscore the robustness of the proposed system under diverse conditions, demonstrating its effectiveness in managing power distribution based on battery charge levels, even in scenarios of insufficient solar power. Overall, this research significantly contributes to advancing the understanding of PV/battery systems and offers a practical, sustainable solution for optimizing energy production, distribution, and storage, marking a substantial stride towards a more efficient and sustainable energy future.

A self-tuning hierarchical controller in which a Fuzzy logic controller supervises the control actions of a conventional PID has been proposed, implemented and presented in this paper. The controller has been applied to a control study of Fluid Catalytic Cracking unit (FCCU) riser temperature, and regenerator temperature respectively. Comparison between the performance of the proposed Fuzzy-PID controller and the conventional PID was made in simulation studies of regulatory and servo performances of the two controller types. Six performance measures: Percent overshoot (OS), settling time (ST), integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE) and integral time square error (ITSE) were employed as the tools for performance comparison between the conventional PID and the Fuzzy-PID controller. For the tracking of riser temperature with a set point at 524oC, the performance indicators under PID control gave the following results overshoot (14.5%); settling time (40 seconds) Integral absolute error (8.246), integral square error (3.3); integral time absolute error(1762);integral time square error (43.77) while for the same indicators under Fuzzy-PID control the following values: overshoot (3.3%); settling time (40 seconds) ;Integral absolute error (8.811); integral square error (14.5); integral time absolute error(280),;integral time square error (31.11) .The results allude to the superiority of the fuzzy-PID scheme over the PID scheme in tracking the optimal set point of riser temperature. More so, for tracking the regenerator set point temperature of 746oC, comparative study of step response under the two schemes gave the following results in six performance indicators: overshoot (PID (12.6%) / Fuzzy-PID (6%)); settling time (PID (80 seconds) / Fuzzy-PID (20seconds)); Integral absolute error (PID (14.29) / Fuzzy-PID (8.63)); integral square error (PID(6.713). Fuzzy-PID (4.506)); integral time absolute error(PID(2858)/Fuzzy-PID(305.9)), integral time square error (PID(77.55)/Fuzzy-PID(33.05)) . Moreover, the fuzzy-PID controller also showed superior performance over the conventional PID controller in terms disturbance rejection (regulatory response) of both riser and regenerator temperature. The results from this study suggest that the application of fuzzy-PID scheme to temperature control offers good promise of improved fluid catalytic cracking unit (FCCU) operations.

Detecting and isolating power grid faults quickly and efficiently ensures a constant power flow. The lack of speed and accuracy in conventional approaches to fault management highlights the importance of creating a smart decision model. The suggested approach uses topology analysis to better localize and isolate faults, reducing downtime and boosting grid performance. Real-time fault detection, precise fault location identification, and choosing the best isolation schemes are all difficulties in power grid fault management. Furthermore, significant issues need innovative solutions, such as managing the grid's complexity, responding to changing load conditions and guaranteeing cyber security. The present research unveils a new approach combining the Intelligent Decision Model integrated with the Supervisory Control and Data Acquisition system (SC-DAS). Here, Real-time decision-making, control, and monitoring are made possible by the SC-DAS system, which mediates between the intelligent decision model and the physical grid. In SC-DAS, Phasor Measurement Units (PMUs) are used for the real-time monitoring of the grid's electrical properties, which are collected using SCADA (Supervisory Control and Data Acquisition) systems. Further, SC-DAS uses decision-making models for instantaneous modifications and remedial measures of the grid's functioning. The grid's reliability uses Topological DAS for fault detection, localization, and isolation. Topological DAS represents a revolutionary approach that could significantly improve power grid reliability, reduce interruption frequency, and boost overall system performance in microgrids and renewable energy sources. The grid layouts, load circumstances, and fault scenarios used in the simulations help to improve the decision-making strategies in a controlled environment.

This study proposes a discrete event system-based control strategy for autonomous tributary mapping using multi-unmanned aerial vehicle. When considering the unmanned aerial vehicles as discrete event systems, supervisory control theory is used to model and control individual unmanned aerial vehicle behavior in the system. In tributary mapping, the situation changes each time depending on environmental factors (e.g. weather) and the work must be performed in an unstructured environment. Therefore, this article proposes a multi-unmanned aerial vehicle-based supervisory control system to solve real-field problems. Unlike the control systems of recent studies, which mainly deal with continuous-time dynamics, we modeled a multi-unmanned aerial vehicle system based on a discrete event system in which the dynamic states are transitioned by asynchronous events. The proposed multi-unmanned aerial vehicle-based supervisory control system was validated in dynamic simulators and demonstrated that multi-unmanned aerial vehicle satisfies the behavior specifications. The supervisor proposed in this study was validated using a physics-based simulator. [ABSTRACT FROM AUTHOR]

