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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]

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]

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 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]

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 proposes a controller synthesis method based on matrix transformation and integer linear programming to address the problem of forbidden state controller design for Petri net systems with uncontrollable transitions. The key idea behind this method involves transforming generalized mutual exclusion constraints (GMEC) that represent the legal states of a Petri net system. Based on the incidence matrix of a Petri net, it divides the set of places into irrelevant places, uncontrollable places, and complementary places. By modifying the weights of complementary places and uncontrollable places in the given GMEC, it then converts the given GMEC into an allowable one by solving integer linear programming problems. Using the allowable GMEC, this paper designs a structural controller for Petri nets based on the principle of place invariants. Finally, it takes a part processing system as an example to show that the application is versatile and efficient, offering an effective solution for the controller design of intelligent manufacturing systems. [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]

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]

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]

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]

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]

Salt Gradient Solar Ponds (SGSPs) are conventionally operated wholly manually. They hence suffer from a loss of efficiency for substantial durations when disturbed by natural diffusion and environmental elements such as temperature variation, solar radiation, and wind. They also impose considerable operational costs and time when a repair is unavoidable. Here, a Zero-Discharge Closed Cycle (ZDClC) is proposed for sustainable implementation of control architectures. We then define the SGSP as a control system and propose an automatic approach that uses Artificial Intelligence (AI) to mimic experts' actions during repair operations. More specifically, an Automatic Supervisory Controller (ASC-SGSP) is proposed that decomposes the pond into several uniform sublayers according to their thicknesses and salt concentrations. The automated supervisor then uses a decision tree analysis of pond stability to determine the sequences of injection/suction actions to guarantee the pond stability and prevent layer overturning. An on–off subcontroller then directs the chosen sublayer's salt density to its desired value. The supervisory structure is general and can operate with either the proposed open-loop or closed-loop configurations. Results indicate the potential utility of AI and expert systems as a successful paradigm for SGSP control. Specifically, the proposed approach is examined in a simulated pond under several scenarios challenging its efficiency, adaptivity, robustness, and constraint handling properties. The ASC-SGSP fulfills the automatic control requirements during either open/closed-loop configurations and regulates the desired salinity levels under parameter constraints and uncertainties, even when the SGSP parameters are perturbed by as much as 50%. • A zero discharge closed-cycle sustainable implementation of controllers for SGSPs is proposed. • A proposed systems theory perspective leads to the automatic control of decomposed interactive MIMO sublayers. • Expert systems and AI analyze sublayers' interactions thru a sequence supervising decision tree. • Several subcontrollers manipulate the uniform sublayers according to the supervisor's recommended sequence. • The pond's efficiency and robustness confirm the success of the automatic salinity repairing. [ABSTRACT FROM AUTHOR]

Design of an adaptive supervisory fuzzy sliding mode control for a planar cable-driven parallel robot is aided in this paper. The fuzzy logic controller is proposed to generate the switching control signal without occurring the chattering problem. For this purpose, an adaptive mechanism is suggested for online tuning of the output gain of the fuzzy sliding mode controller. Moreover, for better tracking, a supervisory control system is considered for online tuning of the PID sliding surface gains. The Grasshopper Optimization Algorithm is suggested for optimization of the membership functions selected for the fuzzy sliding surface. The stability proof of the closed-loop system is derived by using the Lyapunov stability theorem. Simulation results are reported to show the merits of the proposed controller on reduced chatter, and system robustness against parameter uncertainty, load disturbance, and nonlinearities. [ABSTRACT FROM AUTHOR]

This article proposes a semi-structural approach to verify the nonblockingness of a Petri net. We construct a structure, called minimal-maximal basis reachability graph (min-max-BRG): it provides an abstract description of the reachability set of a net while preserving all information needed to test if the net is blocking. We prove that a bounded deadlock-free Petri net is nonblocking if and only if its min-max-BRG is unobstructed, which can be verified by solving a set of integer constraints and then examining the min-max-BRG. For Petri nets that are not deadlock-free, one needs to determine the set of dead markings. This can be done with an approach based on the computation of maximal implicit firing sequences enabled by the markings in the min-max-BRG. The approach we developed does not require the construction of the reachability graph and has wide applicability. [ABSTRACT FROM AUTHOR]

