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This paper presents an edge‐based event‐triggered delay distributed algorithm for solving the economic dispatch problem (EDP) in smart grids. The objective of the EDP is to minimize the total generation cost by allocating power to individual generators, each with its local generation constraint. To save the overhead of communication resources between agents, an edge‐based event‐triggered mechanism is suggested. By setting distinct triggering thresholds for each communication edge, the agent may efficiently regulate the communication frequency among all neighbors. However, in practice, owing to the instability of the network, the agent may receive information regarding its neighbors, leading to communication lags for every communication link. The virtual agent technique and double‐stochastic matrix augmentation technique provide an equivalent delay‐free EDP. It is demonstrated that the proposed algorithm can asymptotically converge to the global optimal solution as long as the communication delay is arbitrary, time‐varying, and random, but bounded. Objective and clear evidence is provided through a case study in smart grids to verify the algorithm's feasibility. [ABSTRACT FROM AUTHOR]

In this paper a framework for engineering supervisory controllers for product lines with dynamic feature configuration is proposed. The variability in valid configurations is described by a feature model. Behavior of system components is achieved using (extended) finite automata and both behavioral and dynamic configuration constraints are expressed by means of requirements as is common in supervisory control theory. Supervisory controller synthesis is applied to compute a behavioral model in which the requirements are adhered to. For the challenges that arise in this setting, multiple solutions are discussed. The solutions are exemplified in the CIF toolset using a model of a coffee machine. A use case of the much larger Body Comfort System product line is performed to showcase feasibility for industrial-sized systems. [ABSTRACT FROM AUTHOR]

Abstract Discrete event systems (DESs) are powerful abstract representations for large human-made physical systems in a wide variety of industries. Safety control issues on DESs have been extensively studied based on the logical specifications of the systems in various literature. However, when facing the DESs under uncertain environment which brings into the implicit specifications, the classical supervisory control approach may not be capable of achieving the performance. So in this research, we propose a new approach for optimal control of DESs under uncertain environment based on supervisory control theory (SCT) and reinforcement learning (RL). Firstly, we use SCT to gather deliberative planning algorithms with the aim to safe control. Then we convert the supervised system to Markov Decision Process simulation environments that is suitable for optimal algorithm training. Furthermore, a SCT-based RL algorithm is designed to maximize performance of the system based on the probabilistic attributes of the state transitions. Finally, a case study on the autonomous navigation task of a delivery robot is provided to corroborate the proposed method by multiple simulation experiments. The result shows the proposed approach owning 8.27 $$\%$$ % performance improvement compared with the non-intelligent methods. This research will contribute to further studying the optimal control of human-made physical systems in a wide variety of industries.

Abstract This paper presents an edge‐based event‐triggered delay distributed algorithm for solving the economic dispatch problem (EDP) in smart grids. The objective of the EDP is to minimize the total generation cost by allocating power to individual generators, each with its local generation constraint. To save the overhead of communication resources between agents, an edge‐based event‐triggered mechanism is suggested. By setting distinct triggering thresholds for each communication edge, the agent may efficiently regulate the communication frequency among all neighbors. However, in practice, owing to the instability of the network, the agent may receive information regarding its neighbors, leading to communication lags for every communication link. The virtual agent technique and double‐stochastic matrix augmentation technique provide an equivalent delay‐free EDP. It is demonstrated that the proposed algorithm can asymptotically converge to the global optimal solution as long as the communication delay is arbitrary, time‐varying, and random, but bounded. Objective and clear evidence is provided through a case study in smart grids to verify the algorithm's feasibility.