Objective: In future deep space exploration missions, crew will have to work more autonomously from Earth. Greater crew autonomy will increase dependence on automated systems. This study investigates the performance effects of different strategies to automate procedural work for space exploration operations. Background: The following strategies are investigated for performing procedural work: • Manual Work uses no procedure automation and crew performs all actions. • Shared Work uses procedure automation to perform some actions within a procedure while crew performs other actions. • Supervised Work uses procedure automation to perform procedure actions while crew supervises the automation. Method: Twenty-seven participants participated in a planetary habitat scenario-based simulation using electronic procedures with automatable actions to investigate the effect of these strategies on situation awareness (SA) and workload. This study used a modification of the Situation Presence Assessment Method to measure SA and the Bedford Workload Scale to measure subjective workload. Results: Mean response times and accuracy for SA queries show no significant difference among the three strategies. Bedford Workload ratings compared across the three strategies indicate that participants rated their workload as highest in the Manual Work condition, followed by the Shared Work condition, and lowest in the Supervised Work condition. Conclusion: The study hypothesized that increased levels of automation would lead to lower subjective workload and decreased SA. Although no significant difference in SA was observed, subjective workload was lower in automation strategies. Based on subjective ratings, 93% of participants preferred some form of automation, with 56% preferring the Shared Work automation condition. [ABSTRACT FROM AUTHOR]

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The supervisory control of Petri nets aims to enforce the undesired behavior as unreachable by designing a set of control places. This work presents a set cover approach to design maximally permissive supervisors. For each first-met bad marking, an integer linear programming problem is developed to obtain a control place to prohibit it. An objective function is formulated to make the maximal number of first-met bad markings forbidden. Then, we develop a set covering approach to minimize the number of selected control places. The proposed approach can guarantee the maximal permissiveness of the obtained supervisor and provide a trade-off between structural complexity and computational cost. Several examples are considered to validate the proposed method.

This paper establishes a connection between control theory for partially observed discrete-event systems (DESs) and automated theorem proving (ATP) in the calculus of positively constructed formulas (PCFs). The language of PCFs is a complete first-order language providing a powerful tool for qualitative analysis of dynamical systems. Based on ATP in the PCF calculus, a new technique is suggested for checking observability as a property of formal languages, which is necessary for the existence of supervisory control of DESs. In the case of violation of observability, words causing a conflict can also be extracted with the help of a specially designed PCF. With an example of the problem of path planning by a robot in an unknown environment, we show the application of our approach at one of the levels of a robot control system. The prover Bootfrost developed to facilitate PCF refutation is also presented. The tests show positive results and perspectives for the presented approach.

Over the last decade, lower limb exoskeletons have seen significant development, with a particular focus on improving the interaction between the subject and the exoskeleton. This has been achieved by implementing advanced control strategies that enable the safe and efficient use of the exoskeleton. In this work, the control strategies for lower limb exoskeletons are divided into upper-level control (supervisory and high-level control) and lower-level control (the servo layer). Before discussing these control strategies, a brief introduction to lower limb exoskeletons and their control schemes is provided. The control hierarchy for lower limb exoskeletons is then systematically reviewed along with an overview of the techniques used. A Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement is used to highlight the systematic process of identifying relevant articles with inclusion and exclusion criteria. The details of supervisory control, high-level control, and servo control strategies are presented by citing relevant state-of-the-art studies, particularly from the past five years. The targeted lower limb joint, training mode, and development stage for different control strategies are highlighted in a tabulated form to articulate the overall hierarchy level. Finally, the potential opportunities and limitations of subject-cooperative control are discussed. Overall, this work aims to provide an in-depth understanding of the control strategies used in lower limb exoskeletons, focusing on subject cooperation. This knowledge can be used to improve the safety and efficacy of lower limb exoskeletons, ultimately benefiting individuals with mobility impairments. [ABSTRACT FROM AUTHOR]

The application of robots in urban search and rescue (USAR) is a domain receiving increasing attention from the scientific community, although it does not yet grant the requirements for field implementation, such as robustness, reliability, and safety. In this work, we propose a multi-robots coordination system based on the supervisory control theory (SCMCS) divided into two layers: reactive and deliberative. While the reactive layer assures that each robot attains to the critical USAR requirements in face of unexpected events by means of a maximally permissive and nonblocking supervisory control, the deliberative layer coordinates the multi-robots system exploiting heuristics to perform the missions with efficiency. The formal modeling and synthesis of supervisors are applied to a case study based on the international USAR agency procedures. The SCMCS architecture is implemented in a simulated scenario composed of two ground robots and two aerial robots executing USAR missions in a post-disaster scene subject to unexpected events such as robot failures, victims and dangers detection and battery losses. The simulation results show that SCMCS is capable of efficiently coordinating the tasks assigned to multiple robots while guaranteeing critical requirements even in face of the uncertainties of robots and environments in USAR disasters. [ABSTRACT FROM AUTHOR]

