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In a multi-robot system, several robots coordinate to perform a joint task such as border patrol, etc. There are many different methods which can be employed to model a multi-robot system. Some of these techniques are based on the discrete events systems theory. In this work, we have proposed, a novel deliberative/reactive hybrid architecture based on the supervisory control theory (SCT). We have incorporated safety requirements as well as heuristics’ performance requirements into our system. We have validated our proposed architecture via several computer simulations. Simulation results confirm the effectiveness of the proposed architecture. [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.

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]

Model predictive control of residential air conditioning could reduce energy costs and greenhouse gas emissions while maintaining or improving occupants' thermal comfort. However, most approaches to predictive air conditioning control either do not model indoor humidity or treat it as constant. This simplification stems from challenges with modeling indoor humidity dynamics, particularly the high-order, nonlinear equations that govern heat and mass transfer between the air conditioner's evaporator coil and the indoor air. This paper develops a machine-learning approach to modeling indoor humidity dynamics that is suitable for real-world deployment at scale. This study then investigates the value of humidity modeling in four field tests of predictive control in an occupied house. The four field tests evaluate two different building models: One with constant humidity and one with time-varying humidity. Each modeling approach is tested in two different predictive controllers: One that focuses on reducing energy costs and one that focuses on constraining electric power below a utility-specified threshold. The two models lead to similar performance for reducing energy costs. Combining the results of this study and a prior heating study of the same house, the estimated year-round energy cost savings were $340–497 or 22%–31% (95% confidence intervals); these savings were consistent across both humidity models. However, in the demand response tests, the simplifying assumption of constant humidity led to far more frequent and severe violations of the power constraint. • Field implementation of model predictive control for residential air conditioning. • Development of scalable methods for data-driven modeling of humidity effects. • Comparison of practical impacts of humidity model fidelity. • Model fidelity matters less for energy efficiency than for limiting peak demand. • Estimated year-round savings of $340-497 or 22%–31% of heating and cooling energy cost. [ABSTRACT FROM AUTHOR]

In supervisory control, computational complexity and implementation flexibility represent major challenges when a large number of local components compose a target system. To overcome these difficulties, we propose a formal approach to distributed control synthesis and implementation for automated manufacturing systems (AMS). We assume that the system is modelled with automata in a local modular fashion. Local control specifications are defined for each local subsystem by means of logical equations to construct local controllers (LCs). Then, global control specifications, stated as logical implications, are applied to the LCs, which allows synchronisation and cooperative interaction among the subsystems. This paper makes two contributions. First, it outlines a formal method for constructing minimally restrictive and deadlock-free distributed controllers (DCs). Second, it proposes a method for the interpretation of these DCs into Grafcet, which is a graphical modelling formalism widely used to design the controller's dynamic behaviour for AMS. An experimental manufacturing system illustrates the approach. [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]

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.

To improve the accuracy of tracking the trunk center-of-mass (CoM) trajectory and foot-end trajectory in a fully electrically driven quadruped robot, an efficient and practical new trajectory tracking control method is designed. The proposed trajectory tracking method is mainly divided into trunk balance controller (TBC) and swing leg controller (SLC). In TBC, a quadruped robot dynamics model is developed to find the optimal foot-end force that follows the trunk CoM trajectory based on the model predictive control (MPC) principle. In SLC, the Bessel curve is planned as the desired trajectory at the foot-end, while the desired trajectory is tracked by a virtual spring-damping element driving the foot-end, meanwhile, the radial basis function neural network (RBFNN) is applied for supervisory control to improve the control performance for the system. The experimental results show that the control method can modify the robot's foot-end trajectory tracking effect, so that the stability error can be eliminated and the robustness of the controller can be improved, meanwhile, the linear and circular trajectory for CoM can be tracked accurately and quickly. [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]

This paper proposes an iterative control policy to design a less restrictive and admissible Petri net (PN) controller for a Discrete Event System (DES) with uncontrollable transitions, when a maximally permissive controller is not obtains by. This paper exploits a structural supervisory control method, avoiding reachability graph. This exciting method addresses the controllability condition of the desired functioning PN to define it as General Mutual Exclusion constraints (GMEC), which leads to design place invariant-based controller. The controller is non-admissible if one control place is connected to uncontrollable transition. To obtain an admissible PN controller, if such a controller exists, authors propose constraints transformation, which is computationally complex, while the controller arcs displacement approach is unsystematic. Based on it, we develop the idea to iterate the structural supervisory control method, to ensure that control place is connected to controllable transition. Through this, it was found that the displacement of arcs is systematic and the controller is less restrictive and admissible, namely the supreme controller. The results indicate that the linear constraints are never violated through the firing of uncontrollable transitions. The finding of this work may serve to evaluate the structural optimality of the controller, in order to perform it practically. [ABSTRACT FROM AUTHOR]