We propose an energy-efficient supervisory control method for the power management of parallel hybrid electric vehicles (HEVs) to improve the fuel economy and reduce exhaust gas emissions. Plug-in HEVs ((P)HEVs) have multiple power sources (e.g., an engine and motor) that should be cooperatively operated to meet the required instantaneous traction power for the desired vehicle speed while satisfying their physical limits. Because the efficiencies of the engine and motor vary with different operating speeds and torques, the main issue of energy-efficient power management is to allocate the power demand among the power sources by achieving maximum power conversion efficiencies and satisfy the operating limits. For an efficient power allocation, an optimal control problem is formulated, and a global solution is found through deterministic dynamic programming (DP). Owing to the curse of dimensionality and uncertainties in real driving, DP solutions are not directly applicable in real time. To resolve the limitations of DP, we employ a non-parametric Bayesian function approximation technique using a Gaussian process (GP). The offline DP solutions obtained from a set of real vehicle driving test data were used to learn a state-dependent probabilistic value function through Gaussian process regression. For online implementations, a receding horizon control scheme was applied for the feedback control of the power management. In comparison with the existing charge sustaining strategy and charge depleting and charge sustaining mixed controllers, we recorded fuel efficiency improvements of over 4.8% and 7.3%, respectively, in a mixed urban-suburban route. [ABSTRACT FROM AUTHOR]

In control theory, as in other areas of engineering research, there is an inherent tension between the breadth of a technique's applicability and its mathematical tractability. For the area of discrete-event systems (DES), this manifested itself in a theory of supervisory control that originally provided correct-by-construction guarantees for offline solutions to a restricted kind of deterministic process. Follow-on work extended the reach of these techniques to a number of new settings, notably the development of online control without sacrificing any of the original DES performance guarantees. The ability to enact online control opened the door to applying DES techniques to the adaptive control processes presented by modern technologies: processes with dynamic and time-varying natures, whose characteristics may be understood poorly or not at all. Although many works have built on the seminal work of online control in DES, we believe that these ideas have not reached their full potential due to the difficulty in translating them to adjacent fields. In this survey, we look back at 30 years of research concerning the online control of DES and closely related limited lookahead policies with an eye to making the works accessible to practitioners in the broader control theory and artificial intelligence communities. We conclude with some thoughts on future research directions for the further development and application of online DES control techniques to problems requiring intelligent control in our modern world. [ABSTRACT FROM AUTHOR]

This work presents a methodological framework for the design of a resource-aware supervisory control system for process networks with model uncertainty and communication resource constraints. The developed framework aims to balance the objective of closed-loop stabilization of the overall network with that of meeting the local performance requirements of the component subsystems while keeping the rate of data transfer between the local control systems to a minimum. First, a quasi-decentralized networked control structure, with a set of local model-based controllers communicating with one another over a shared communication medium at discrete times, is designed. A Lyapunov stability analysis of the closed-loop system is then carried out, and the results are used to derive appropriate bounds on the local model state estimation errors as well as the dissipation rates of the local control Lyapunov functions. These bounds are used as stability and performance thresholds to trigger communication between the local control systems and a higher-level supervisor that coordinates the transfer of state measurements between the distributed control systems. A breach of the local stability and performance thresholds generates alarm signals which are transmitted to the supervisor to determine which subsystems should communicate with one another. The supervisor employs a composite Lyapunov function to assess the impact of the local threshold breaches on the stability of the overall closed-loop system. The supervisory communication logic takes account of the evolution of the local and composite Lyapunov functions in order to balance the stability and local performance requirements. Finally, the developed framework is demonstrated using a representative chemical process network and compared with other unsupervised event-based control approaches. It is shown that the supervisory event-based control approach leads to a more judicious utilization of network resources that helps improve closed-loop process performance in the presence of unexpected disturbances and input rate constraints. [ABSTRACT FROM AUTHOR]