Failure of resource in automated manufacturing systems could cause a complete system shutdown. This paper addresses the issue of unreliable resource failure in manufacturing cells through the use of load-sharing redundant resources (LSRRs). The aim is to use more than one type of a failure-prone resource to share tasks between a failure-prone resource, called a target resource, and reliable ones called load-sharing redundant resources (LSRRs). Both an unreliable resource and its LSRR perform the same tasks, and there is normally a system that assigns tasks to them. If the target resource fails, all the tasks will be performed by the LSRRs. After the faulty target resource is fixed and restored, its assigned tasks are automatically returned to it. This way the system can continue to produce or process parts. Thus, a total system shutdown due to unreliable resource failures is eliminated. The proposed method is tested using real examples. The results, compared with those obtained by the studies in the literature, show that the proposed method has an outstanding performance and outperforms some of the existing studies. [ABSTRACT FROM AUTHOR]

Roughly speaking, differential privacy is a privacy-preserving strategy that guarantees attackers to be unlikely to infer, from the previous system output, the dataset from which an output is derived. This work introduces differential privacy to discrete event systems modeled by probabilistic automata to protect the state information pertaining to system resource configurations. State differential privacy is defined to protect the initial state of a discrete event system, which represents its initial resource configuration. Step-based state differential privacy verification is proposed in the framework of probabilistic automata, such that an attacker is unlikely to determine the initial state from which a system evolves, within a finite step of observations, if two systems with two different initial states satisfy state differential privacy. Specifically, the probability distributions of generating observations within a finite step from the two different initial states are approximate. If the two systems do not satisfy state differential privacy, a control specification is proposed, such that state differential privacy is enforced via supervisory control that is maximally permissive. Experimental studies are given to illustrate that the proposed method can effectively verify state differential privacy and enforce privacy protection in the probabilistic automata framework. [ABSTRACT FROM AUTHOR]

In our earlier work, we introduced a discrete-event system-based fault-tolerance approach designed to handle intermittent faults. This approach is different from the typical fault-tolerant methodology as the approach does not rely on detecting faults and switching to a new supervisor; it requires a supervisor to be designed that works correctly under normal and fault conditions. This is a passive approach that relies upon inherent redundancy in the system being controlled. This is also a foundation method that should allow a wide variety of existing fault approaches to be modelled but still allow controllability and nonblocking properties to be verified. Permanent faults could be modelled in this framework, but the current method was onerous. In this paper, we introduce a new modelling approach for permanent faults that is easy to use, as well as a set of new permanent fault-tolerant definitions. They are designed to capture several types of permanent fault scenarios (generic situations such as at most one fault occurs) and to ensure that our system remains controllable and nonblocking in each scenario. New definitions and scenarios were required as the previous ones were incompatible with the new permanent fault modelling approach. Finally, we present algorithms to verify these properties, followed by complexity analyses and correctness proofs of the algorithms. An example is then provided to illustrate our approach. [ABSTRACT FROM AUTHOR]

Priority tasks in electric power transmission are: the reliability of power supply to consumers and the efficiency of energy resources use. In postfault steady-state heavy modes of power system operation, these tasks are mainly solved by supervisory control. In order to improve the reliability of power supply, the authors propose an algorithm for assessing the weight of emergency actions, carried out by the power grid manager in postfault conditions. During them the power grid manager redistributes power flows in the electric grid. The application of the suggested algorithm allows to reduce the time for the manager to make a proper decision and choose the correct actions sequence. Methods, used to solve the problem: the theory of multifactorial experiment, methods of mathematical modelling, software “RastrWin3” and “SPSS Statistics”. The regression function is used to simulate the process of changing the load current in the power grid elements. The suggested algorithm can be used in the practice of managing power system modes, by research organizations to improve the characteristics of modes and to develop power grids and power systems. The proposed algorithm complements existing methods for ensuring the reliability of an electrical power system and allows increasing their efficiency.