This paper discusses the problem of marking constraints in a class of partially controllable timed Petri nets. Specifically, these are Timed Event Graphs (TEGs) which include disturbed input transitions and which are subject to token capacity constraints in some paths. Using the Min-Plus algebra formalism, formal approaches are proposed to design causal control laws to guarantee compliance with marking constraints in TEGs. Sufficient conditions are defined to show the existence and synthesis of these controllers. The developed methodologies are illustrated in a case study of a disassembly system with a limited stock of components. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of synthesizing network attacks against fault diagnosis in the context of discrete event systems (DESs). It is assumed that the sensor observations sent to the operator that monitors a system are tampered with by an active attacker. We first formulate the process of online fault diagnosis under attack. Then, from the attack viewpoint, we define a sensor network attacker as successful if it can degrade the fault diagnosis in the case of maintaining itself as undiscovered by the operator. To verify such an attacker, an information structure called a joint diagnoser (JD) is proposed, which describes all possible attacks in a given attack scenario. Based on the refined JD, i.e., stealthy joint diagnoser (SJD), we present an algorithmic procedure for synthesizing a successful attacker if it exists. [ABSTRACT FROM AUTHOR]

Marine aquaculture, particularly in the Mediterranean region, faces the challenge of minimizing growth dispersion, which has a direct impact on the production cycle, market value and sustainability of the sector. Conventional grading methods are resource intensive and potentially detrimental to fish health. The current study presented an innovative approach in predicting fish weight dispersion in European seabass (Dicentrarchus labrax) aquaculture. Seabass is one of the two major fish species cultivated on the Mediterranean coast, with a fattening cycle of 18–24 months. During this period, several grading operations are carried out to minimize growth dispersion. The intricate feed-fish-water system, characterized by complex interactions among feeding regimes, fish behavior, individual metabolism and environmental factors, is the focus of the study. The comprehensive, five-step methodology addresses this complexity. The process begins with a Discrete Event System (DES) model that simulates the feed-fish-water dynamics, taking into account individual fish metabolism. This is followed by the development of a surrogate machine learning (ML) regressor model, which is trained on DES simulation data to efficiently predict growth distribution. The model is then calibrated and customized for specific fish stocks and production tanks. The preliminary results from 21 tanks in two trials with European seabass (D. labrax) showed the effectiveness of the method. The results from the simulation models achieved a R2 of 99.9 % and a Mean Absolute Percentage Error (MAPE) of 1.1 % for the prediction of mean final weight and a R2 of 90.3 % with a MAPE of 8.1 % for the standard deviation of final weight. In summary, this study represents a significant advance in the planning and management of seabass aquaculture. Given the lack of effective prediction tools in the aquaculture industry, the proposed methodology has the potential to reduce risks and inefficiencies, thus possibly optimizing aquaculture practices by increasing sustainability and profitability.

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.

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]

The public health sector, considered to be vital, especially in this pandemic crisis of COVID‐19, requires automation and control of drug distribution in pharmacies commonly called: "Dispensing." In this paper, we address the medication assignment problem for automated dispensing cabinets (ADCs). First, we use a network of conflicting timed event graphs (CTEGs), a class of timed Petri nets with shared resources, to model pharmaceutical cabinets. Second, we develop a new method for controlling CTEGs under mutual exclusion constraints (MECs) to solve the problem of drug assignment, using a control approach based on Min‐Plus algebra. Finally, a case study of assigning drugs is given to illustrate the proposed methodology and show the efficiency of the developed control laws. [ABSTRACT FROM AUTHOR]

This study proposes an analytical control method for the meeting of capacity constraints in discrete event systems with disturbances. More precisely, it consists of computing control laws for partially controllable and observable timed event graphs that are subject to marking constraints. To resolve the issue, linear Min‐Plus models are used to describe the behavior of these graphs, and the constraints are expressed by inequalities in Min‐Plus algebra. Sufficient conditions for the existence of causal control laws to guarantee marking specifications are established. Finally, to illustrate the efficiency of the proposed approaches in this paper, an application for a disassembly process with some disturbance inputs and limited component capacities is carried out. [ABSTRACT FROM AUTHOR]

This paper addresses the control problem for a class of discrete event systems with disturbances, which is subject to mutual exclusion constraints (MECs). Our aim is to propose a new analytical method for designing control laws to guarantee a set of MECs in network timed event graphs (NTEGs) with uncontrollable input transitions. To develop this control law, linear min-plus equations are used to describe the behavior of the NTEGs and min-plus inequalities to translate the specifications to be satisfied. Sufficient conditions for the existence of causal control laws are established. These calculated laws are represented by control places that will supervise the NTEGs to ensure that constraints are respected. To demonstrate the effectiveness of the suggested approach, an illustrative application on an assembly system is carried out. [ABSTRACT FROM AUTHOR]