With integer linear programming problems (ILPPs) being formulated and solved, the existing approaches design optimal Petri-net supervisors via nonpure net structures, including self-loops and data inhibitor arcs. Nonpure net structures are powerful for control of Petri-net-modeled discrete-event systems. However, in the existing work, the formulated ILPPs contain a large number of constraints, which is computationally inefficient. In this article, we propose approaches that formulate ILPPs with fewer constraints such that the computational efficiency is significantly improved. To do so, in formulating ILPPs for optimal Petri-net controllers by using self-loops and data inhibitor arcs, we remove the reachability conditions for legal markings. By doing so, an obtained solution may result in some legal markings unreachable. To solve this problem, a novel technique is developed to design an optimal controller by modifying the initial marking and structure of the obtained supervisor. It is shown that, by the reduced ILPPs, one can find the same feasible solutions as that obtained by the existing work. Finally, the proposed approaches are demonstrated by examples. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new property of quantitative nonblockingness of an automaton with respect to a given cover on its set of marker states. This property quantifies the standard nonblocking property by capturing the practical requirement that every subset (i.e. cell) of marker states can be reached within a prescribed number of steps from any reachable state and following any trajectory of the system. Accordingly, we formulate a new problem of quantitatively nonblocking supervisory control, and characterize its solvability in terms of a new concept of quantitative language completability. It is proven that there exists the unique supremal quantitatively completable sublanguage of a given language, and we develop an effective algorithm to compute the supremal sublanguage. Finally, combining with the algorithm of computing the supremal controllable sublanguage, we design an algorithm to compute the maximally permissive solution to the formulated quantitatively nonblocking supervisory control problem. [ABSTRACT FROM AUTHOR]

This work introduces Physics-informed State-space neural network Models (PSMs), a novel solution to achieving real-time optimization, flexibility, and fault tolerance in autonomous systems, particularly in transport-dominated systems such as chemical, biomedical, and power plants. Traditional data-driven methods fall short due to a lack of physical constraints like mass conservation; PSMs address this issue by training deep neural networks with sensor data and physics-informing using components' Partial Differential Equations (PDEs), resulting in a physics-constrained, end-to-end differentiable forward dynamics model. Through two in silico experiments – a heated channel and a cooling system loop – we demonstrate that PSMs offer a more accurate approach than a purely data-driven model. In the former experiment, PSMs demonstrated significantly lower average root-mean-square errors across test datasets compared to a purely data-driven neural network, with reductions of 44 %, 48 %, and 94 % in predicting pressure, velocity, and temperature, respectively. Beyond accuracy, PSMs demonstrate a compelling multitask capability, making them highly versatile. In this work, we showcase two: supervisory control of a nonlinear system through a sequentially updated state-space representation and the proposal of a diagnostic algorithm using residuals from each of the PDEs. The former demonstrates PSMs' ability to handle constant and time-dependent constraints, while the latter illustrates their value in system diagnostics and fault detection. [ABSTRACT FROM AUTHOR]

Solutions to decentralized discrete-event systems problems are characterized by the way local decisions are fused to yield a global decision. A fusion rule is colloquially called an architecture. Current approaches do not provide a direct way to compare existing architectures. Determining whether an architecture is more permissive than another architecture had relied on producing examples ad hoc and on individual inspiration that puts the conditions for solvability in each architecture into some form that admits comparison. In response to these research efforts, a method based on morphisms between graphs has been extracted to yield a uniform approach to compare the permissiveness of the architectures. [ABSTRACT FROM AUTHOR]

In this work, a predictive supervisory controller is presented that optimizes the interaction between a diesel engine and its aftertreatment system (ATS). The fuel consumption is minimized while respecting an upper bound on the emitted tailpipe NOx mass. This is achieved by optimally balancing the fuel consumption, the engine-out NOx emissions, and the ATS heating. The proposed predictive supervisory controller employs a two-layer model predictive control structure and solves the optimal control problem using a direct method. Through experimental validation, the resulting controller was shown to reduce the fuel consumption by 1.1% at equivalent tailpipe NOx emissions for the nonroad transient cycle when compared to the operation with a fixed engine calibration. Further, the controller's robustness to different missions, initial ATS temperatures, NOx limits, and mispredictions was demonstrated. [ABSTRACT FROM AUTHOR]