In the realm of data protection strategies, differential privacy ensures that unauthorized entities cannot reconstruct original data from system outputs. This study explores discrete event systems, specifically through probabilistic automata. Central is the protection of state data, particularly the initial state privacy of multiple starting states. We introduce an evaluation criterion to safeguard initial states. Using advanced algorithms, the proposed method counters the probabilistic identification of any state within this collection by adversaries from observed data points. The efficacy is confirmed when the probability distributions of data observations tied to these states converge. If a system’s architecture does not meet state differential privacy demands, we propose an enhanced supervisory control mechanism. This control upholds state differential privacy across all initial states, maintaining operational flexibility within the probabilistic automaton framework. Concluding, a numerical analysis validates the approach’s strength in probabilistic automata and discrete event systems.

The feedback control system with network-connected components is vulnerable to cyberattacks. We study a problem of attack detection in supervisory control of discrete-event systems. The scenario of a system subjected to actuator enablement attacks is considered in this article. We also consider that some unsafe places that should be protected from an attacker exist in the system, and some controllable events that are disabled by a supervisor might be re-enabled by an attacker. This article proposes a defense strategy to detect actuator enablement attacks and disable all controllable events after detecting an attack. We design algorithmic procedures to determine whether the system can be protected against damage caused by actuator enablement attacks, where the damage is predefined as a set of "unsafe" places. In this way, the system property is called "AE-safe controllability". The safe controllability can be verified by using a basis diagnoser or a basis verifier. Finally, we explain the approach with a cargo system example. [ABSTRACT FROM AUTHOR]

Wastewater treatment plants (WWTPs) are highly non-linear processes that must be optimized to meet rigorous environmental water regulations. In this context, efficiency and costs are equally important terms. The ASM3bioP framework is employed in this study to enable simultaneous nitrogen and phosphorus removal using an activated sludge process model with seven-reactor configurations. The activated sludge process is the most complicated and energy-intensive phase of a WWTP. To control dissolved oxygen in aerobic reactors and nitrate levels in anoxic reactors, two robust PI controllers — a classical PI and a non-integer (fractional)-order PI — with both integer-order and fractional-order models are designed. The controllers are created and simulated with the use of a mathematical model that has been developed based on the input data. The lower level fractional controller with a fractional-order model improves both the effluent quality (EQI) and operational cost (OCI) indices significantly. For such biological WWTP, a hierarchical fuzzy logic controller is designed to adjust the dissolved oxygen in the seventh reactor (DO7) to control ammonia. The implemented supervisory layer control strategy improves effluent quality EQI while increasing OCI marginally. [ABSTRACT FROM AUTHOR]

In the last decade Internet of Things (IoT) emerged as a concept for intelligent, flexible, autonomous, and distributed system while IoT-based devices have highlighted for agent communication, data analysis, and decision-making tasks. Integrating high-level behavioral and low-level control has capital significance for their implementation and is still a challenge. In the field of high-level behavior, Petri nets are widely applied for modeling, simulating, and analyzing concurrent, discrete-event and knowledge-based systems, furthermore even models are used as basis for control algorithm, direct controlling devices' behavioral with high-level Petri nets is little diffused. This paper presents a control architecture used to integrate models in high-level Petri nets with IoT-based devices. Operational tests were provided using CPN Tools and micro-controlled physical devices. A study with a mobile robot is shown and results indicate the reasonability and applicability of this proposed approach in IoT-based devices. [ABSTRACT FROM AUTHOR]

In the present paper, a manufacturing cell in the presence of faults, coming from the devices of the process, is considered. The modular modeling of the subsystems of the cell is accomplished using of appropriate finite deterministic automata. The desired functionality of the cell as well as appropriate safety specifications are formulated as eleven desired languages. The desired languages are expressed as regular expressions in analytic forms. The languages are realized in the form of appropriate general type supervisor forms. Using these forms, a modular supervisory design scheme is accomplished providing satisfactory performance in the presence of faults as well guaranteeing the safety requirements. The aim of the present supervisor control scheme is to achieve tolerance of basic characteristics of the process coordination to upper-level faults, despite the presence of low-level faults in the devices of the process. The complexity of the supervisor scheme is computed. [ABSTRACT FROM AUTHOR]

Accurate modeling and efficient control of inverted pendulums have always been a challenge for researchers. So, the current research aims to achieve the following objectives: (I) proposing a comprehensive dynamic model for the inverted pendulums which accounts for the flexibility of the pendulum bar and (II) suggesting an appropriate supervisory fuzzy-pole placement control strategy for stabilizing the pendulum system. Using a Lagrangian formulation, the equations of motion are derived and linearized. Then, a state feedback controller with a reduced-order observer is designed to stabilize the system. Closed-loop simulations reveal that at least six modes shall be considered in the dynamic equations. To improve the quality of the transient response, a novel fuzzy system is developed for real-time assignment of the controller poles. Simulation results demonstrate that the control quality is significantly improved by adding a supervisory fuzzy system to the control loop. The developed approach for dynamic modeling of the system, and the idea of multi-level fuzzy-pole placement control architecture developed in this paper, may be successfully applied to improve the response specifications in other dynamic systems. [ABSTRACT FROM AUTHOR]