Aiming at the problem that it is difficult to model DES(discrete event systems) for large-scale real systems and it is impossible to carry out effective fault diagnosis, this paper proposed a fault diagnosis method based on active learning. Firstly, the method added normal/fault labels to the acquired system event logs, divided the log set into a training set and a test set, and proposed an iterative algorithm based on abstraction technique to extract fault feature samples from the event logs in the training set. Then, it constructed the initial fault identifier from the fault feature samples, and checked the accuracy of identifier using the event logs in the test set. Simulation results show that the fault diagnosis algorithm enabled higher diagnosis accuracy under model unknown. Finally, examples illustrate the application of the fault diagnosis algorithm under system model unknown. Compared with the existing research, the proposed method can diagnose faults when the system model is unknown and the complexity of the algorithm is polynomial, which results in higher diagnostic accuracy and a wider range of applications. [ABSTRACT FROM AUTHOR]

This paper addresses fault pattern diagnosis of discrete event systems, involving fault pattern detection and diagnosability. A fault pattern is modelled as a finite automaton whose accepted language is the objective to be diagnosed, representing the occurrence of complex or composite faults. A verifier for fault pattern detection based on the synchronous product of a system and a fault pattern is proposed. By removing all silent events, a silent closure is calculated based on the synchronous product, which offers computational advantages for systems that have a large number of silent events. An NSC/FSC pair verifier is then computed by taking the product of a normal silent closure and an accepted silent closure. By studying indeterminate cycles of the NSC/FSC pair verifier, necessary and sufficient verification conditions are established, asserting that a system is diagnosable with respect to a fault pattern if and only if there is no indeterminate cycle in the NSC/FSC pair verifier. It is shown that the proposed method requires polynomial time at most. Finally, a case study to illustrate the results is provided. [ABSTRACT FROM AUTHOR]

To ensure opacity, it is optimal to retain as many as possible occurring event sequences. Contrary to this problem, the other optimal goal is to preserve the minimal occurring event sequences. Based on the choosing cost, an optimal opacity-enforcing problem with minimal discount choosing cost is presented under two constraints in this paper. The first constraint is the opacity of the controlled system. The second is the retention of the secret to the maximum. To solve the model, two scenarios on opacity are considered. For the two scenarios, some algorithms are presented to achieve the optimal solution for the model by using the method of dynamic programming. Then, the solutions produced by the algorithms are proved to be correct by theoretical proof. Finally, some illustrations and an application example on location privacy protection for the algorithms are given. [ABSTRACT FROM AUTHOR]

Max-plus algebra provides mathematical methods for solving nonlinear problems by using linear equations. These kinds of the problems arise in areas such as manufacturing, transportation, allocation of resources, and information processing technology. In this paper, the scheduling of production systems consisting of many stages and different units is considered, where some of the units can be used for many stages. If a production unit is used for different stages cleaning is needed in between, while no cleaning is needed between stages of the same type. Cleaning of units takes a significant amount of time, which is considered in the scheduling. The goal is to minimize the total production time, and such problems are often solved by using numerical optimization. In this paper a max-plus formalism is used for the modeling and scheduling of such production systems. Structural decisions such as choosing one unit over another proved to be difficult in the latter case, but this can be viewed as a switching max-plus linear system. No switching (and thus no cleaning) is considered as a base case, but for larger production batches the durability constraints will require switches. Switching as seldom as possible is shown to be optimal. Scheduling of a small production system consisting of 6 stages and 6 units is used as a case study. [ABSTRACT FROM AUTHOR]