Pipeline systems in naval ships with special combat purposes (such as warships) are frequently exposed to the risk of damage. It is necessary to detect and isolate such unexpected damage quickly via an autonomous recovery system in the pipelines to ensure the ship's survivability. This recovery system employs an autonomously controlled valve, which consists of a valve body, two pressure sensors, an actuator to open/close the valve, and a controller equipped with damage detection capability and a control algorithm. To enhance the reliability of the autonomous recovery system, this study first proposes a multistage control algorithm that can comprehensively consider the various damage scenarios in a naval ship and then suggests the design procedure for the autonomous piping system based on support vector machine (SVM). To design the suggested algorithm, a simulation model for the 1-D real-time flow analysis is first established, and its results for possible damage scenarios are used as training data for the SVM with the valve operation information for recovery of the piping system and to minimize the damaged area. A performance comparison of the representative control algorithms for the autonomous piping system is conducted via flow simulations under three representative damage scenarios. Finally, a real-scale testbed of the firemain in a naval ship is designed and constructed, and the effectiveness of the algorithm is demonstrated through experiments. [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]

Supervisory control for discrete event systems that is widely studied in the literature to tackle the Petri net model’s deadlock occurrence of flexible manufacturing systems. A supervisory structure constructed using control places imposed restrictions on the concurrent operation in the systems, which degrade the supervisor’s efficiency. This study proposed a new method to construct a supervisory structure using combine control places and control transitions to ensure flexible manufacturing systems’ smooth operation. Place-Transition controller is designed for each concurrent process of the systems. Three algorithms are proposed in this method. The algorithm 1 computes the loop markings at each process of the Petri net model. Algorithm 2 is used to sort the deadlock markings based on the concurrent processes of the Petri net model. The Transition-Place controller and Place-Transition controller are designed using algorithm 3. Transition-Place controller creates extra states in the reachability graph, which is called dump markings. Place-Transition controller projects the deadlock markings to the loop markings via dump markings. The number of states created by the Transition-Place controller depends on the number of concurrent states in the Petri net model. The final supervisory structure controls the deadlock occurrence of flexible manufacturing systems with zero restrictions of systems operation. This proposed method is efficient as it retains all the states generated in the controlled Petri net model.

Many biomedical systems and some engineering systems can be modeled as fuzzy discrete event systems. In this article, fuzzy discrete event systems with constraints (FDESwC) are introduced and detectabilities of FDESwC are investigated. While detectabilities of conventional crisp discrete event systems (DES) have been investigated before, detectabilities of FDESwC are much more complex because the state space of FDESwC is infinite, unlike that of crisp DES. To overcome this difficulty, trajectories of FDESwC bounded by $N$ observations are considered and $N$ -detectabilities are introduced. $N$ -detectabilities of crisp DES are first defined and proved to be equivalent to detectabilities if $N$ is sufficiently large. Fuzzy $N$ -detectabilities of FDESwC are then defined. An algorithm is derived to check fuzzy $N$ -detectabilities. While fuzzy detectabilities require to determine which state the system is in, this requirement can be relaxed in some applications. To do so, fuzzy $D$ -detectabilities are introduced and investigated, where the requirement is to distinguish certain pairs of states. [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]

The human–agent collaboration (HAC) is a prospective research topic whose great applications and future scenarios have attracted vast attention. In a broad sense, the HAC system (HACS) can be broken down into six elements: “Man,” “Agents,” “Goal,” “Network,” “Environment,” and “Tasks.” By merging these elements and building a relation graph, this article proposes a systematic analysis framework for HACS, and attempts to make a comprehensive analysis of these elements and their relationships. We coin the abbreviation “MAGNET” to name the framework by stringing together the initials of the above six terms. The framework provides novel insights into analyzing various HAC patterns and integrates different types of HACSs in a unifying way. The presentation of the HACS framework is divided into two parts. This article, part I, presents the systematic analysis framework. Part II proposes a normalized two-stage top-level design procedure for designing an HACS from the perspective of MAGNET. [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]