This paper investigates an adaptive cooperative fault-tolerant supervisory control problem for nonlinear strict-feedback leader-following systems with unknown control coefficients and actuator faults under the fixed directed graph. Radial basis function neural networks are used to approximate system uncertainties. The Nussbaum gain technique is introduced to address unknown signs of control gains. Then, based on the dynamic surface control method, a distributed adaptive fault-tolerant control scheme is presented to compensate for the actuator faults of followers. For neural networks approximation theory, the unknown smooth function can only be approximated on a compact set due to the approximation errors. By theoretic analysis, the supervisory-based adaptive controllers are proposed to guarantee that system state signals can converge to compact sets and the leader-following systems can achieve the practical output consensus. Finally, the simulation results are provided to show the validity of the proposed consensus scheme. [ABSTRACT FROM AUTHOR]

This article develops, evaluates, and verifies a supervisory controller (SC) to transfer from the grid-connected mode and operate a battery-enhanced electric vehicles’ (EVs) dc fast charging (DCFC) station in the autonomous mode, when the supply grid is not available. The SC is designed based on the supervisory control theory (SCT) of discrete event systems and is based on a rigorous mathematical process; nonblocking, i.e., avoids entering an operational deadlock scenario that leads to the system collapse; minimally restrictive with respect to the station's discrete behavior; modular; and scalable. The SC also operates the station in the grid-connected mode and provides a seamless transition between the two modes and thus the DCFC station is also synonymous with a dc-microgrid. The SC is implemented on an industrial programmable logic controller and its performance is verified in a real-time hardware-in-the-loop environment using an OPAL-RT testbed. [ABSTRACT FROM AUTHOR]

In cyber–physical systems (CPSs), it is of great importance to handle network attack issues. In this article, we consider the supervisory control layer of CPSs, focusing on closed-loop control systems vulnerable to sensor-reading modification attacks (SM-attacks), which may disguise the occurrence of an event as a different event by modifying appropriately sensor readings in sensor communication channels. In particular, we consider the plant modeled as a bounded Petri net and the control specification consisting in liveness enforcing. Based on repeatedly computing a more restrictive liveness-enforcing supervisor under no attack and constructing a so-called basic supervisor, a method that synthesizes a liveness-enforcing supervisor tolerant to an SM-attack is proposed. [ABSTRACT FROM AUTHOR]

In supervisory control of Petri nets (PNs), the place-invariant ($P$ -invariant) control principle is the most typical and principal method to deal with the siphon control problem. Although it has a relatively narrow application, this principle is widely acknowledged due to its simplicity and efficiency. In this article, we first propose the extended $P$ -invariant control principle in order to extend the application of $P$ -invariants and provide a general methodology for the control of siphons. Second, three types of $P$ -invariants, from the special to the general, are developed to implicitly or explicitly invariant control the siphons. In the most general case, the virtual $P$ -invariants are constructed in the PNs. Third, the extended principle is further applied to the supervisor simplification. In the paradigm of the extended principle, it presents the redundancy from a structural perspective in contrast to several typical methods, and shows the significant importance of structural analysis in PNs, especially the important role of $P$ -invariants. As a consequence, the extended $P$ -invariant control principle can be considered as the fundamental principle of siphon control as well as its supervisor simplification. [ABSTRACT FROM AUTHOR]

Manufacturing systems may be subject to external attacks and failures, so it is important to deal with the recovery of the system after these situations. This article deals with the problem of recovering a manufacturing system, modeled as a discrete event system (DES) using the supervisory control theory (SCT), when the control structure, called supervisor, desynchronizes from the physical plant. The desynchronization may be seen as plant and supervisor being in uncorresponding states. The recovery of the system may be attained if there is a word, the synchronizing word, that regardless the state of each one of them, brings the system and supervisor back to a known state. The concepts of synchronizing automata are used to do so. In this article, we show under what conditions a set of synchronizing plants and specifications leads to a synchronizing supervisor obtained by the SCT. The problem is extended to cope with multiple supervisors, proposing a local recovery when possible. We also present a simple way to model problems, composed of machines and buffers, as synchronizing automata such that it is always possible do restore synchronization between the control (supervisor) and the plant. Note to Practitioners—Given the unpredictability of faults and malicious attacks occurring in industrial systems, recovery strategies are crucial for a harmonic operation of the plant. The possibility of leading the system to a known state, recovering control, is of extreme importance to the safety of industrial processes. The method proposed in this article uses well-known concepts of supervisory control theory (SCT) of discrete event systems (DESs), introducing the recovery process (using recovery events) in the modeling phase such that it is possible to isolate and fix only the part of the control system subject to the fault. The result of the proposed approach allows the implementation of such control system with the recovery procedure directly in the programmable logic controllers (PLCs). [ABSTRACT FROM AUTHOR]