Brake systems represent one of the most critical components contributing to the safety of modern automotive systems. They are commonly referenced as one of the most vehicle-related factors in fatal crashes. Usually, the faults in this type of component are provoked by brake fluid leaks affecting the performance and response of the entire system, which require manual maintenance procedures to locate the source of the problem. This study presents a novel Discrete Event System (DES) model capable of detecting brake fluid leakage faults using a decentralized Most Permissive Observer (MPO) architecture for fault diagnosis. The developments were verified via an enhanced virtual environment employing a simulation model of the braking system using Simscape and assessing the event-driven fault diagnosis scheme using Stateflow. A ‘Design of Experiment’ method is performed using a factorial design of $3^{k}$ , where k is the number of levels of a factor in the factorial experiment (in this paper k is 4), to conduct the assessment of test cases that combined edge values. The efficacy of the proposed DES model for fault diagnosis is demonstrated across a wide range of simulated scenarios, including fault locations, pressure drops, and single and multiple fault combinations. The proposed method consistently diagnoses the injected fault conditions, showcasing its efficiency under diverse operational conditions of the braking system.

In contemporary urban environments, the growing utilization of vehicles has emerged sophisticated challenges. Notably, degradation in air quality and amplified fuel consumption are the most issues that are being faced in urban cities due to traffic-related disruptions. Addressing these challenges is necessary to concern the adverse impacts on public health and economic aspects. This paper presents a comprehensive Traffic Management System employing the Symbolic Discrete Controller Synthesis Technique, offering a paradigm shift in urban traffic control. Our approach synthesizes effective controllers to reduce congestion and enhance system reliability by benefitting formal control frameworks and advanced modeling techniques. The key features of the approach include a symbolic safety algorithm to ensure compliance with regulations, an optimization algorithm to minimize congestion costs. Simulations across various scenarios validate the efficacy and robustness of our framework and also it suggests its potential to significantly improve urban mobility. The research directions may explore scalability and real-time data integration for broader applicability, however, our work lays a foundation for integrated traffic management systems combining formal control techniques, safety algorithms, and optimization strategies.

Opacity is an essential security indicator in archive systems. There exists a set of secret states and an external intruder who can observe the behavior of the system in the archive system. The intruder can steal private information by observing the behavior of the system. The system is said to be ${K}$ -step opaque when the intruder cannot confirm whether the system has been in a secret state at any time, within the observation of ${K}$ events. In the case where an intruder can never be sure whether the system has ever been in a secret state, the system is referred to be infinite-step opaque. To be realistic, we consider an archive system modeled as a bounded labeled Petri net, and propose an algorithm for constructing a modified state estimator to increase the security of the archive system. Our aim is to verify the two types of opacity of the system by the observer and the modified state estimator. Our new algorithm improves the security of the system so that an intruder cannot easily know whether the system is in a secret state or not, which also improves the previously-known results.

Abstract This work proposes a symbolic method to verify and enforce the current‐state opacity of labelled Petri nets (LPNs). The notion of basis markings of partially observed Petri nets currently dominates the development of opacity verification and enforcement for discrete event systems. However, the related computational efficiency, to a great extent, depends on the number of basis markings in a system, which increases exponentially with respect to the size of its corresponding LPN model. Binary decision diagrams (BDDs) are capable of computing a set of basis markings in a compact shared structure. To mitigate the computational overheads, a BDD‐based method to efficiently model the structure and behaviour of an LPN is proposed. Then, the current‐state opacity of LPNs is verified and enforced in a symbolic manner. Finally, a number of examples are provided to demonstrate the effectiveness and efficiency of the proposed method.

Fleet systems are considered complex due to the interaction between their units and components. Maintenance management systems face various challenges to achieve acceptable availability and reliability rates at a reasonable cost. A critical task for making maintenance decisions is understanding the system requirements to select maintenance policies appropriate for the actual and future system state. When there is a replacement shortage in a fleet system, and it is impossible to supply new spare parts quickly, cannibalization policies can mitigate this scarcity via the interchange of components. However, this procedure presents the maintenance manager with different evaluation effects, such as increased maintenance hours, decreased system reliability rate, and unavailability in some units. Finding an equilibrium between the benefits and risks has caught the attention of researchers. This work gathers diverse proposals for applying cannibalization policies and the effects that arise from using them. Models, methods, tools, and identified gaps in understanding what parameters of the components and environments of the fleet systems favor cannibalization are discussed.