This paper proposes algorithms for supervisor synthesis in discrete event system models with distinguishers. Distinguishers are special components responsible to select an enabled event from a group of related refined events. They are a helpful modelling tool, but their use increases the state space and makes supervisor synthesis more difficult. The paper shows how general algorithms for modular or compositional synthesis can be enhanced by considering the special properties of distinguishers. This gives rise to systematic algorithms that compute least restrictive controllable and nonblocking supervisors, while working with only a part of the distinguisher model. A worked case study of a bottling plant demonstrates the efficacy of the approach. [ABSTRACT FROM AUTHOR]

Cyber-physical systems have been highly integrated into many contemporary infrastructures. As this integration deepens, the importance of protecting these systems from unauthorized access and data corruption increases. Nowadays, cyber-physical systems are not well protected against network attacks. One solution is to improve the security of a system by encrypting the transmitted data. In this paper, we consider the encryption of supervisors of discrete event systems modeled with deterministic finite-state automata. We propose an encryption framework of supervisory control systems based on the matrix notation of automata. The purpose of using matrix notation is to make it suitable for homomorphic encryption schemes over integers, which are emerging in the cryptography area. We calculate the entropy of the matrix notation and find that as the size of a system increases, it gets smaller and approaches zero. Owing to the low entropy of the matrix notation, we propose an algorithm to enhance its entropy. By applying the entropy-enhancing process, the distribution characteristics of entries in matrices or vectors can be hidden to avoid a brute force attack. Correspondingly, we propose an entropy restoration algorithm to ensure that the control action can be transmitted correctly.

Automated manufacturing systems (AMSs) consist of computer-controlled interconnected manufacturing components (MCs) that are used to transport and process different product types. Each product type requires a certain sequence of processing steps in different MCs. Hereby, multiple product types can share processing steps on the same MC and the paths of different products types can overlap. In this paper we consider the modeling of AMSs in the scope of supervisory control for discrete event systems (DES). On the one hand, a suitable AMS model must allow the representation of sequential and concurrent processing steps in MCs. On the other hand, such model must be able to track different product types traveling through the AMS so as to process the products correctly. While previous work is commonly concerned with the first requirement, this paper identifies that the existing literature lacks a general treatment of the second requirement. Accordingly, we first introduce order-preserving (OP) languages that preserve the order of different product types in MCs and we propose a suitable finite state automaton model for OP languages. Then, we show that the composition of OP languages again leads to an OP language. That is, modeling MCs by OP languages, an OP model of a complete AMS that is suitable for supervisory control is obtained. In addition, it is possible to use both OP models and non-OP models for general AMSs, where MCs have different properties. We demonstrate the applicability of the proposed modeling technique by a flexible manufacturing system example.

Despite advances in automated manufacturing systems (AMSs), faults occur from time to time, which cannot be avoided in a complex real system. A fault is one of the primary causes of failures making some AMS operations unable to complete, and the diagnosis is one of the most important steps in fault-recovery and repair. This work develops a methodology for investigating the behavior of faults on the resources in discrete event systems that are failure-prone. We tackle the fault-tolerant problem and propose a method to make the system able to continue performing its duties, while the failed resources are under a repair and recovery process. In this work, a failure-safe model is proposed and, at the same time, a method for fault recovery and repair of a faulty element is presented without interrupting task processing due to the occurrence of fault to some elements. We use redundant elements to replace the target elements, and these redundant elements are used to do the same work as the target elements do, when faults occur to the target elements. A target element is an unreliable element that is prone to failure. After a faulty target element is repaired and recovered, its failure model is automatically replaced by its repaired model to indicate that the corresponding element has returned to work. The proposed method is tested using an application example. The results show, compared with those obtained by the studies in the literature, that the proposed method has a great performance and outperforms the existing studies.

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.

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.

Real-time control applications are highly parallelizable and can be used to effectively harness the capacity of a given computing platform when appropriately scheduled. Given a multicore platform for executing a set of parallelizable applications, it is necessary to ensure proper functioning of the system even in the presence of transient processor faults. However, most existing scheduling approaches for parallel applications have been heuristic schemes which are often based only on the satisfaction of a set of sufficiency conditions and cannot take into consideration of all necessary schedulability requirements. Consequently, such schemes lead to suboptimal usage of resources resulting in higher design costs. Formal model-based safe design methodologies such as supervisory control are often desirable in the design of correct-by-construction fault-tolerant schedulers. This article proposes a supervisory control-based fault-tolerant scheduler synthesis scheme for real-time tasks modeled as precedence-constrained task graphs, executing on multicores. Further, we devise search strategies to obtain schedules that 1) maximize fault-tolerance and 2) minimize peak-power (MPP) dissipation. Conducted experiments using real-world benchmarks reveal the efficacy of our scheme. [ABSTRACT FROM AUTHOR]