During the last decades, the use of sustainable energies instead of conventional fuel has been increased due to the global anxiety on environmental and economic problems like climate changes, the limitation of traditional resources, and the increase of electricity consumption. In the present work, design, modeling, and simulation of a Hybrid Power System (HPS), which is united a Wind Turbine System (WTS) with a battery bank, is presented. However, the power operation of the HPS is controlled by a power supervisory in order to reduce energy loss. The supervisory has been implemented in STM32F4 by using the 'PIL simulation' technology and tested under different scenarios of climatic conditions. The results indicate that, during a windy climatic condition, the proposed HPS is broadly enough to generate 100% of the required power with 0.005% of LPSP and 99.84% of RF. Whereas, through a moderate wind speed, the HPS reaches 0.675% of LPSP and 66.94% of RF. But, with a low wind speed, the HPS achieves 0.919% of LPSP and 26.10% of RF. Besides, the proposed WTS was broadly sufficient for supplying the needed power, with an empty battery, especially, when the available wind velocity is around 14.3 m/s at least. Furthermore, it illustrates the efficiency of the supervisory control. [ABSTRACT FROM AUTHOR]

This article provides a detailed description of a supervisory control problem for a heavy-duty vehicle equipped with a diesel engine and an exhaust aftertreatment system (EATS). The control problem considers the coupling between the engine and EATS and assumes the availability of predictive driving information for a long horizon. The article also proposes a computationally efficient supervisory control algorithm, which is implemented in an existing engine electronic control unit. The algorithm is based on distributed economic nonlinear MPC. It minimizes total fuel consumption (by the diesel engine and EATS) and fulfils Euro VI real-driving and world harmonized transient cycle based NO $_\text{x}$ emission legislation for the heavy-duty diesel vehicle. The performance of the algorithm is evaluated using a high-fidelity simulation platform, which includes a validated GT-POWER model of a 13 L turbo compound diesel engine and the first-principles model of an EATS. Simulation results show that the supervisory controller has 1.7% less total fuel consumption, and 88.4% less NH $_3$ slip, compared to a baseline controller, to achieve the same real-driving NO $_\text{x}$ emission. [ABSTRACT FROM AUTHOR]

The protection of power system is an essential trait in a huge network to efficiently detect and isolate the sections undergoing faults or abnormal behaviour. The key components of a protection scheme include circuit breakers, relays, switchgears and fuses which employ communication from one station to another to achieve high-speed tripping. The automation of these components at the laboratory level using programmable logic controller (PLC) along with supervisory control and data acquisition (SCADA) system owns paramount importance for intelligent decision making, sensing, actuating, monitoring and maintaining the record in the host server. This paper discusses such a technique for conventional power system protection laboratory at a new level of development to promote a control system through PLC and SCADA. The control system has indication of over and under values of voltage, load and frequency, which can trigger malfunctioning of equipment and must be rectified. Furthermore, ground fault and inverse current indication are added to the system for monitoring and controlling purposes. The proposed system enhances the efficiency and safety of the expensive equipment and the personnel to the next level and also introduces new standards of automated protection schemes for modern technical institutes. [ABSTRACT FROM AUTHOR]

In this paper, we model and analyze democratic progress and regression in the framework of discrete event systems. Since democratic progress strongly correlates with the growing number of people who meet their economic interests and political rights, it can be represented as an event-driven dynamical system evolved by local agents that make a decision to enable or disable the progress and regression events according to their private objectives. The final decision on enabling/disabling an event is made by the majority rule, which also complies with the feature of democracy. Based on majority-based supervisory control, we investigate the conditions for the democratic progress model to be progressive or regressive under full observation. Moreover, we present a scheme that can modify the characteristics of the model by designing masks for agents, i.e. the regressive system under full observation is changed to a progressive one under partial observation via masks, and vice versa. [ABSTRACT FROM AUTHOR]