This work proposes a symbolic method to verify and enforce the current‐state opacity of labelled Petri nets (LPNs). The notion of basis markings of partially observed Petri nets currently dominates the development of opacity verification and enforcement for discrete event systems. However, the related computational efficiency, to a great extent, depends on the number of basis markings in a system, which increases exponentially with respect to the size of its corresponding LPN model. Binary decision diagrams (BDDs) are capable of computing a set of basis markings in a compact shared structure. To mitigate the computational overheads, a BDD‐based method to efficiently model the structure and behaviour of an LPN is proposed. Then, the current‐state opacity of LPNs is verified and enforced in a symbolic manner. Finally, a number of examples are provided to demonstrate the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

A Cyberphysical system, being an autonomous guided vehicle (AGV) and having diverse applications such as thematic parks and product transfer in manufacturing units, is modeled and controlled. The models of all subsystems of the AGV are provided in discrete event systems (DES) form following the Ramadge–Wonham (R–W) framework. The safe performance of the AGV, being the desired behavior of the system, is presented in the form of desired rules and translated into a set of regular languages. Then, the regular languages are realized as supervisory automata in the framework of Supervisory Control Theory (SCT). To ease implementation and coordination of the control architecture, the supervisors are designed to be in two-state automata forms. The controllability of the regular languages, regarding the AGV, will be proved, using the physical realizability (PR) of the synchronous product of the automata of the system and the supervisors. Also, the nonblocking property of all the controlled automata will be proven to be satisfied. Simulation of the controlled AGV will validate the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a method for verifying and validating a cyber-physical system. The proposed method identifies possible blockings between the physical and cybernetic parts and is based on the controllability and non-blocking tests of the supervisory control theory. Furthermore, the proposed modeling complies with the hierarchical structure inherent to the cyber-physical system. The paper also presents a method for extracting models of the cybernetic part as it is and puts together models of both physical and cybernetic components that interact with each other. We have also developed tools to perform the verification and validation task and illustrate the proposed method with a computer-integrated manufacturing platform case study. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of synthesizing network attacks against fault diagnosis in the context of discrete event systems (DESs). It is assumed that the sensor observations sent to the operator that monitors a system are tampered with by an active attacker. We first formulate the process of online fault diagnosis under attack. Then, from the attack viewpoint, we define a sensor network attacker as successful if it can degrade the fault diagnosis in the case of maintaining itself as undiscovered by the operator. To verify such an attacker, an information structure called a joint diagnoser (JD) is proposed, which describes all possible attacks in a given attack scenario. Based on the refined JD, i.e., stealthy joint diagnoser (SJD), we present an algorithmic procedure for synthesizing a successful attacker if it exists.

Abstract This article investigates the event‐triggered consensus problem of general linear multiagent systems with denial‐of‐service (DoS) attacks. A distributed event‐triggered mechanism is presented to alleviate the limited communication resources, the designed mechanism is fully distributed owing to the introduction of some adaptive parameters. Moreover, since the DoS attacks have been considered in the event‐triggered rule design, a switching strategy for the adaptive parameters is adopted. A switching Lyapunov function is used to strictly demonstrate that secure consensus can be reached effectively under the proposed control mechanism. The quantitative relationship between the frequency of the DoS attacks and the consensus is also presented. Moreover, the Zeno behaviour can be excluded for the studied close‐looped systems. Finally, a numerical simulation example is given to demonstrate the validity of the designed control law.

Fleet systems are considered complex due to the interaction between their units and components. Maintenance management systems face various challenges to achieve acceptable availability and reliability rates at a reasonable cost. A critical task for making maintenance decisions is understanding the system requirements to select maintenance policies appropriate for the actual and future system state. When there is a replacement shortage in a fleet system, and it is impossible to supply new spare parts quickly, cannibalization policies can mitigate this scarcity via the interchange of components. However, this procedure presents the maintenance manager with different evaluation effects, such as increased maintenance hours, decreased system reliability rate, and unavailability in some units. Finding an equilibrium between the benefits and risks has caught the attention of researchers. This work gathers diverse proposals for applying cannibalization policies and the effects that arise from using them. Models, methods, tools, and identified gaps in understanding what parameters of the components and environments of the fleet systems favor cannibalization are discussed. [ABSTRACT FROM AUTHOR]