A basic notion of transparency in automated systems design is the need to support user tracking and understanding of system states. Many usability principles for complex systems design implicitly target the concept of transparency. In this study, we made comparison of a "baseline" control interface mimicking an existing available UAV ground control station with an "enhanced" interface designed with improved functional transparency and usability, and a "degraded" interface which removed important design features. Each participant was extensively trained in the use of one of the interfaces and all simulated UAV control tasks. Each participant was tested in four trials of a typical military concept of UAV operation with different mission maps and vehicle speeds. Results revealed participants using the enhanced interface to produce significantly faster task completion times and greater accuracy across all UAV control tasks. The enhanced features were also found to promote operator understanding of the system and mitigate workload. By defining and setting automation transparency as an overarching design objective and identifying specific transparency and usability issues within existing GCS designs, we weress able to design and prototype an enhanced interface that more effectively supported human-automation interaction. Automation transparency as a high-level design objective may be useful for expert designers; whereas, usability design guidelines, as "building blocks" to transparency, may be a useful tool for new system designers. [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]

This paper addresses a simple democratic progress model represented by a discrete‐event system (DES) and the influence of information accessibility on the progressiveness of the model. Each agent makes a decision according to its selfish criterion, and the final decision for the progress or regression of the system is done by the majority rule based on individual decisions of agents. We first present the conditions for the closed‐loop system to be progressive or regressive under full observation where every agent can observe all the states. Then we focus on the masked observation problem, namely, whether the progressiveness of the closed‐loop behavior can be changed by restricting the information accessibility of agents that are characterized by their masks. Especially, we show that due to the elite class, the regressive closed‐loop system can never be transformed into progressive regardless of mask design, which conforms to the feature of modern democracy. [ABSTRACT FROM AUTHOR]

This study aimed to replicate Muir and Moray that demonstrated operators' trust in automated machines developing from faith, then dependability, and lastly predictability. Following the procedure of Muir and Moray, we asked undergraduate participants to complete a training program in a simulated pasteuriser plant and an experimental program including various errors in the pasteuriser. Results showed that the best predictor of overall trust was not faith but dependability, and that dependability consistently governed trust throughout the interaction with the pasteuriser. Thus, the obtained data patterns were inconsistent with those reported in Muir and Moray. We observed that operators in the current study used automatic control more frequently than manual control to successfully produce performance scores contrary to the operators in Muir and Moray. The results imply that dependability is a critical predictor of human-machine trust, which automation designer may focus on. More extensive future research using more modern automated technologies is necessary for understanding what factors control human-autonomy trust in modern ages. Practitioner Summary: The results suggest that dependability is a key factor that shapes human-machine trust across the time course of the trust development. This replication study suggests a new perspective for designing effective human-machine systems for untrained users who do not go through extensive training programs on automated systems. [ABSTRACT FROM AUTHOR]

A set of non-negative integral vectors is said to be right-closed if the presence of a vector in the set implies all term-wise larger vectors also belong to the set. A set of markings is control invariant with respect to a Petri Net (PN) structure if the firing of any uncontrollable transition at any marking in this set results in a new marking that is also in the set. Every right-closed set of markings has a unique supremal control invariant subset, which is the largest subset that is control invariant with respect to the PN structure. This subset is not necessarily right-closed. In this paper, we present an algorithm that computes the supremal right-closed control invariant subset of a right-closed of markings with respect to an arbitrary PN structure. This set plays a critical role in the synthesis of Liveness Enforcing Supervisory Policies (LESPs) for a class of PN structures, and consequently, the proposed algorithm plays a key role in the synthesis of LESPs for this class of PN structures. [ABSTRACT FROM AUTHOR]

This work considers the optimal scheduling problem for a campus central plant equipped with a bank of multiple electrical chillers and a thermal energy storage (TES). Typically, the chillers are operated in ON/OFF modes to charge TES and supply chilled water to satisfy the campus cooling demands. A bilinear model is established to describe the system dynamics of the central plant. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity cost. At each time step, the MPC problem is represented as a large-scale mixed-integer nonlinear programming problem. We propose a heuristic algorithm to obtain suboptimal solutions for it via dynamic programming (DP) and mixed integer linear programming (MILP). The system dynamics is linearized along the simulated trajectories of the system. The optimal TES operation profile is obtained by solving a DP problem at every horizon, and the optimal chiller operations are obtained by solving an MILP problem at every time step with a fixed TES operation profile. Simulation results show desired performance and computational tractability of the proposed algorithm.