A fundamental expectation of the stakeholders from the Industrial Internet of Things (IIoT) is its trustworthiness and sustainability to avoid the loss of human lives in performing a critical task. A trustworthy IIoT-enabled network encompasses fundamental security characteristics, such as trust, privacy, security, reliability, resilience, and safety. The traditional security mechanisms and procedures are insufficient to protect these networks owing to protocol differences, limited update options, and older adaptations of the security mechanisms. As a result, these networks require novel approaches to increase trust-level and enhance security and privacy mechanisms. Therefore, in this article, we propose a novel approach to improve the trustworthiness of IIoT-enabled networks. We propose an accurate and reliable supervisory control and data acquisition (SCADA) network-based cyberattack detection in these networks. The proposed scheme combines the deep-learning-based pyramidal recurrent units (PRU) and decision tree (DT) with SCADA-based IIoT networks. We also use an ensemble-learning method to detect cyberattacks in SCADA-based IIoT networks. The nonlinear learning ability of PRU and the ensemble DT address the sensitivity of irrelevant features, allowing high detection rates. The proposed scheme is evaluated on 15 datasets generated from SCADA-based networks. The experimental results show that the proposed scheme outperforms traditional methods and machine learning-based detection approaches. The proposed scheme improves the security and associated measure of trustworthiness in IIoT-enabled networks. [ABSTRACT FROM AUTHOR]

The complexity of industrial processes imposes a lot of challenges in building accurate and representative causal models for abnormal events diagnosis, control and maintenance of equipment and process units. This paper presents an innovative data-driven causality modeling approach using interpretable machine learning and process mining techniques, in addition to human expertise, to efficiently and automatically capture the complex dynamics of industrial systems. The approach tackles a significant challenge in the causality analysis community, which is the discovery of high-level causal models from low-level continuous observations. It is based on the exploitation of event data logs by analyzing the dependency relationships between events to generate accurate multi-level models that can take the form of various state-event diagrams. Highly accurate and trustworthy patterns are extracted from the original data using interpretable machine learning integrated with a model enhancement technique to construct event data logs. Afterward, the causal model is generated from the event log using the inductive miner technique, which is one of the most powerful process mining techniques. The causal model generated is a Petri net model, which is used to infer causality between important events as well as a visualization tool for real-time tracking of the system's dynamics. The proposed causality modeling approach has been successfully tested based on a real industrial dataset acquired from complex equipment in a Kraft pulp mill located in eastern Canada. The generated causality model was validated by ensuring high model fitness scores, in addition to the process expert's validation of the results. [ABSTRACT FROM AUTHOR]

In discrete event systems, it is often convenient and possible to observe directly whether or not the system is in a subset of the state space, typically after some delay, even if some event occurrences leading to the current state were not observed. We model supervisory control with the delayed observations of events and states and investigate the existence of a supervisor to obtain a given desired language accordingly. An existence verifier with polynomial run time is presented. [ABSTRACT FROM AUTHOR]

The present study investigated how pupil size and heart rate variability (HRV) can contribute to the prediction of operator performance. We illustrate how focusing on mental effort as the conceptual link between physiological measures and task performance can align relevant empirical findings across research domains.Physiological measures are often treated as indicators of operators’ mental state. Thereby, they could enable a continuous and unobtrusive assessment of operators’ current ability to perform the task.Fifty participants performed a process monitoring task consisting of ten 9-minute task blocks. Blocks alternated between low and high task demands, and the last two blocks introduced a task reward manipulation. We measured response times as primary performance indicator, pupil size and HRV as physiological measures, and mental fatigue, task engagement, and perceived effort as subjective ratings.Both increased pupil size and increased HRV significantly predicted better task performance. However, the underlying associations between physiological measures and performance were influenced by task demands and time on task. Pupil size, but not HRV, results were consistent with subjective ratings.The empirical findings suggest that, by capturing variance in operators’ mental effort, physiological measures, specifically pupil size, can contribute to the prediction of task performance. Their predictive value is limited by confounding effects that alter the amount of effort required to achieve a given level of performance.The outlined conceptual approach and empirical results can guide study designs and performance prediction models that examine physiological measures as the basis for dynamic operator assistance. [ABSTRACT FROM AUTHOR]