This work addresses the problem of quantifying opacity for discrete event systems. We consider a passive intruder who knows the overall structure of a system but has limited observational capabilities and tries to infer the secret of this system based on the captured information flow. Researchers have developed various approaches to quantify opacity to compensate for the lack of precision of qualitative opacity in describing the degree of security of a system. Most existing works on quantifying opacity study specified probabilistic problems in the framework of probabilistic systems, where the behaviors or states of a system are classified as secret or non-secret. In this work, we quantify opacity by a state-worth function, which associates each state of a system with the worth it carries. To this end, we present a novel category of opacity, called worthy opacity, characterizing whether the worth of information exposed to the outside world during the system's evolution is below a threshold. We first provide an online approach for verifying worthy opacity using the notion of a run matrix proposed in this research. Then, we investigate a class of systems satisfying the so-called 1-cycle returned property and present a worthy opacity verification algorithm for this class. Finally, an example in the context of smart buildings is provided. [ABSTRACT FROM AUTHOR]

To ensure opacity, it is optimal to retain as many as possible occurring event sequences. Contrary to this problem, the other optimal goal is to preserve the minimal occurring event sequences. Based on the choosing cost, an optimal opacity-enforcing problem with minimal discount choosing cost is presented under two constraints in this paper. The first constraint is the opacity of the controlled system. The second is the retention of the secret to the maximum. To solve the model, two scenarios on opacity are considered. For the two scenarios, some algorithms are presented to achieve the optimal solution for the model by using the method of dynamic programming. Then, the solutions produced by the algorithms are proved to be correct by theoretical proof. Finally, some illustrations and an application example on location privacy protection for the algorithms are given.

This paper addresses the topic of robust scheduling for flexible manufacturing systems (FMS) with operation interruptions and unreliable resources. The proposed approach uses timed Petri nets as a model of the FMS in uncertain environments. This model includes controllable and uncontrollable transitions. The unexpected firings of the uncontrollable transitions represent operation and resource failures and the risk to deviate from the scheduled trajectories. This paper proposes an anytime graph search algorithm with a new objective function that combines performance and risk. In addition, the graph search incorporates a new filtering mechanism that learns from previous runs and two node expansion strategies. The performance of the proposed algorithm is compared with that of another already existing algorithm. A running example and a case study from a real company illustrate the efficiency of the proposed scheduling approach. [ABSTRACT FROM AUTHOR]

The symmetry between customer expectations and operator goals, on one hand, and the digital transition of the railways, on the other hand, is one of the main factors affecting green transport sustainability. The European Train Control System (ETCS) was created to improve interoperability between different railway signaling systems and increase safety and security. While there are a lot of ETCS Level 2 deployments all over the world, the specifications of ETCS Level 3 are under development. ETCS Level 3 is expected to have a significant impact on automatic train operation, protection, and supervision. In this paper, we present an innovative control system architecture that allows the incorporation of artificial intelligence (AI)/machine learning (ML) applications. The architecture features control function virtualization and programmability. The concept of an intelligent railway controller (IRC) is introduced as being a piece of cloud software responsible for the control and optimization of railway operations. A microservices-based approach to designing the IRC's functionality is presented. The approach was formally verified, and some of its performance metrics were identified. [ABSTRACT FROM AUTHOR]

As an essential attribute of information flow, opacity is used to characterise whether or not the intruders can infer the system secrets, which has caused considerable concern. For a complex system with multiple secret states, it is difficult to ensure the system opacity, which makes it necessary to actively control the system to enforce their opacity. In this regard, based on the framework of partially observable and controllable discrete event system, the active current-state opacity is studied. The system being actively opaque means that we can find a control for each secret state such that the behaviour of system reaching the secret state is opaque. Under such control, the intruder is unable to confirm the current state as secret state through limited observation. We construct an active-opacity verifier and derive a necessary and sufficient condition to check the active opacity of the system. [ABSTRACT FROM AUTHOR]

This article investigates the event‐triggered consensus problem of general linear multiagent systems with denial‐of‐service (DoS) attacks. A distributed event‐triggered mechanism is presented to alleviate the limited communication resources, the designed mechanism is fully distributed owing to the introduction of some adaptive parameters. Moreover, since the DoS attacks have been considered in the event‐triggered rule design, a switching strategy for the adaptive parameters is adopted. A switching Lyapunov function is used to strictly demonstrate that secure consensus can be reached effectively under the proposed control mechanism. The quantitative relationship between the frequency of the DoS attacks and the consensus is also presented. Moreover, the Zeno behaviour can be excluded for the studied close‐looped systems. Finally, a numerical simulation example is given to demonstrate the validity of the designed control law. [ABSTRACT FROM AUTHOR]

Petri net (PN) based prevention and control methods are widely studied in the literature to solve deadlock problems in flexible manufacturing systems (FMS). In PN models of FMS suffering from deadlocks, the reachability graph (RG) of the PN model can provide all reachable system states from the initial state, including all legal states, bad states, and deadlock states. A maximally permissive deadlock controller allows the system to reach all legal states exist within the live zone (LZ) that determines the optimal live behavior, while prohibits reaching bad and deadlock states exist within the dead zone. It is necessary to know the exact number of legal states that must be provided by a deadlock controller to determine the behavioral permissiveness of a control policy. Therefore, the number of legal states has been considered as a quality measure for deadlock prevention methods available in the literature. Unfortunately, to date for a given RG of a PN model of an FMS suffering from deadlocks, no study has been reported to provide the number of reachable legal states exist within the LZ of the given RG. In this paper, a method is proposed for the computation of the number of legal states that must be provided by an optimal deadlock prevention policy. The proposed method makes use of the reachability analysis of a given PN model of a deadlock-prone FMS together with the first strongly connected component (SCC) by using INA (Integrated Net Analyzer). The number of legal states computed from the first SCC that includes the initial marking represents the LZ of a RG. The proposed algorithm is implemented as an executable program. The number of legal states of a deadlock controller can easily and correctly be computed by using the proposed method and tool. Several well-known examples of FMS are considered to illustrate the applicability and the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this work, we introduce a modelling and simulation process based on Matlab/Simulink for Petri nets that considers inhibitor arcs and priorities of transitions, herein named eXtended Petri nets, for modelling deterministic complex systems. The presented process comprises the method for modelling the system, the functions for the import to Matlab of all the data structures that define a Petri net with inhibitory arcs and priorities in transitions, and the functions to perform the simulation of the Petri net behaviour as a Simulink block. We present an exploratory case study about Rate Monotonic Scheduling for tasks with harmonic periods to show the complete modelling and simulation process. First, the scheduling system is modelled with Renew editor and saved as a PNML file. Then, this file is read and transformed into a Matlab data type. The produced data structures are the inputs to the proposed Simulink block, which performs the dynamic of the eXtended Petri net. Finally, the outputs of the simulation help validate the logical and temporal correctness of the scheduler model used as a case study. The concluding remarks section provides a link for downloading and testing the simulation process. [ABSTRACT FROM AUTHOR]

A decentralized marking fault diagnosis method is proposed to solve the problem that increasing the number of unobservable transitions may result in the inability to diagnose faulty markings, in a class of decentralized systems modeled by labeled Petri nets. Assuming that each local site knows the structure of the labeled Petri net, and the subnet induced by unobservable transitions is acyclic. The decentralized architecture consists of a set of local sites communicating with a coordinator that determines whether the faults have occurred in the system, which are modeled by markings. First, each local site constructs the corresponding dual verifier and calculates the local marking fault diagnosis state according to the local observations. Then, it exchanges the corresponding information with the coordinator according to the two proposed diagnosis protocols. Finally, the coordinator calculates the global diagnosis state according to the received information. In addition, the marking diagnosability under both protocols is analyzed. A sufficient and necessary condition for marking fault diagnosis in the decentralized architecture under the second protocol is proved.